field report · 2026-10-26 · three reports, one page

Operations research in the field: LLMs, adoption and robots

Three reports, one question: why does mathematical optimisation reach so little of the operations it could improve, and what is changing that. The first looks at the supply side, where large language models meet solvers, and sorts the players into four buckets with China as its own scene. The second looks at the buyer side across eight language regions and finds projects stalling at data, daily use and ownership rather than at the solver. The third replays those stalls with a robot executing the plan instead of a person, and asks who can own the optimisation when the plan becomes imperative.

LLMs × optimisation2026-10-14

A landscape of where large language models meet mathematical optimisation: 22 players sorted into four buckets along the dimensions that actually separate them. It covers the lineage, the people, the bets and the evidence for each bucket, with China as its own scene and a timeline of the goals behind all four.

Main questions answered

What are the four ways an LLM can sit next to a solver, and where does correctness come from in each?
Which players keep exact optimisation, and which have real-world evidence?
How did each generation of tools try to meet the same three wishes?
Who is working in each bucket, what are they betting on, and why does China have its own solver scene?
Where is the white space that nobody fills?

OR adoption by region2026-10-16

A buyer-side field study of why optimisation projects stall, built from practitioner voices in eight language regions. It sorts the stalls by pipeline stage, by the layer the solver depends on, by vertical and by region, and records what was not found.

Main questions answered

At which stage of the pipeline do projects stall, and what does the planner fall back to?
What does a working optimisation depend on besides the solver?
Why is optimisation sold as consulting rather than as a product?
Which regions show readiness signals, and which do not?
What do the failed projects have in common?

Robots and the OR stall2026-10-16

A reasoning piece that replays the adoption report's three stalls with a robot instead of a person executing the plan. It asks what changes for data, trust and ownership, and who can own the optimisation layer once the plan becomes imperative.

Main questions answered

What is the one mechanism behind the three stalls, and why can a robot not be the adapter?
What happens to the data, trust and ownership stalls when a robot executes the plan?
Who owns the robot, the service, the data and the control, and so who can own the optimisation?
Do optimisation and formalisation stay as important when robots execute?
What does this do to an industrial-agents thesis?

How to read this page

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field report · 2026-10-14 · rewrite of the landscape note, from five sourced research lanes (A, B, C, D, China)

LLMs and optimisation, sorted by what defines them

1. Four ways AI can advance OR adoption

Bucket	Where the LLM sits	Where correctness comes from	One-line difference
A Front end for standard OR	Between a person and a solver. Writes or explains a formal model.	The solver solves whatever model was written. Whether it is the right model is checked by tests, an existing model, or nobody.	Same old models, easier to write and read.
B Algorithm discovery	Inside a search loop, writing code. Never talks to a user.	An evaluator that scores each program on test instances.	New algorithms, found by mutation plus a scorer.
C Better solvers	Mostly nowhere. Learning and GPUs work inside or instead of the solver.	Optimality gaps and certificates, where the method keeps them.	Same models, solved faster or better.
D Richer models of reality	Between the messy world (emails, planners, news) and the model.	Mostly a human accepting or rejecting. Nobody has an automatic check that the model got more faithful.	Different models, closer to what actually decides whether a plan gets used.

flowchart LR
  R[Messy reality
emails, contracts,
planner knowledge] -->|D. richer models| S[Problem statement]
  S -->|A. LLM formulation| M[Formal model
LP, MIP, VRP]
  M -->|C. better solving
GPU, learned parts| P[Plan]
  B[B. algorithm discovery
LLM plus evaluator] -.->|new heuristics| M
  P -->|A. explain, what-if| U[Decision maker]
  U -.->|D. feedback| S

2. The dimensions table

The table shows where each of the 22 players puts the LLM and what that does to correctness, evidence and persistence. Sorted by where the LLM sits, the split is plain: in A and B and in most Chinese work the LLM writes the core artefact, a model or an algorithm; in C it is absent, and in the D industry products it sits at the edge, recommending. Exactness kept, read next to real-world evidence, shows that almost every method which reached production with numbers keeps a classical engine, with AlphaEvolve at FM Logistic as the one exception that ships a heuristic with no gap, a named external customer and the word production. Direction and persistence together are almost empty: hardly anyone carries reality into the model and keeps the elicited rule versioned, and the two products that persist something do not test the edit against history. Base model shows the open-weight bases clustered at OptiMind and the ORLM line, while everything that leads the discovery results runs on Gemini or OpenAI models.

Rows are the dimensions that actually split the players. Columns are 22 representative players or approaches, grouped by bucket. Each row has its own meaning for green, amber and red, written in its definition (hover the row name, or read the list under the table). The colours are not "good" and "bad". They say how much of that property a player has.

Click a dimension's name to sort the columns by it (green first, then amber, red, plain, n/a, ?). Click again to go back to bucket order. Hover anything marked ✦ for a note. Every note is also written out below the table.

has the property strongly (meaning per row) partly does not descriptive, no ranking n/a ? could not find out ✦ note on hover, repeated below

Show: Colors only

Chinese columns carry two chips: China, and the bucket they belong to. The A to D filters include them.

3. The four buckets, and China

3A. LLMs as the front end for standard OR

Lineage

This is the oldest dream in OR wearing new clothes. For fifty years the bottleneck was not the solver but the translation from a business problem to a formal model. GAMS, AMPL and AIMMS all made the same pitch as today: let someone who knows the problem write the model without writing solver code. They worked for trained OR people and failed for everyone else, because algebraic notation still needs the modelling skill.

The LLM version went through three phases. In 2022 and 2023 the question was whether an LLM can write a model at all, on textbook word problems (NL4Opt). From 2024 to early 2026 it split into prompting agents (OptiMUS, Chain-of-Experts) and fine-tuned specialist models (ORLM, LLMOPT, OptiMind). From mid-2026 the vendors stopped trying to own the chat window: Gurobi, Nextmv, AMPL and NVIDIA all expose their tools through MCP servers or agent skills, so Claude, Copilot or any coding agent calls the solver. That concedes the front end to general agents and retreats to what a vendor can uniquely check.

Groups and people

Group	Main people and background	What the background predicts
Microsoft Research, ML and Optimization (Redmond)	Ishai Menache, Partner Research Manager, PhD Technion, MIT postdoc. Stable core: Mellou, Molinaro, Kulkarni, Sirui Li, Barbalho, Beibin Li, with Jeevan Pathuri from cloud supply chain operations. Business partner: David Simchi-Levi (MIT).	Their customer is Microsoft's own cloud hardware supply chain, which already has OR models and planners. So the first product was explanation and what-if over an existing model, not formulation from scratch.
Stanford, Madeleine Udell's group	Udell, Associate Professor of MS&E (tenured July 2026), from Cornell in 2022. OptiMUS led by PhD student Ali AhmadiTeshnizi.	An optimisation and numerics group, not an NLP group. Emphasis on solver-checked correctness, modularity for long problems, debugging loops, on whichever frontier model is best.
Zhejiang University with Huawei	Ziyang Xiao, Dongxiang Zhang, Gang Chen.	Agent architecture (Chain-of-Experts) and benchmark hygiene. Their 2025 survey found the benchmarks full of errors.
Gurobi	CEO Duke Perrucci; R&D VP Tobias Achterberg on AI Modeling; CTO since October 2025 Oliver Bastert, from FICO (VP Product Management across analytics, ML, generative AI and optimisation), earlier MIP algorithms at Dash Optimization.	A solver and product team. The LLM goes where errors can be caught (spec, unit tests, infeasibility diagnosis) and the solver stays the source of truth. Bastert: optimisation "should benefit from that same transformation" without "compromising the rigor".
NVIDIA cuOpt	Agent work published by cuOpt engineers and product marketing (Linsenmaier, Chung in 2024; Geva, Narasimha, Arunagiri in 2026). No single research lead named.	Reference architectures that show off GPUs and NIM models, not a product NVIDIA sells to planners.
Nextmv	Carolyn Mooney (CEO; simulation at Lockheed and Zoomer, then Grubhub) and Ryan O'Neil (CTO; decision engineering at Zoomer and Grubhub).	People who ran routing in production at a delivery company. They care about tests, rollouts and logs around a model, not about writing it from words.
Strangeworks / Quantagonia	Quantagonia founded 2021 by Dirk Zechiel, Philipp Hannemann, Sabina Jeschke and Sebastian Pokutta; Zechiel co-founded Gurobi GmbH and is now Strangeworks CTO. Strangeworks CEO is whurley.	Solver-orchestration heritage (run several solvers, pick the best) plus quantum positioning, sold through expert-led engagements rather than self-serve.

Cardinal Operations (ORLM) belongs here too and is covered under China. AMPL (board still includes Fourer, Gay and Kernighan) and AIMMS are shipping assistants without named people behind the AI work.

The bets

Player	Main bet, in one or two sentences	Where they say they are going
Gurobi	The LLM is a funnel and a safety net for the solver. Co-write a plain-language spec, generate formulation, gurobipy and unit tests, diagnose infeasibility, and let any coding agent reach this through MCP.	Lifecycle coverage: build, understand, troubleshoot.
Microsoft Research	A small, locally runnable specialist model on clean data beats prompting a frontier model, and the first business value is what-if over models you already have.	OptiMind next: reinforcement learning, self-generated expert hints, Excel input, product teams.
NVIDIA	Any agent framework, any open model, our GPU solver underneath. Skills framed as replacing "weeks of specialized OR work".	cuOpt as the tool agents call.
Nextmv	The LLM should operate decision apps, not invent them. "Deterministic control" over runs, tests and rollouts.	The operations layer, explicitly not chatbots for modelling.
AMPL	Ground general LLMs in the official language so they stop hallucinating syntax: skills and MCP for ChatGPT, Claude, Gemini.	No dates on the page.
AIMMS	The LLM is the interface for business users. "SENSAI doesn't do the math. It works with the math."	Agentic app and model building inside AIMMS.
Strangeworks Aura	Optimisation as an always-on managed system, from finding the opportunity to monitoring after go-live, across classical, quantum-inspired, HPC and quantum compute. The release does not mention LLMs.	Expert-led engagements.
Fine-tuning line (ORLM, LLMOPT, OptiMind)	The translator should be a fine-tuned open model trained on synthetic, solver-verified data.	RL with the solver as reward (SIRL, see China).

The real axis is what each player protects. Gurobi, AMPL, AIMMS and COPT protect a solver or a language, so their LLM must emit their syntax and they want to be the most reliable tool a general agent can call. NVIDIA protects hardware and is happy to commoditise the front end. Microsoft protects Azure and its own supply chain and publishes open specialist models. Nextmv protects the deployment layer. Strangeworks protects an engagement model and a compute broker. Nobody in this bucket bets that the LLM replaces the solver.

Evidence, strongest first

#	Who	What is documented	Why not higher
1	Microsoft (internal)	OptiGuide "has already been used in production" for cloud supply-chain planners' what-if questions; HBR and MIT describe LLM-based server deployment planning across 300+ data centres, a week of analysis down to minutes.	No external customer, no published cost or accuracy figure. "Week to minutes" is a description, not a measurement.
2	AIMMS SENSAI	Inside a product claiming 250+ enterprise customers; one unnamed testimonial (a US beauty retailer) using SENSAI Pro for scenarios.	No named LLM user. Logos on the page are not tied to SENSAI.
3	NVIDIA (internal)	Own operations team uses scenario analysis for nearly 3,000 products across a dozen factories (2024).	Unclear how much is the LLM agent versus cuOpt.
4	Gurobi	Shipping, documented, MCP server real.	No named customer, no usage numbers. Its own survey: 6% of users combine optimisation with LLMs.
5	Nextmv, AMPL, IBM	Shipped features.	No named users of the AI parts. IBM's Modeling Assistant documentation never mentions an LLM.
6	Strangeworks Aura, Cosmo Tech	Announcements.	No named customers or measured outcomes.
7	OptiMUS, Chain-of-Experts, ORLM, LLMOPT, OptiMind	Benchmarks.	OptiMind released as experimental.

The best documented case is a large company using LLMs over its own existing models with its own OR team in the loop. No source shows a non-expert business user getting a production model from natural language at a paying customer with a measured outcome. Verification, not generation, is the bottleneck: a wrong formulation runs fine and returns a confident optimum to the wrong problem.

3B. Algorithm discovery

Lineage

This is OR's own idea. The hyper-heuristics community wanted machines to "search over heuristics or heuristic components rather than directly over solutions" long before LLMs, usually through genetic programming. Its weakness was that a human had to define the building blocks for every new problem. The FunSearch paper says exactly that, and positions itself inside genetic programming and hyper-heuristics.

The hinge was OpenAI's 2022 paper showing that a code model makes program mutation far more effective. DeepMind's discovery team, who had shown with AlphaTensor that "provably correct algorithms" were a lab-scale goal, turned it into FunSearch (Nature, December 2023): search for programs, not solutions, use a fast cheap code model at around a million samples, and let an exact evaluator absorb the hallucinations. On online bin packing it got 0.03% excess bins over the lower bound against 3.79% for best fit. The evolutionary-computation community answered within months with cheaper open versions (EoH, ReEvo, which renamed the field "Language Hyper-Heuristics", LLaMEA). AlphaEvolve (May 2025) went from one function to whole files, and became a Google Cloud product in July 2026. Contests became the public scoreboard.

Groups and people

Group	Main people and background	What the background predicts
Google DeepMind, AI for Science	Pushmeet Kohli, VP Research, heads AI for Science and Strategic Initiatives, also titled Chief Scientist of Google Cloud; about a decade at Microsoft Research before. Matej Balog leads the algorithm discovery team (AlphaTensor, FunSearch). Alexander Novikov, AlphaEvolve first author, co-wrote the T3F tensor-train library.	Program synthesis, tensor maths and RL search, run by Cloud's chief scientist. So generality over any one OR domain, prestige maths results as proof, and commercialisation through Google Cloud. That is what happened.
CityU Hong Kong (EoH, LLM4AD) with Huawei	Qingfu Zhang, Chair Professor at CityU, earlier Essex, known for MOEA/D. Fei Liu first author. Huawei Noah's Ark co-authors Xialiang Tong and Mingxuan Yuan.	Evolutionary-computation people treat the LLM as one more variation operator: efficiency, breadth of benchmarks, an open platform.
AI4CO network (PKU, KAIST, SMU)	Haoran Ye, Federico Berto, Chuanbo Hua, Jinkyoo Park, Zhiguang Cao, Guojie Song.	The neural combinatorial optimisation community, pivoted to LLM heuristics. Plug LLM-written parts into existing templates, measure on routing benchmarks. Close to OR practice, far from customers.
Bielefeld and routing specialists (VRPAgent)	Hottung, Tierney and others with KAIST. Tierney also co-wrote the ORTEC EURO meets NeurIPS paper.	Keep the LLM in a small slot of a strong metaheuristic so feasibility is never at risk.
Leiden (LLaMEA)	Niki van Stein (Natural Computing Group, AutoML and Bayesian optimisation), Thomas Bäck.	Continuous black-box optimisation with rigorous benchmarking, closer to engineering design than logistics.
Sakana AI	Robert Tjarko Lange (TU Berlin PhD on evolutionary meta-learning, interned on DeepMind's Discovery team, maintains evosax), Yuki Imajuku, Edoardo Cetin; ALE-Bench with AtCoder.	An evolution-strategies researcher plus a contest partnership gives sample efficiency, open source, and contests as proof. Sakana's business is finance and Japanese government, not OR.
OpenAI	After the 2022 paper, no published discovery system. Visible only through AtCoder.	Heuristic design as an emergent skill of a general coding agent.

The bets

DeepMind / Google Cloud. One general engine: "bring a seed program and a deterministic evaluator", sold as a cloud service, the evaluator running on the customer's side. The scarce input is frontier models plus search infrastructure, and customers will write evaluators. Sakana. Do more with fewer calls: ShinkaEvolve's circle packing in about 150 samples, the AHC058 win at about 4,773 calls and $1,300. OpenAI. No OR scaffolding; a general reasoning agent won the 2026 AtCoder Heuristic final. CityU and AI4CO. Cheap, open, reproducible methods and a platform. VRPAgent and PyVRP+. Constrain the LLM to operators inside a state-of-the-art routing metaheuristic and measure against the best hand-written code (VRPAgent, about 0.3% better than SISRs at 1,000 to 2,000 customers, one CPU core).

The real axis is how much of the program the LLM may touch. AlphaEvolve rewrites whole files, VRPAgent writes two operators inside a fixed search, FunSearch and EoH evolve one priority function. The smaller the slot, the cheaper, safer and more OR-compatible. The larger, the more surprising the result and the more evaluator engineering it needs. A second axis is who pays for the search: Google sells compute, Sakana and the academics sell efficiency.

Evidence, strongest first

#	Who	What is documented	Caveat
1	Google internal	Borg scheduling heuristic in production for over a year, recovering on average 0.7% of worldwide compute; 23% kernel speedup giving 1% less Gemini training time; a Verilog change in an upcoming TPU.	Self-reported by the operator.
2	FM Logistic, one Polish warehouse	Seed program was their own grouping and routing logic; evaluator on 60 tours (over an hour of workforce data) with five penalty terms; 10.4% routing-efficiency gain on an "already highly optimized baseline", "now running in production". FM Logistic's own article confirms day-to-day use at one site.	One site, small evaluation set, the 15,000 km figure reads as a projection, co-written with the vendor. Still the only classical-OR deployment of an LLM method with a named customer.
3	Klarna, Coolblue (AlphaEvolve GA page)	Training pipeline throughput doubled; forecast WMAPE improved over 5%.	Real, but ML and forecasting, not optimisation.
4	Kinaxis, Infineon, BASF, ORNL	Early tests and experiments.	Pilots; Kinaxis numbers are on benchmark datasets.
5	Sakana, OpenAI	Sakana 1st of 804 at AHC058 (hosted by ALGO ARTIS, a planning-optimisation company); OpenAI 1st at the 2026 World Tour Finals Heuristic.	Puzzles with a published scorer.
6	EoH, ReEvo, LLaMEA, VRPAgent, PyVRP+	Benchmarks.	No deployment found.

The evaluator is the product. In business OR the evaluator is a simulator of the operation with every hard and soft constraint priced as a penalty. FM Logistic's is exactly that. Writing it is the same work as building a good model, which is bucket D's problem in disguise. Gains are big against hand-tuned rules (batching, slotting, dispatching rules), small against strong metaheuristics, and it fits where three things exist: a stable instance distribution, a fast simulator, and a seed algorithm. That describes the inside of planning products more than end customers.

On "DeepMind and ORTEC": no ORTEC work on LLM-generated heuristics and no ORTEC link to DeepMind or AlphaEvolve was found. ORTEC's documented ML-for-OR work is the EURO meets NeurIPS 2022 routing competition it initiated with real data (first author Wouter Kool). Google's OR-Tools lead Laurent Perron sits in Google Research, not DeepMind.

3C. Better solvers: ML and GPUs

Lineage

Two stories that started together and split. The first, from 2015, was "learn the solver": networks that output tours (Pointer Networks, the attention model, POMO), or that replace one hand-written rule inside branch and bound (Khalil, Gasse, DeepMind's Neural Diving). Thousands of papers. Then the reality checks. Amazon's 2021 Last Mile challenge was designed for ML and won by classical combinatorial optimisation. The ORTEC competition in 2022 said in its abstract that deep learning "fails to outperform OR baselines" on static routing, and every static winner was a hybrid genetic search. But on the dynamic variant every top approach used ML as a key part, and the overall winner (Kleopatra) let ML predict prizes while a classical engine did the routing. Learning wins where it predicts the uncertain part. The engine still does the combinatorics.

The second story is the one with production evidence, and it has no LLM in it. Google's OR team (Applegate, Lu, Lubin and others) built PDLP, a first-order LP method whose core operation is a matrix-vector product, in production in Google traffic engineering since May 2023. Haihao Lu's cuPDLP.jl matched Gurobi on standard LP sets on a GPU in late 2023. Gurobi's support page records that in 2023 its team concluded GPUs were "not a good fit". Within two years COPT, HiGHS, NVIDIA cuOpt, Gurobi and Xpress had all absorbed it. The counter-current already exists: Rothberg's "Backing PDHG into a Corner" on PDHG's lower accuracy and costly crossover, and Gurobi's own count that only 72 of 2,633 internal models ran faster with PDHG.

Groups and people

Group	Main people and background	What the background predicts
Montréal school, OR insiders	Andrea Lodi: Bologna, CPLEX R&D consultant 2006 to 2021, Canada Excellence Research Chair at Polytechnique Montréal, now at Cornell Tech. Co-wrote the manifesto, Gasse's branching paper, the CPLEX classifier and the critical VRP guidelines. Elias Khalil (Toronto) bridges both camps.	Someone who lived inside a commercial solver learns components that keep exactness, and demands fair benchmarks. That is his record. The one learned component that shipped in a commercial solver (CPLEX 12.10) came from him.
ML-first routing (Amsterdam, KAIST, InstaDeep)	Wouter Kool: OR MSc, ORTEC OR engineer, UvA PhD under Max Welling, wrote the reference attention model, then co-organised the competition that found deep learning does not beat OR on static routing. Jinkyoo Park (KAIST) leads AI4CO and founded Omelet (2023, Kakao Ventures). InstaDeep: Karim Beguir and Zohra Slim, bought by BioNTech in 2023 for about €500M for the remaining shares.	ML researchers optimise for speed on distributions of small instances and generality across variants, not the last percent on one large instance, which is what a dispatcher pays for. InstaDeep's COMPASS abstract concedes RL heuristics "have not yet replaced industrial solvers".
Google OR team	David Applegate; Haihao Lu (MIT OR PhD, Google, now MIT Sloan), who "co-developed the PDLP algorithm, which many industry solvers and tech companies have adopted"; Miles Lubin (co-created JuMP).	Classical LP solvers (simplex, barrier) spend their time on sparse matrix factorisation: hard to spread over thousands of GPU cores and hungry for memory (the page's own example: barrier hit 1 TB RAM on the biggest LPs). PDLP instead repeats one cheap operation, a matrix-vector product (multiply the constraint matrix by a vector), which is the operation GPUs are built for and needs only the matrix itself in memory. That operation is the "simple kernel", a kernel being the small inner routine that does nearly all the work. People who know both OR and hardware choose the algorithm that fits the machine, even though it converges less precisely, instead of porting the old algorithm. This is the background-predicts-bet reading of the Google team, not something they state.
NVIDIA cuOpt	Product team since 2021; Li & Lim records in 2023; Apache 2.0 now, MIP in beta ("proving feasible solutions optimal remains under active development").	A hardware vendor needs every solver to run on its chips, so it builds a reference implementation and gives it away.
Incumbent solver vendors	Gurobi (Rothberg and the GPU team), FICO, HiGHS, COPT.	Robustness on every customer model, so GPU becomes one more algorithm in the portfolio. "There is no silver bullet."

The bets, ordered by how much classical OR they keep

School	What it keeps fixed	Bet	Evidence
End-to-end neural CO	Only the problem definition	A learned policy replaces hand heuristics; now "one foundation model for all routing variants" (RouteFinder, 48 variants).	Benchmarks; HGS-PyVRP stays ahead on quality, RouteFinder 1 to 3% above it but seconds instead of minutes.
Learned branching	The exact solver and its certificate	Learn the expensive decisions inside the solver.	Benchmarks; its 2019 tooling (Ecole) is "looking for a new home". One shipped classifier in CPLEX 12.10.
ML plus CO layer	The classical engine	Learn the inputs of a combinatorial layer and train through it.	Won the dynamic ORTEC competition on real data.
GPU first-order LP	The LP itself	Change the algorithm to fit memory and parallel hardware.	Google production since 2023; in every major solver; giant LPs (up to 6.3 billion nonzeros) where barrier hit 1 TB RAM.

Ordered by how much classical OR each keeps, the order matches the order of real-world evidence. Language models are almost absent. They appear in 2026 in three thin places: as the agent calling a GPU solver (NVIDIA's AI Planner, internal, with what-if planning cut from about a day to under 10 minutes and a 2026 Edelman finalist), as code generators writing solver internals (LLM4Branch at ICML 2026, from eight training examples; LLM-guided cut management in SCIP at IJCAI 2026), and as generators of training instances (MILP-Evolve). The second is bucket B aimed at C's components.

Every learned method that reached production kept a classical engine and its certificate. "A network that outputs the plan" was tested on real data twice and lost both times, and an LLM that emits a schedule directly is the same bet with a bigger network. The speed gains came from algorithm-hardware fit, which NVIDIA owns. A model lab has little to add on GPU kernels. The LLM4Branch pattern is the interesting exception: an LLM can tune or write a solver component for one customer's instance distribution from a handful of examples, where a GNN needed thousands of strong-branching labels.

3D. Richer models of reality

Lineage

Buckets A to C make the model easier to write or faster to solve. D asks whether the model is about the right world. OR has known for fifty years that often it is not, and that this, not solver speed, is why plans get overridden. Little (1970) wanted the manager's judgment as an input. Ackoff (1979) said OR had stopped being useful. The next decades built machinery that could only take judgment in formal form: constraint acquisition (learn the constraints by asking the user to classify examples), inverse optimisation (infer the objective from what planners did), stochastic and approximate dynamic programming (Powell's dispatch models). In 2020 Kesavan and Kushwaha measured the override problem in the field: overrides lost money on mature products and won on growth products. Both sides are partly right, and neither has a channel to give the other what it lacks.

2023 was the first time judgment in its native form, sentences, could be parsed. In twelve months: OptiGuide, Holy Grail 2.0 (Tsouros, Guns and others reviving "the user states the problem, the computer solves it"), OptiChat and MeetMate. Then the planning vendors arrived with agents: Blue Yonder, o9, Kinaxis, Pigment, Optimal Dynamics, SAP.

Groups and people

Group	Main people and background	What the background predicts
Microsoft Research	Menache's group (OptiGuide); MeetMate from a separate group (Lawless, now Cornell; Suh, Rowan, Sarrafzadeh).	Operator-scientists with their own supply chain as testbed. Conservative and credible: solver as truth, LLM writes the query and the explanation.
Purdue, Can Li (OptiChat)	Chemical engineering; Tsinghua, CMU PhD in process systems optimisation, postdoc at Polytechnique Montréal.	Process systems engineering, where experts build large models and hand them to plant engineers who cannot read them. Aimed at that handover.
MIT, Chuchu Fan (LLMFP)	AeroAstro and LIDS; formal methods and verification for autonomous systems. Led by Yilun Hao.	Formalise, then let a solver guarantee. Robotics-style planning, not enterprise data.
KU Leuven, Tias Guns and Dimos Tsouros	From constraint acquisition (QuAcq2) to LLM modelling.	The only group whose pre-LLM agenda was literally getting the model out of the user's head.
IBM Haifa and Benelux OR (DOCP manifesto)	Wasserkrug, Boussioux, den Hertog, Mirzazadeh, Birbil, Kurtz, Maragno.	Practitioners who watched projects fail at "understand the business problem", which the manifesto names first.
Cambridge, Alexandra Brintrup	Seven-agent system reading news and mapping disruptions to supplier tiers.	Recommends; does not change or re-solve a model.
Planning-suite founders	o9 (Sanjiv Sidhu, i2 co-founder; Chakri Gottemukkala) and Blue Yonder (i2 to JDA to Blue Yonder, Panasonic) are two branches of the same 1988 family tree. Aera: Fred Laluyaux, ex-Anaplan CEO. Optimal Dynamics: Warren Powell's lineage.	They own the system of record and the planners' screens, so they build "agent that explains the plan" and "agent that fixes an exception".

The bets

Research: the LLM is the interface, the solver is the truth. The research systems split by direction. OptiGuide and OptiChat translate outward (model to human). MeetMate and LLMFP translate inward (human to model). Only inward is bucket D proper. MeetMate has the most telling number: GPT-4 turned stated preferences into constraint code with about 95% precision and 94% recall, but the check for whether it had enough information was only about 80% accurate. Knowing when you do not know enough about reality is the bucket's core problem in miniature.

Incumbents: the agent lives where the plan lives. Blue Yonder bets on a knowledge graph as the editable model of reality. o9 on learning from plan versus execution ("post-game analysis"). Kinaxis on agents that "plan with you, not for you", built by customers in Agent Studio on GPT and Gemini. SAP on owning the transaction system, so the agent can act. Aera on the decision log ("decision memory"), which of the incumbents is closest to bucket D's thesis. Palantir on the Ontology as a live model of operations. Pigment (Paris, planning rather than OR) on an agent that edits the planning model with auditable change history.

New agent companies: optimisation core, agents at the messy edge. Optimal Dynamics states the thesis better than anyone: "optimization software tends to be brittle with respect to input data quality", and LLM agents alone cannot see how one decision ripples through a network. Its Scale product bids on loads across load boards, email and EDI. UiPath bought Peak, a decision layer. Airlines show the non-LLM version: Lufthansa Systems' aiOCC at Eurowings (reinforcement learning, controller decides, feedback evaluated) and SITA's purchase of Big Blue Analytics (OCCam, "up to 30%" lower disruption costs, LLMs planned on top).

Evidence, strongest first

The question was who runs agents that change the optimisation model in production, with named customers and measured outcomes. Strictly, there is no documented case.

#	Who	What is documented	Caveat
1	Palantir at General Mills	Recommender that "consumes constraints, capacity, and network cost" in real time; over 70% accepted; about $14M a year; named customer executive.	Only part of the network. The document does not say an LLM is involved or that the model changes.
2	JD.com generative planning agents	Paper with Zuo-Jun Max Shen; about 22% better planning accuracy, about 2% higher in-stock in deployment.	Baselines and scope not in the abstract.
3	SITA OCCam, Lufthansa aiOCC	Production; aiOCC named at Eurowings.	Not LLMs. OCCam names no airline; aiOCC gives no numbers.
4	Microsoft OptiGuide	First-party production.	Outward only, no numbers beyond "days to minutes".
5	Kinaxis, o9, Blue Yonder, SAP, Aera	Named platform customers, productivity anecdotes (Kinaxis "up to 10x" at an unnamed pharma).	No agent-specific outcome on a planning KPI. SAP agents GA listed for Q4 2026.
6	Research	LLMFP 85% vs 39% on nine tasks; MeetMate n=10 user study; Brintrup on 30 synthetic scenarios.	Small and clean.

Nobody publishes the closed loop: the planner says something, the agent turns it into a candidate constraint or objective change, tests it against history, and the model keeps it with a scope and an expiry. Aera's decision memory, Kinaxis' guardrails and aiOCC's feedback evaluation each hold one piece. The hard parts are telling knowledge from habit (the Kesavan result means an LLM that faithfully encodes every planner sentence encodes the bad overrides too), knowing what you do not know (MeetMate's 80%), governance (a rule from Tuesday's email must expire and be auditable), and evaluation, since no benchmark measures "the model became more faithful to reality".

China

Why China has its own solver scene

The domestic solvers came first, from 2017 to 2021: LEAVES (SUFE), CMIP (CAS), COPT (Cardinal Operations), MindOpt (Alibaba DAMO), OptVerse (Huawei, Huawei Cloud only). They were driven by Stanford-trained academics who started companies, by platform companies wanting the capability in-house, and by state utilities. China Southern Power Grid built its own solver, Tianquan, ran it in parallel with imported solvers from 2022, and now clears the southern regional market across five provinces with it. A 2026 review reports COPT and OptVerse first and second on Mittelmann's LP benchmark and COPT, OptVerse and TAYLOR the top three on MIPLIB2017, with the caveat that Gurobi left those benchmarks in August 2024 and MindOpt left in December 2024. In 2026 Gurobi ended free academic licences in China, Hong Kong and Macau without giving a reason. No central policy document naming solvers as a self-reliance target was found.

People

Group	People and background
Cardinal Operations (杉数科技), Beijing, 2016	Four Stanford PhDs. Luo Xiaoqu, CEO (Stanford GSB PhD, ex-BCG). Dongdong Ge, CSO (Stanford OR PhD, professor at SUFE, now Antai College at SJTU; leads COPT; consulted for Boeing, IBM, JD, SF-Express, Huawei, Didi). Zizhuo Wang, CTO (Stanford OR PhD, ex-Minnesota, now CUHK-Shenzhen). Chief scientific adviser Yinyu Ye (Dantzig's student, interior-point pioneer, 2009 von Neumann Prize). Investors include a national manufacturing fund, Ansteel and China Nonferrous Metals (2023 round of nearly RMB 500M).
Alibaba DAMO Decision Intelligence Lab	MindOpt and MindOpt Copilot; Wotao Yin co-authors both the solver review and OptLLM.
Huawei (Noah's Ark, Cloud, OptVerse)	26-author ML-inside-OptVerse paper (Xijun Li, Jianye Hao, Junchi Yan, Defeng Sun and others). Mingxuan Yuan and Xialiang Tong connect Huawei to both main academic lines (EoH; the Zhejiang modelling survey).
CityU Hong Kong	Qingfu Zhang (PhD Xidian, MOEA/D at Essex), Fei Liu. LLM4AD platform.
Peking University	Zaiwen Wen (SJTU, Columbia PhD, UCLA postdoc; head of Industrial Engineering at PKU). MIPLIB-NL with Great Bay University and Huawei.

The core people are mathematical programmers trained in the US OR tradition (Stanford MS&E, Columbia, the Dantzig and Ye lineage), often holding a professorship and a company role at once. That predicts exactness and solver verification at the centre, benchmarks and products tightly coupled (IndustryOR is a Cardinal artefact, MIPLIB-NL has Huawei co-authors), and growth through owning the stack.

Bets

Cardinal owns the full decision stack: domestic compute, COPT, an LLM modelling layer, sold as "decision AI". Its Qingtian platform (2026-07-22) turns language into models, then solves, simulates, backtests and does "knowledge retention", with an operator library for domestic chips. Wang: the key "is not just having more powerful AI models, but the ability to truly embed AI into key business systems". Its research arm fine-tunes open models: ORLM (in Operations Research, bases Mistral-7B, DeepSeek-Math-7B, LLaMA-3-8B) and SIRL (NeurIPS 2025, RL with the solver as reward on Qwen2.5, with COPT and Gurobi variants). Alibaba bet in 2023 on natural-language optimisation as a cloud service on Qwen (MindOpt Copilot, trial since June 2023), and nothing public on it followed after 2024. Huawei puts ML inside OptVerse (GCNs for the initial basis, RL for presolve and cut selection) and uses LLM-designed heuristics for the hardest routing. PKU bets on data and honest evaluation. Meituan, Cainiao, JD run classical OR plus ML at huge scale, with no documented LLM in dispatch or routing.

Evidence

Tier	Case	What it shows
Peer-reviewed, no LLM	Meituan dispatch (Edelman finalist 2023)	Over 5 million couriers, 60 million orders a day; delivery time down 20.96%, distance per order down 23.77%, about USD 0.23 billion a year.
Peer-reviewed, no LLM	Alibaba / Cainiao routing	ALNS plus deep learning, over USD 50M a year.
Edelman finalist, no LLM	Huawei Cloud live-streaming scheduling (Edelman finalist 2023)	Network utilisation up more than 30%; Douyu and Huya named.
State media, no LLM	CSG Tianquan	Primary market-clearing solver for five provinces.
Vendor, no LLM	OptVerse at Tianjin Port	"Up to 144x" faster planning.
LLM, claims only	Cardinal ORLM, Qingtian; Alibaba MindOpt Copilot	Named users are universities. No industry numbers.
LLM, competition	Huawei-CityU, CVRPLib BKS challenge	Best-known solution on 51 of 100 instances (1,000 to 10,000 customers), with EoH-evolved operators inside AILS-II. The LLM was Gemini-2.5-pro, used offline, about 200 samples at one hour each per operator.

The contrast with the West, and what it means for a European lab

Western bucket-A work mostly prompts frontier closed models and calls Gurobi. Chinese bucket-A work mostly fine-tunes open 7B to 32B models and grounds them in a solver the same group owns. Qwen2.5 is now the default base. Then Chinese groups took their own leaderboard apart. The Zhejiang/Huawei survey found error rates of at least 26.4% in NL4Opt, 23.7% in MAMO-ComplexLP and 54% in IndustryOR, Cardinal's own benchmark (Cardinal has since issued corrected sets). PKU's MIPLIB-NL showed fine-tuned systems mostly below 6% Pass@1 on real models, while frontier general models did several times better.

China is ahead on owning the stack, not on LLM models of reality. Nobody there, or anywhere, has shown an LLM taking messy enterprise context into a validated industrial model in production. Small fine-tuned translators hit a ceiling on industrial models, which is good news for a frontier lab. The best Chinese discovery result used a US frontier model, so China's edge in B is people and problems, not models.

For a European lab: Europe has no domestic commercial solver at COPT or OptVerse scale, so it would rely on Gurobi or FICO (US), HiGHS or SCIP (open), or a partner. The Chinese experience says owning or deeply partnering a solver matters, because the solver is both the product engine and the RL reward. The sovereignty argument that built Tianquan (an engine you control at the centre of critical infrastructure) is available in Europe for grids, rail and defence, but only paired with an exact solver. The people to read first: Ge, Wang, Ye, Wen, Zhang and Liu, Yuan and Tong.

4. Across the buckets

Who has really tested any of this in business

Ranked across all buckets, separating "LLM involved" from "OR or ML without an LLM", because the strongest evidence is mostly the second kind.

#	Case	Bucket	LLM?	Strength
1	Meituan dispatch; Alibaba/Cainiao routing	ChinaC	No	Peer-reviewed, Edelman-grade numbers, years in production.
2	Google: PDLP in traffic engineering; AlphaEvolve's Borg heuristic (0.7% of compute)	CB	AlphaEvolve yes	Production, self-reported by the operator.
3	FM Logistic with AlphaEvolve	B	Yes	The only named external classical-OR deployment of an LLM method with a number (10.4%). One site.
4	Palantir at General Mills	D	Not stated	Named customer, $14M a year, partial network.
5	cuOpt at AT&T, clicOH	C	No	Named, vendor-reported numbers, no baseline for clicOH.
6	Microsoft OptiGuide; NVIDIA AI Planner	AC	Yes	Internal production. AI Planner an Edelman finalist; day to under 10 minutes.
7	JD.com generative planning agents	DChina	Yes	Paper claims deployment, about 22% accuracy gain; baselines not in abstract.
8	Gurobi, AIMMS, Nextmv, AMPL, Kinaxis, o9, Blue Yonder, Aera, Pigment	AD	Yes	Shipped; customers for platforms, not for the LLM part.
9	Everything else	all	mostly	Benchmarks and contests.

Pattern across all buckets: the OR is in production, the LLM layer is in pilots, platforms and press. Where an LLM is in production, it is either at a company using it on its own models with its own OR team (Microsoft, NVIDIA, Google), or it writes a heuristic against an evaluator someone built from real operations (FM Logistic).

The benchmark reality

Benchmark	What it shows
NL4Opt (2022)	1,101 LP word problems. Best competition formulation accuracy 0.899; untuned gpt-3.5-turbo 0.927 on the combined task. Textbook level, near saturated.
ORLM on IndustryOR (2024)	Best ORLM 38.0% vs GPT-4 prompting 28.0%. SIRL-Qwen2.5-32B later 61.9% on the cleaned set, 87.4% on MAMO-Complex.
The cleanup (2025 to 2026)	Xiao et al.: errors in at least 26.4% of NL4Opt, 23.7% of MAMO-ComplexLP, 54% of IndustryOR. OptiMind team: 30 to 50% of public test data flawed.
MIPLIB-NL (2026)	223 real MIPLIB 2017 models in language. Average Pass@1 17.85%. GPT-5.1 39.05%, Gemini-3-Pro 36.19%, DeepSeek-V3.2 23.81%. Fine-tuned systems mostly below 6%. "The main bottleneck is verification rather than generation."
FrontierOR (2026)	180 tasks from OR papers. The best one-shot model beats Gurobi on quality and speed in 31%.
ORAgentBench (2026)	Best agent passes 35.51% overall, 20.59% of hard tasks.
Gurobi user survey (2025)	6% already combine optimisation with LLMs, 24% experiment, 30% interested; 51% see model generation or code assistance as a use case.

Formulation is far from solved on realistic sets, and the old leaderboards were partly measuring their own errors.

People movements

Talent flows from solver companies and OR groups toward model and hardware companies, and between incumbents. Oliver Bastert went from FICO to Gurobi CTO (2025). Dirk Zechiel went from co-founding Gurobi GmbH to Quantagonia to Strangeworks CTO. Pushmeet Kohli spent a decade at Microsoft Research before DeepMind. Haihao Lu went from Google's OR team to MIT Sloan, and his algorithm went into every solver. Andrea Lodi went from CPLEX consulting to Montréal to Cornell Tech. Robert Lange interned on DeepMind's Discovery team before ShinkaEvolve at Sakana. Lawless did MeetMate at Microsoft and is now at Cornell. Cardinal's Wang Xi came from Google. The Chinese founders went Stanford to Shanghai, Beijing and Shenzhen.

5. Where the white space is

A is crowded and commoditising. Every solver vendor has a modeller assistant and an MCP server, and general coding agents already write gurobipy. A pure words-to-model product has no moat. What is left is the checker: spec plus acceptance tests, infeasibility diagnosis, "why did it do that", and the corpus of real industrial formulations with known-correct answers. Solver companies sit on that, which is a reason a lab would want people from them.

B is where the labs are, and still mostly scored on puzzles. The loop itself is free (OpenEvolve, ShinkaEvolve). The open gap is building evaluators from a company's own operations quickly, and earning trust in the result. Evolve the heuristic for this warehouse, this fleet, this week. That is the "engines start from a blank page" claim in a new form.

C is a hardware story and NVIDIA is winning it. A lab adds little except as a good caller of solvers, and maybe as the writer of solver components tuned to one customer's instances.

D is the emptiest and the most valuable. Nobody publishes the closed loop: read the contracts and the planner's notes, propose a typed, testable model edit (constraint, parameter, objective weight, with scope and expiry), abstain when information is missing, replay history to decide if it is knowledge or habit, and keep it versioned. That needs an exact solver so correctness never depends on the LLM, a design partner with years of plan, override and outcome logs, and a model trained to produce edits rather than whole models. European on-premise deployment matters because the inputs are exactly the sensitive documents that OptiGuide was designed never to send to an LLM.

6. Sources

All pages were opened by the research lanes or for this page. Claims that only appeared in search snippets, or that a lane could not confirm, were left off.

B. Algorithm discovery

C. Better solvers

Pointer Networks · Bello et al. · Dai et al. · Khalil et al.
Kool et al. attention model · POMO · Bengio, Lodi, Prouvost · Gasse et al. · Nair et al.
CPLEX classifier (Bonami, Lodi, Zarpellon)
Amazon Last Mile winner · Accorsi, Lodi, Vigo · ML4CO
EURO meets NeurIPS 2022 · Kleopatra, Transportation Science
PDLP · Google PDLP blog · PDLP revision
cuPDLP.jl · cuPDLPx · Backing PDHG into a Corner
Gurobi GPU support page · Gurobi GPU blog
cuOpt LP · cuOpt open source · cuOpt repo · cuOpt records, AT&T · clicOH · NVIDIA AI Planner
RouteFinder · RL4CO · Ecole
Lodi · Khalil · Kool profile · Haihao Lu · Omelet
InstaDeep · BioNTech acquisition · Poppy · COMPASS · Jumanji · DB Netz CTMS
LLM4Branch · LLM cuts in SCIP · MILP-Evolve

D. Richer models of reality

History, one timeline

The goals are old. Each generation found a new tool for the same three wishes: let non-experts write models (A), let machines design heuristics (B), and get the manager's judgment into the model (D). The new thing since 2022 is a mutation operator and a parser that both understand language and code.

1960s to 2014: the old wishes
1960s, 2000	B	Idea of machines designing heuristics; the term "hyper-heuristics" from 2000 (2013 JORS survey by Burke, Gendreau, Hyde, Kendall, Ochoa, Özcan, Qu).
1969 to 1970	D	John Little's "Models and Managers": a decision calculus processes "data and judgments". Judgment was meant to be an input.
1970s, 1987	A	GAMS starts in a World Bank economic modelling group; commercial product in November 1987.
1979	D	Ackoff, "The Future of Operational Research is Past". The charge is usefulness, not mathematics.
1985	A	AMPL designed at Bell Labs by Fourer, Gay and Kernighan "to let people express optimization models in familiar algebraic form".
1990	D	Warren Powell founds CASTLE Lab at Princeton; dispatch models used at Yellow Freight, Schneider, Norfolk Southern.
2008	A	Gurobi founded by Gu, Rothberg and Bixby.
2015 to 2021: learning the solver, and the first reality checks
2015 to 2016	C	Pointer Networks output TSP tours; Khalil et al. learn to imitate strong branching in a MIP solver. Replace the solver versus improve one decision inside it, from day one.
2017 to 2020	China	LEAVES (SUFE, 2017), CMIP (CAS, 2018), COPT 1.0 (Cardinal, 2019-05-28), MindOpt 0.9 (Alibaba, 2020-08).
2018	C	Kool, van Hoof, Welling's attention model for routing; Bengio, Lodi, Prouvost's "tour d'horizon" sets the ML-for-solver programme; Google starts the work that becomes PDLP.
2019	C	Gasse et al. put a graph neural network on the MIP and beat expert branching rules in a solver.
2020	D	Kesavan and Kushwaha's field experiment: merchant overrides of a data tool lose money on mature products and win on growth products.
2021	CChina	Amazon Last Mile challenge won by classical combinatorial optimisation (Cook, Held, Helsgaun), 42% ahead of second. PDLP at NeurIPS. Huawei announces OptVerse.
2022 to 2023: the language turn
2022	BAC	OpenAI's "Evolution through Large Models" (June): a code model as the mutation operator. AlphaTensor (October). NL4Opt competition at NeurIPS. EURO meets NeurIPS on real ORTEC data: deep learning "fails to outperform OR baselines" on static routing.
2023	ABCD	PDLP in Google traffic engineering (May). OptiGuide (July). Holy Grail 2.0, OptiChat and MindOpt Copilot (August). OptiMUS (October). cuPDLP.jl matches Gurobi on standard LP sets (November). FunSearch in Nature and MeetMate (December).
2024 to 2025: split into prompting versus fine-tuning, academics versus clouds
2024	BACD	EoH (January), ReEvo (February), LLaMEA (May). DOCP manifesto (February). ORLM fine-tunes 7B models (May). NVIDIA's cuOpt agent uses Codestral-22B (July). COPT and HiGHS add PDLP. Gurobi leaves the Mittelmann benchmarks (August).
2025	ABCD	HBR piece on Microsoft's LLM supply planning (January). Gurobi AI Modeling (February). cuOpt goes open source (March). AlphaEvolve (May) and Blue Yonder agents. OpenAI 2nd at AtCoder World Tour Finals Heuristic (July). Strangeworks buys Quantagonia (August). Benchmark error survey (August). Gurobi survey: 6% use LLMs with optimisation (September). Gurobi GPU alpha (September). AlphaEvolve Cloud preview and Sakana's AHC058 win (December).
2026: the reckoning and the MCP turn
Jan to Mar	AChinaBD	OptiMind; 30 to 50% of benchmark test data flawed (January). Huawei-CityU wins the CVRPLib BKS challenge (January to February). MIPLIB-NL (February). FM Logistic AlphaEvolve case and Pigment Modeler Agent (March). Nextmv MCP server (preview March, GA April).
Apr to Jul	ABCChina	cuOpt agent skills (May). FrontierOR (May), ORAgentBench (June). SITA buys Big Blue Analytics (June). Gurobi Intelligence Hub with MCP; AlphaEvolve GA on Google Cloud; OpenAI wins the AtCoder Heuristic final; Strangeworks Aura; Cardinal's Qingtian platform (all July).
Aug to Oct	ChinaD	Gurobi ends free academic licences in China, Hong Kong and Macau (help page updated 2026-08-31). SAP announces planning agents for Q4 GA (October).

field report · 2026-10-16 · eight regional research lanes (German, Dutch, French, Spanish, Slavic, Arabic and Gulf, Chinese, English baseline)

OR adoption: where it stalls and where it is ready

The buyer and user side of classical optimisation: what planners, plant managers, IT, associations and honest practitioners say about why scheduling, routing, rostering and planning models get bought, shelved, overridden or kept alive, region by region. Companion to the technology-side page LLMs and optimisation, by dimension.

1. Bottlenecks by stage

Seven stages, each with the strongest voices across regions. Original language first, then English, then the region tag and the source. The dotted lines in the diagram are where the regional files put the three recurring stalls.

1.1 Problem awareness: the buyer cannot name the problem

The buyer knows something is wrong but cannot say what it is in terms a model can use: the objective, the constraints, what a better result would look like. Planning runs on an experienced person's judgement, so there is no named problem to buy a solution for. And the buyer wants the problem gone, not a method, so talk about technique lands nowhere.

They know the status quo has problems and want improvement, but precisely describing the essence of the problem, the desired effect and the constraints is usually beyond them.
big-data research institute, China

The primary danger is that the client does not know what production planning actually is in the context of his firm.
planning consultant, Czechia

Management is not interested in the technique to be used, but in whether the problem can be solved in a way that satisfies them.
former head of the national OR society, Netherlands

Read the 6 quotes

1.2 Will and budget: know-how and time are named before money

Before anything is bought, the missing ingredient is rarely money. Firms lack the people who could run such a project and the time to take it on, and the firms that need it most have margins too thin to pay it back. Those that did pay often saw turnover rise and profit stay flat, which teaches the market that the spend does not pay.

lack of qualified staff (79 %) and lack of time (75 %) … is explained more by low interest than by barriers
survey of about 500 manufacturers, Québec

The firms that most need to transform are the least able to … takes 20 years to pay back … most chose another path: squeeze efficiency out of management
management study of 200 factories, China

turnover rises by tens of percent but profit does not rise at all or even falls.
APS maker, Czechia

Read the 5 quotes

1.3 Data: the first stall, in every language

The data a planning model needs does not exist in usable form. The real rules live in planners' heads, the recorded master data is missing or stale, and the slack that actually makes a plan work is never written down. Capturing and cleaning it becomes most of the project, and many projects stop there.

Ask a veteran material planner 'what is the normal lead time for this part?' and he says 'which supplier? A is usually 7 days, B needs 10, C is hit and miss, you have to chase.' None of this is written in Excel.
APS implementer, China

A time window for a given customer on a given day can be much wider than the data says. Some drivers are willing to work an hour extra, others are not. This flexibility is often what makes a plan close.
field study at a container trucking firm, Netherlands

The system itself would be sufficient, but because of an inadequate introduction it is used only in part: invoices, delivery notes, but no production planning or stock management. … The main reason in my view is the missing bills of material and routings
employee at a machining SME, Germany

Read the 7 quotes

1.4 Implementation and integration: the messy process around the solver

The optimisation itself is rarely what fails. What fails is everything around it: vague requirements, assumptions nobody fully understands, a new tool that does not quite match the systems it has to plug into, and an IT department that will not support what it did not build. A small mismatch at the interface turns into a physical error on the shop floor.

The solver never was and is not the real problem. The bottleneck is the messy human process around it: working with vague requirements, half-understood assumptions, iterative fiddling with formulations, and endless validation.
OR consultant, Netherlands

ERP operation numbers were 10, 20, 30; APS used 1, 2, 3. No conversion was done ... the shop executed in ERP order: 30 first, then 10, then 20, and product quality failed.
APS implementer, China

We faced similar challenges with our IT department which caused us to be frequently at odds with them. They tried to stick us with the name 'shadow IT'.
in-house OR practitioner, English-language forum

Read the 6 quotes

1.5 Daily use and trust: parallel running, override, rollback to Excel

Once the tool is live, planners do not trust output they cannot check, so they keep the old way running in parallel, override the plan, or drift back to Excel. A plan that is better on paper can be worse in practice because it ignores what the people executing it know. The tool ends up as a screen for management while the real work happens elsewhere.

Some planners took matters into their own hands and attempted their own validation, for instance by continuing to work in the old system in parallel and checking whether the output was identical. Of course this increased their workload considerably
interview study of APS planners, Belgium

After one logistics firm introduced route optimisation, routes were indeed 8 % shorter, but complaints rose 30 %. The algorithm ignored customers who do not receive in the morning, old estates where big trucks cannot enter, drivers unfamiliar with routes: a plan that looks good but cannot be executed.
routing software vendor, China

The system is for the boss to look at; Excel is what I actually work with.
planner quoted by an APS blogger, China

Read the 8 quotes

1.6 Monitoring and evaluation: nobody can say what the plan was worth

After go-live nobody can say what the optimised plan was worth. There is no baseline, the obvious test against yesterday's plan is never run, and nobody has defined what a good plan is. Savings figures end up as estimates, simulations or judgement, so the tool cannot defend itself when the budget is reviewed.

Schedule once before leaving, keep the result, let the solver run all night, compare. I doubt any APS vendor will let you do that test.
APS consultant, China

many companies are unclear on governance, and don't know how to measure a good plan.
supply chain analyst

Because it relies on human experience, evaluation tends to be qualitative and lacks objectivity and quantified standards.
optimisation vendor, China

Read the 6 quotes

1.7 Keeping it alive: the champion leaves, IT will not maintain it, the consultants are gone

A working model depends on the few people who understand it. Every change in the business has to be built back into the model, and the organisation rarely has anyone in house who can do that. When the champion leaves or the consultants go, the model either freezes or is shelved.

If the champions leave the organization or are transferred to some other part of the organization, the project may easily get shelved.
OR practitioner, English-language forum

For every other department or institution all the specific constraints have to be mapped again and built into the model. You need people with the right knowledge for that. … Care institutions often do not have the people in house who can do this themselves.
hospital rostering modeller, Netherlands

And 'finely tuned' systems tend to turn into unchangeable ones.
practitioner commenting on an APS article, Russia

Read the 6 quotes

2. Matrix views

Three two-dimensional views of what sections 3 to 9 say in prose, quotes and long tables. Nothing here is new: every cell is a compression of a quote block, a number or a table row further down, and every cell links to that place (click the text; row names link to their section). The symbols say how much user-side evidence the lanes found for that cell, not how true the claim is: ●● a user, planner or buyer in their own words, or a field study of them; ● a survey, association, government body, independent practitioner or trade press; ○ vendor-only or indirect material, kept because it admits a problem. "silence" marks a cell where a lane searched and reported finding nothing; a blank cell means the report has nothing for it. In the Egypt / Morocco column "silence" refers to Egypt, which returned nothing; Morocco's evidence is the single OCP report, so only the rows it covers are filled. Columns split the eight research lanes into thirteen regions (the French lane covers France and Québec, the Spanish lane Spain and Latin America, the Slavic lane Poland, Czechia / Slovakia and the Russian-speaking space, the Arabic lane the Gulf and Egypt / Morocco), so a lane counted once in the summary table of 3.8 can show evidence under one of its regions and a blank under another. Wide tables scroll sideways; the first column stays put.

%%{init: {"flowchart": {"useMaxWidth": false}, "themeVariables": {"fontSize": "18px"}}}%%
flowchart LR
  FL[Floor and fleet] --> MON[4.1 Monitoring] --> DAT[4.2 Data integration] --> SOL[Solver] --> ACT[4.3 Action loop] --> PLAN[Executed plan]
  UNC[4.5 Uncertainty, tacit rules] -. unwritten rules must get into the data .-> DAT
  SIM[4.4 Simulation, what-if] -. tests plans before they run .-> SOL
  VAL[4.7 Value signal] -. lets you build .-> EVA[4.6 Evaluator]
  EVA -. decides whether the plan is trusted .-> ACT
  ACT -. plan not followed .-> XL[(Excel plus a skilled planner)]
  PLAN -. rarely measured .-> FB[Did it work? Usually unknown]
  classDef side fill:none,stroke-dasharray:4
  classDef fail stroke:#B23A2E,color:#B23A2E
  class UNC,SIM,VAL,EVA side
  class XL,FB fail
  linkStyle 9,10 stroke:#B23A2E,color:#B23A2E

What has to exist around the solver for a plan to reach the floor, drawn from the seven subsections of section 4. The solid row is the pipeline: observe the floor, clean the data, solve, act. The dashed boxes are what a step needs to work, and each arrow says what it adds. Red is how it usually fails: the plan is not followed and a skilled planner in Excel stays the real system (4.3), and nobody measures whether the executed plan was any good (4.7). The solver itself is rarely the problem; the complaints are in every box around it.

Matrix 3. Vertical × problem type

Rows are problem types, columns verticals, drawn from the section 5 table, its nine vertical paragraphs and the failure stories. "mature" means named in-house teams, long-running deployments or a prize; "stalls" means the lanes found Excel, hand planning, parallel running or rollback; "both" means the cell has mature owners and stalled buyers side by side; "absent" means the report explicitly looked and found nothing; blank means the report does not cover that cell. Region letters: DE DACH · NL Netherlands and Flanders · FR France · QC Québec · ES Spain · LA Latin America (MX Mexico) · PL Poland · CZ Czechia and Slovakia · RU Russian-speaking · GF Gulf · MA Egypt and Morocco · CN China · EN English baseline.

maturestallsbothabsent: looked, nothing foundblank: not covered

Problem type, then vertical	Logistics and transport	Discrete manufacturing	Process industry	Retail and e-commerce	Healthcare	Energy and utilities	Public sector	Mining and agriculture	Services: rail, airlines, telecom
Routing (VRP, last mile)	both: mature CN RU DE CZ (platforms, parcel, large firms); stalls FR ES NL CZ GF EN (small operators, SMEs, drivers)			both: mature CN RU (JD, Cainiao, Ozon); stalls GF (no geocodes)			both: GF Hajj routing 2007 to 2014, lost at hand-over	mature: LA large miners, fleet dispatch for decades; medium miners presumed not
Workforce rostering	stalls: DE Excel first in warehouses (IML); RU driver rostering survives as a calculator			absent: CN retail shift scheduling: papers and vendor pages only	both: mature NL (capacity-management teams); stalls NL EN QC ES; absent CN		stalls: EN inspector roster shot down by unions		both: mature EN DE (airlines, DB crews); stalls NL (NS 2001, De Lijn 2024) EN (AA first attempt)
Production scheduling, machine assignment		both: mature CN RU CZ FR (head firms); stalls DE CZ PL CN EN ES LA RU (SMEs, Excel)	both: mature DE (BASF) CN (tobacco, steel) GF (Aramco LP, snippet); MA OCP unused 5 years, then rescued			mature: FR EDF, Lhyfe; CN power dispatch vendor-only		both: MA OCP (see process industry)
Inventory and lot sizing		both: CZ 84 % large vs 43 % SME; stalls ES CN DE		both: mature CN (JD); stalls EN (suites overkill for mid-size)
Network and facility design	stalls: NL FloraHolland thesis stayed a simulation	absent: no SME evidence in any region	mature: MA OCP supply-chain model, Edelman finalist 2021	mature: CN JD warehouse network, 永辉 site selection		mature: FR GRTgaz Biozone and Minopex, Lhyfe			mature: DE Flix (GOR prize 2024), DB
Cutting and packing	mature: CN Cainiao carton selection, 安吉 vehicle loading	both: mature DE (craft, 250 EUR tool); stalls DE (Insellösung, liability) CN (garment rules); absent NL ES PL CZ RU GF EN					mature: FR DGA cutting stock with explainability
Pricing and revenue		absent: no SME or mid-market evidence anywhere		absent: RU X5 pricing under NDA			mature: EN FCC auctions		both: mature FR DE (SNCF, ÖBB, Sixt, Amadeus, TF1); stalls EN (car-rental tool dropped by IT)
Maintenance planning		absent: LA PL GF: subsidy lists and IoT counts only				mature: FR EDF nuclear outage planning		stalls: LA Chile pilots do not scale	mature: DE DB (GOR prize text)
Timetabling	mature: LA port slot booking, voluntary, no numbers						both: GF Hajj 2007 to 2014, lost at hand-over		both: mature DE FR (DB, ÖBB, SNCF); stalls NL (De Lijn) EN (sports league, once a year)
General planning (APS, S&OP, IBP)	stalls: DE 37 % pure-Excel supply chains; EN 81 % S&OP in Excel	both: mature NL CN CZ (large firms); stalls DE FR ES LA PL CZ RU CN EN below the large tier	both: CN steel S&OP veteran as translator; NL feed PoC without deployment; FR one anecdote	absent: DE NL FR ES GF: nothing user-side; MX pages 403		absent: DE NL ES PL CZ RU EN: no buyer-side voice	both: mature NL (Rijkswaterstaat LP rebuilt yearly); absent GF (SIRI, ITTI: will, no user voice)	both: stalls LA Chile (pilots); absent LA Peru (agro, planning effectiveness about 65 %)	mature: EN airlines and big-tech supply chains

Matrix 4. Region × pull and readiness

One row per region; this replaces the table that stood in section 6. Adoption numbers are the proxies the lanes could open, none measures OR adoption directly. The three pull columns split what the old table called "what is pulling adoption"; a blank pull cell means the lane named none of that kind. "User voice" is how much first-person buyer or planner material the lane found: rich, thin or silent. Row names link to the region's source list, voice cells to its list of what was not found.

Region	Adoption numbers (proxies)	Dominant bottleneck	Labour-shortage pull	Regulation or market pull	Subsidy or state push	Buyer default	User voice found	Verdict
Germany, Austria, Switzerland	PPS software 67 % of manufacturers (2015); 70 % MS Office at detailed scheduling, AT SMEs (2011); 37 % of supply chains pure Excel (2023, vendor-commissioned); analytics "AI" 3 % of Mittelstand (KfW 2026); OR departments dissolved into IT; GOR about 1,050 members; large R&D-intensive firms spend 19× small ones	master data, missing feedback loop; know-how (73 % of logistics firms); interfaces; co-determination for rostering	53 % of logistics firms lack dispatchers, 55 % drivers; 55 % see AI as the answer	works councils demanding traceable criteria (BetrVG § 87)		product (APS from ERP/MES vendors; 35 small routing vendors, 5k to 20k EUR); in-house at DB, ÖBB, Flix, Sixt	thin: one Reddit thread, two woodworker.de threads, the rest vendor anecdotes and surveys	forced rather than ready in road logistics; mature in rail and intercity transport; stalled at the Excel layer in discrete SMEs
Netherlands and Flanders	dense supply side (ORTEC, Quintiq, AIMMS, CQM, LNMB since 1987, STAtOR); hospital rosters by hand "vrijwel overal"; 80 % of a haulier's plan changes intraday; shippers self-rated 6.1/10 (2018); CBS: 4 % of AI-using firms use it for logistics; 7 in 10 Flemish SMEs have no digitalisation plan	soft data not digitised; package fit to department rules; nobody in house to maintain a bespoke model; acceptance (strikes)	care staff shortage; Regioroster hypothesis: preference rosters give 15 to 20 % more hours; 200+ organisations contacted one student	collective-agreement compliance drives rostering purchases (VieCuri)	SmartPort and Dinalog (250+ projects, EUR 100 m+); MIT vouchers to EUR 5,000, not for implementation; VIL AI-readiness programme Q4 2026	product for the standard case (ORTEC Harmony for compliance); bespoke university MILP for hard constraints; subsidised pilots	rich for a small region: STAtOR practitioner articles, TNO field study, Vlerick interviews	ready in hospitals with capacity-management teams and Rotterdam container trucking; density shows as pilots and self-criticism, not measurably higher use
France	no adoption survey; large groups in-house, everyone else buys or outsources (ROADEF, unchanged 2011 to 2019); mid-market "planning is just done in Excel" (Hexaly founder); 60 % of industrial firms on an ERP; 5 % of subsidy beneficiaries "très avancé"; 17 % can deploy 4.0 at planned pace	master data; black-box perception and "retour à Excel"; governance; small operators trust themselves over the tool		GDPR pushing explainable rostering; energy transition (biomethane, hydrogen) creating new problems for incumbents	subsidy money exists but buys 3.0 equipment (60 % of guichet money)	in-house at EDF, SNCF, GRTgaz, Renault, Orange; consulting (Eurodécision) and APS integrators for the rest	thin: one interview study (40 cyclologistics operators), ROADEF bulletins, vendors conceding	ready inside energy and rail incumbents; not ready in PME manufacturing and transport
Québec	STIQ 2021: digital integration "fait du surplace", 8 % autonomous decision-making, robots next to paper work orders; FCEI 2025: three quarters automating production, 30 % used a private consultant, 33 % have not recouped the investment	qualified staff (79 %) and time (75 %); low interest in the least digital firms; labour relations in healthcare	66 % of PME directly hit (2023); shortage as motive 59 % (2025)		IVADO funding (8 M$ page not opened)	consulting and public bodies for automation; nothing shows this reaching planning optimisation	thin: association surveys, one hospital dispute	moving on physical automation; no evidence it reaches planning; healthcare rostering blocked by labour relations
Spain	no OR survey; "la mayoría de las empresas siguen sin aplicar"; INE: 78 to 82 % of non-adopters lack skills, 44 to 53 % cite data; manufacturing SMEs 0.9 % identifiable AI (IndesIA 2026); logistics SMEs 42 % claim AI (Eurobarometer); Activa Industria 4.0 under-subscribed by 40 %	awareness ("¿Y eso qué es?"); cost and skills; academic incentives against transfer	driver deficit 20,000 to 30,000, average age over 50; the ask is immigration; 18.8 % foreign workers in transport		public health tenders (EUR 0.58 M Defence hospital, EUR 0.5 M Galicia); free Industry 4.0 consulting, cut 40 %	product (routing SaaS, APS with integrator); nothing on consulting models staying alive	silent: no forum or LinkedIn planner voice; one 2014 Forocoches planner	latent in logistics and public healthcare procurement; manufacturing SMEs are not pulling
Latin America (Chile, Argentina, Colombia, Peru, Mexico, Ecuador)	Chile: large miners run fleet dispatch for decades, medium miners presumed not (vendor deck), 60 % report gains, 74 % resistance to change; Argentina: SMEs build improvised in-house APS; Colombia: 3.9 % of Bogotá manufacturing SMEs use specialised data software; Peru: agro-export up 65 % 2020 to 2024, planning effectiveness about 65 % (secondary); Mexico: nothing openable	scaling pilots across sites (CL); data and method (AR); cost and training (CO); connectivity and hierarchical decisions (PE)			Argentina KIT 4.0, 50 % co-financing, planning software not named; Chilean port appointment system taken up voluntarily; supplier (METS) maturity rising	product (mine FMS, routing SaaS); in-house ad hoc in Argentine SMEs; consulting as rescue	silent in Spanish forums; trade press and association surveys only	ready only in Chilean large mining and ports; everywhere else pre-data; Mexico unknown
Poland	2 % APS among 350 manufacturers (2016, vendor survey); ERP 39.8 % (2017), 30 % (2025); Opolskie: Industry 4.0 in 12 to 14 % of firms, "na wczesnym etapie"; nothing newer on planning tools	data-use skills; unsystematic adoption; worker reluctance to change ("niechęć pracowników", 71 to 75 %)	young workers "szczególnie niechętni do pracy zmianowej"; firms automate for operational stability			integrators (ASTOR, Streamsoft, DSR) with vendor prose; no user account of either path	silent: no planner, logistician or plant manager in their own words despite nine queries across two engines	unknown; the one hard number is a decade old; silence is the finding
Czechia / Slovakia	NCP 4.0 2023 (207 directors): routing analytics 68 % large vs 18 % SME, inventory analytics 84 % vs 43 %, 23 % of SMEs integrate data centrally; SME intent on routing analytics (28 %) exceeds large firms (23 %); Adastra (vendor sample): 71 % Excel, 11 % predictive tools, 45 % plan under one week; 17 years of APS debate in IT Systems; Slovakia one webinar promo	data: stale purchasing dates, unrecorded actual times, dead orders; planners revert to Excel; client cannot say what planning should deliver	wages up 8 % vs productivity up 2 %		OP TAK covering 20 to 40 % of advanced non-production digital technologies (2 bn CZK planned)	product (APS) with integrators; recommended sequence: audit, process, then software	thin but honest: vendor-side authors arguing against their own pitch; one 2008 user piece	readiest measured gap in the Slavic lane: large firms ahead, SMEs behind, money on the table
Russian-speaking space (Russia, Ukraine, Belarus, Kazakhstan)	HSE 2025 (1,700 machine builders): process automation almost half, broadly 23 %, big data 16 %, AI agents 3 %; Preactor base in electrical, machine building, building materials; about ten domestic APS products since 2022; APS used "as a calculator or Excel substitute" (vendor); routing SaaS down to five-vehicle firms; platforms in-house	budget (57 %) and payback; no shop-floor accounting (GIGO); driver incentives and acceptance; SaaS onboarding and price for small fleets			state as driver and import substitution; logistics automation interest rising to 26 %	consulting-led for planning (Rightstep); SaaS for routing; in-house at Yandex, Ozon, X5, Dodo	rich: Habr articles and comment threads, two first-person vc.ru accounts, app-store reviews	ready for domestic vendors in platform logistics and head manufacturers; a closed market for foreign ones; Ukraine, Belarus, Kazakhstan barely visible
Gulf (Saudi Arabia, UAE, Qatar, Kuwait, Oman, Bahrain)	only digital maturity measured: 479 Saudi factories SIRI-assessed by mid-2024 (target 4,000), 620+ UAE factories under ITTI; GASTAT AI adoption 33.1 % (transport and storage 44.4 %), no planning indicator; McKinsey GCC: two thirds stuck in pilots, 11 % with financial return; Aramco LP (job snippet, page timed out); DP World rules-based berth planning (2011)	human capital (lowest score in the one Saudi survey); fragmented pilots; addresses and geocodes for routing; no institutional memory after consultants leave			grants up to 75 % of project costs; Dh 7.8 bn UAE financing; ITTI participation raises In-Country Value score; logistics a national priority (LPI about 30 vs top-25 target)	consulting at state level (about EUR 2 bn a year in Saudi consulting, "Ministry of McKinsey"); products via subsidised roadmaps; no Gulf user review of any APS or routing product	silent: not one Gulf planner, plant manager or IT lead in either language	capital and will measurable, capability admitted as the gap, no user voice; ready on paper, unknown in practice
Egypt / Morocco	Morocco: OCP (consulting-built supply chain model, Edelman finalist 2021; shelved scheduling optimiser rescued by local universities); Egypt: OECD innovation review returned 403, nothing else found	planner trust and tacit rules; ownership after the consultants (OCP)				consulting, then academic rescue (OCP); OCP's in-house Business Steering Group; "African researchers are the most suitable"	one rich academic implementation report; otherwise silent	OCP is a ready owner; Egypt is covered only by its absence
China	intelligent APS penetration 3 to 4 % among head customers; 10 % of discrete manufacturers have "tried or discussed" it (investor survey); customers mainly above RMB 400 m revenue; 86.92 % of SMEs at basic informatisation; a moderately deep project about RMB 1.5 m; platforms with large in-house OR teams and own solvers; 上汽安吉 system at 90 %+ usage since 2016	base data (BOM, standard times) and shop-floor discipline; expectation mismatch from vendor demos; planners revert to Excel; no plan owner; tightly coupled code	hiring difficulty 46.5 % of 955 Jiangsu manufacturers (2021)	on-time delivery pressure in auto parts	30 pilot cities, 15,000+ SMEs, 2026 "上云用数赋智"; domestic solver push (COPT, MindOpt, OptVerse) under 信创	in-house at platforms and large firms; vendor projects (4 to 8 months, RMB 0.5 to 2 m) in manufacturing; consultants as translators	rich: Zhihu, CSDN, DataFun transcripts, a salesman's failure story, worker-side journalism	ready and in-house at the top; below RMB 400 m revenue the public record is dominated by APS failure
English baseline (US, UK, Nordics; India, Ireland, Australia searched, nothing OR-specific found)	planning software owned by over 92 %; APS deployments completed by about 10 % (second-hand); 65 % miss ROI (n = 80); 67 to 81 % plan in Excel; scheduling modules the highest shelfware rate (15 %, 2003); NHS e-rostering mandated 2018 but "immature"; routing viable above roughly 5 to 15 vehicles	data gaps found at integration testing; planner override; IT refusing to maintain; champion leaves; consultancy dependence	46 % of US manufacturers cannot hire schedulers; 35 % of large ones put advanced scheduling in their top two priorities (Deloitte via vendor page)	CSRD and CO2 data in Nordic tenders (five commenters in one thread, anecdote); Carter Review mandate for NHS e-rostering; airlines' money at stake		product (APS, routing SaaS, e-rostering) then consultancy-dependent; in-house at airlines and big tech	rich: OR Stack Exchange, Reddit planner threads, named practitioners	mature in airlines and big-tech supply chains; stalled at Excel and driver adoption elsewhere; Nordic routing the one regulation-pulled signal

Matrix 6. Buying path × region

Who buys what, from section 5 and the buyer-default column of matrix 4: in-house teams, consulting or bespoke builds, bought products, and the Excel-plus-planner layer that the report says neither path displaces. Vendor admissions against their own interest are marked ○ as elsewhere.

●● user, planner or buyer in their own words, or a field study of them● survey, association, government, independent practitioner or trade press○ vendor-only or indirect, kept because it admits a problemsilence: the lane searched this and reported nothingblank: the report has nothing for this cell

Path, then region	DACH	NL / Flanders	France	Québec	Spain	Latin America	Poland	Czechia / Slovakia	Russian-speaking	Gulf	Egypt / Morocco	China	English baseline
In-house OR team	● DB, ÖBB, Flix, Sixt; OR groups otherwise dissolved into IT	● Amro, NMB, Akzo, PTT groups dissolved in the 1970s and 80s	● EDF, SNCF, GRTgaz (8), Lhyfe (5), Renault, Orange			● Argentine SMEs build improvised ad hoc apps	silence		●● Yandex, Ozon, Dodo, X5	○ Aramco refinery LP (job snippet only)	●● OCP Business Steering Group after the rescue	●● platforms with large teams; JD shed solver dependence; self-built APS disputed	●● airlines (Southwest) and big tech
Consulting or bespoke build		● university MILP for hard constraints; student contacted by 200+ organisations	● SSII or academic labs for firms without in-house OR (ROADEF); Eurodécision clients large groups	● 30 % used a private consultant for automation	silence	● consulting as rescue	○ integrators with vendor prose, no user account	○ integrators; audit, process, then software	● consulting-led planning (Rightstep)	● about EUR 2 bn a year, "Ministry of McKinsey"; no institutional memory	●● consulting-built model shelved, rescued by local researchers	○ 杉数 project deals in millions of RMB; vendors admit the project model scales with headcount and fails more than it succeeds	○ "never once without consulting, customisation and integration"
Bought product	● APS from ERP/MES vendors; 35 routing vendors at 5k to 20k EUR; "überdimensioniert" for Mittelstand	● ORTEC Harmony bought for cao compliance and the ERP link; Agrifirm routing on a pay-structure change	○ APS integrators; vendor concedes planners abandon the tool for Excel		○ routing SaaS, APS with integrator (vendor pages)	● mine FMS, routing SaaS; SAP APO-class products rejected by Argentine SMEs	○ 2 % APS among 350 manufacturers (2016, vendor survey)	● APS pays off only where machines can switch products (2008 user); 71 % Excel (vendor sample)	●● routing SaaS down to five-vehicle firms; "APS-scheduler rarely necessary"	○ products via subsidised roadmaps; no user review of any product	silence	● vendor projects 4 to 8 months, RMB 0.5 to 2 m; success 30 % software, 70 % implementation team	●● 92 % own APS, 10 % complete; "Kinaxis admitted they were overkill"; lock-in
Excel plus a skilled planner	● 37 % pure Excel supply chains; 70 % MS Office at scheduling (AT); skill of individuals compensates	● hospital rosters by hand "vrijwel overal"	○ "planning is just done in Excel" in the mid-market (Hexaly founder)	● paper work orders from a spreadsheet next to robots	○ "Excel que nadie quiere tocar" (vendor); one planner sets stock by eye	○ parallel spreadsheets, expertise in individual heads (vendor)	○ only 2 % APS among 350 manufacturers (2016, vendor survey)	○ 71 % plan in Excel; "back to Excel" ending	●● planning "not implemented, or in MS Excel"; rostering MIP survives as a calculator	○ landmarks and WhatsApp pins rather than data	●● OCP planners scheduled by hand for 5+ years	●● "Excel is what I actually work with"; the S&OP veteran became part of the system	● 81 % of S&OP in Excel; "we always end up back in Excel"

3. What solving actually depends on

The solver is a small part of the stack. Across the eight lanes the model itself is almost never what a user complains about; what is missing is one of the layers around it, and the complaints sort cleanly into the seven dependencies below. Two practitioners from opposite ends of the map say it in one sentence each. The Dutch consultant John Poppelaars, in STAtOR (July 2026): "de solver was en is nooit het echte probleem. Het knelpunt zit in het rommelige, menselijke proces eromheen, werken met vage eisen, half begrepen aannames, iteratief gepruts met formuleringen, en eindeloze validaties." (The solver never was and is not the real problem. The bottleneck is the messy human process around it: vague requirements, half-understood assumptions, iterative fiddling with formulations, and endless validation.) STAtOR 27(2), p. 30-31. And an anonymous APS consultant with twenty years in Chinese factories (2020): "APS的意义不是排程优化，而是排程自动化" (The point of APS is not schedule optimisation but schedule automation). 数据魔术师 mirror. Each subsection below states what the layer is, lists every quote, number or story from the eight lanes that bears on it, and ends with a one-line reading of how often that layer is the real blocker. Where a lane found nothing, that is said.

3.1 Monitoring infrastructure, hardware and software

A plan is only as good as the planner's view of what is actually happening, and most operations cannot see it. Changes arrive by phone or word of mouth and never reach the system, breakdowns go unrecorded, and tracking hardware fails or is switched off by the people it tracks. The plan describes the morning, and by midday it no longer matches reality.

During the day, after the first trip, 80% of the plan still changes, by the planners' own estimate.
planners at a container trucking firm, Netherlands

sales changes or inserts orders verbally, offline adjustments are not synced to the system ... the plan loses meaning and becomes decoration
software vendor to garment factories, China

So in three years we lost 9 devices.
in-house developer at a distributor whose drivers destroyed the trackers, Russia

Read the 12 quotes

An optimiser plans from a picture of the current state. If order status, machine state, driver position and breakdowns are not captured and fed back, the plan describes the past and is re-optimised against nothing. This is the feedback half of the loop; without it a schedule can only be built once, by hand or by model, and then drifts.

English lane: no user described shop-floor or fleet sensing as such; the nearest is a route-software seller's line that "Drivers ignoring routes and bad address and time-window data never end" (r/logistics), which belongs as much to data and to the action loop.

3.2 Data integration, ingestion and silos

The data exists, but in pieces, in systems that were never built to talk to each other. Firms use software somewhere, yet few bring the data into one place, so every planning project starts with interfaces and file cleaning that take longer than the model. The limit is often people and coordination, not technology.

Some software is used in part of production by 93% of large and 74% of SMEs. Overall digitalisation and data integration is another matter, with much worse results.
survey of manufacturing directors, Czechia

was never designed to interface … a human rather than technical constraint
cycle-logistics operators on importing client orders, France

Their team spent two weeks just cleaning route data and time windows before writing integration code.
routing software integration, Sweden

Read the 13 quotes

The optimiser needs bills of material, routings, standard times, lead times, addresses and time windows as structured input, and it needs them pulled from the systems where they live rather than typed again. When that layer is missing, the project becomes a master-data project first, the planner becomes a data clerk, and the model's output is only as good as the stale input.

3.3 The action loop: open or closed

Even when the model produces a good plan, the plan rarely reaches execution intact. Planners rebuild it in a spreadsheet, adjust it out of habit or override it, and drivers ignore the sequence. The planning system becomes a place to record decisions made elsewhere, so the loop from optimisation to action stays open.

Instead of using the expensive optimizers in these systems, planners work on spreadsheets … write the data to the planning systems as a system of record.
supply chain analyst, survey of planning users

Algorithm plans go through manual fine-tuning; people make unnecessary adjustments out of habit, hurting the global result.
e-commerce logistics operator on its own dispatchers, China

forget the safety-stock formulas, they are useless
automotive logistics planner, Spain

Read the 9 quotes

A closed loop means the optimiser's output is what gets executed, and the result of executing it feeds the next run. An open loop means the output is a suggestion that a planner reads, adjusts or ignores, while the real plan lives somewhere else. Most of the corpus describes open loops: a bought system used as a system of record, parallel Excel, dispatchers overriding, drivers not following.

3.4 Simulate as well as optimise: a digital twin of the system

Almost nobody models the operation as a whole to test a plan or a change before committing to it. Where simulation appears, it is a one-off study whose redesign is not implemented, or a way to produce a savings figure rather than a tool planners use. Often the cheaper fix is in the process, not in a richer model.

is often hard to sell … so that it is easier to follow
OR modeller on a public road programme, Netherlands

manual loading compared with the algorithm's simulated result
grocery routing vendor on how its 8% saving was measured, China

sometimes the simplest method is the most effective
research institute, on handling pilot sick calls, China

Read the 4 quotes

A model that can only return "the" optimum cannot answer the questions planners actually ask: what if this machine is down, what if we add a standby, what if the time windows are looser. That needs a simulation layer or a digital twin that runs cheaply and often, with the optimiser as one tool inside it. This is the dependency with the least evidence in the corpus.

Gulf and English lanes: no user voice on a simulation or digital-twin layer of a planning system. The English Southwest story (year-long "contests" against human planners) is evaluation, not simulation, and is placed in 4.6.

3.5 Accounting for uncertainty

Real operations are full of variation that never reaches the model: a loading bay blocked at a certain hour, a driver who will stay late, absences at short notice, constraints nobody says out loud. Human planners absorb it, while the model treats its inputs as fixed and its plan breaks on contact. Small changes in the data can also swing the solution entirely, which makes the output hard to trust.

An algorithm might spot the mathematically optimal path, but it does not know that a specific loading bay gets blocked by refuse collection at 10 AM or that a particular road is awful in the snow.
logistics practitioner forum

'Unspeakable' constraints. There are some constraints that the client will not be able to give you.
OR practitioner forum

how even small data changes can fundamentally change the solutions
OR prize lecture on inventory optimisation, Germany

Read the 6 quotes

Real inputs are soft: time windows are wider than recorded, drivers' willingness varies, machines break, suppliers are "hit and miss". A deterministic model treats those as fixed and produces a plan that is brittle on first contact. The two ways to cope are a prediction layer that forecasts them, or cheap scenario runs that test the plan against them; the corpus shows planners doing both in their heads instead.

3.6 Being able to write evaluators

To trust a model, someone has to test it against what planners would have done, on real data, and explain why it decided what it did. Few organisations can do this: the objective is a guess, nobody can explain the output, and doubt about the tool sets in. Where evaluation happens at all, users or outside researchers build it themselves.

a weighted sum of several key performance indicators (KPIs) where the weights are found by trial and error
researchers reviving an unused commercial scheduler, Morocco

You can only trust an optimisation model by testing it on relevant data.
optimisation vendor, France

one must be able to explain the tools' outputs so that the commanders take ownership
defence planning case, France

Read the 5 quotes

An evaluator is a way to say whether a plan is good without trusting the objective function: a benchmark against the planners' own plan, a plan-execution rate, a dry run, a held-out week. Without one, the objective's weights are tuned by feel, trust is never earned, and parallel running by planners becomes the only test.

Spanish lane: no user or practitioner voice on how a plan or model was evaluated; the sources describe outcomes ("el resultado no será satisfactorio") without a method.

3.7 Clear signals of real-world value

Optimisation survives where its value is large, direct and easy to count, and dies where it is not. Most buyers cannot show a return, so tools sit unused or end up doing what a spreadsheet did. The clearest signals are small and concrete: money saved on a single order.

We spent a year setting up a new tool. No one uses it because the process wasn't in place.
supply chain survey respondent

many bosses spend millions on APS and end up replacing the planner's Excel
manufacturing IT publication, China

saved about 200 EUR on one order
woodworker on a 250 EUR cutting optimiser, Germany

Read the 7 quotes

The last dependency is a measurable link from the plan to money earned or saved in the bigger picture, so that the project can be judged, defended and kept alive. The corpus is full of savings figures without baselines, ROI that is missed or unmeasurable, and cases where the value came from somewhere other than the optimiser.

3.8 Summary table

Dependency	Lanes with evidence (of 8)	Typical symptom in the user's words	Who owns this layer in practice	Strongest evidence
Monitoring infrastructure	7: Dutch, German, Slavic, Chinese, French, Spanish (vendor only), Gulf (vendor only); English none	"der fehlende Rückkanal ist es"; 80% of the plan changes intraday; routings built with stopwatches	IT/OT in large firms; in SMEs nobody (paper, WhatsApp, the planner's memory)	TNO Talking Trucks
Data integration, ingestion, silos	8: all	"špatný vstup znamená špatný výstup"; no BOMs entered eight years after the ERP; lead times in the planner's head	Nobody by default; the planner inherits it; vendor contracts leave it out (data cleaning 40-60% of the project)	r/de_EDV machining SME
Action loop, open or closed	8: all	"Excel才是我真正用来干活的"; parallel running in the old system; drivers ignore the sequence	Planner or dispatcher; closed only where management enforces it or the user is the builder	Vlerick, 26 planners
Simulate as well as optimise (digital twin)	0 direct; 5 indirect: Dutch, Slavic, Chinese, German, French; English, Spanish (one subsidy line), Gulf none	Not named by users; the nearest ask is "Rückmeldung, schnelle Neuberechnung und gemeinsame Sicht"	Academics (thesis stayed a simulation) or in-house teams at large firms; absent elsewhere	王源 talk
Accounting for uncertainty	8: all	"nous faisons un meilleur travail d'optimisation que le logiciel"; "hard constraints become soft"; 8% shorter routes, 30% more complaints	The planner's head; no lane found a bought prediction layer that users trusted	Cyclologistics study
Writing evaluators	7: Gulf, Chinese, Dutch, English, Slavic, French, German; Spanish none	"the weights are found by trial and error"; "我估计没有APS让你这样验证"; planners validate by hand	Nobody; users improvise, a field study or an in-house team supplies it when it exists	OCP, GERAD G-2023-29
Clear signal of real-world value	8: all	"tens of percent from mileage control, a few from routing"; 65% miss ROI; 33% not broken even; savings with no baseline	Management and finance, usually absent; vendor claims fill the gap	FADO logistics head

3.9 Claude's opinion, not a dataset

For someone choosing where to work in optimisation, the solver and the modelling layer are the commodity: every lane has vendors, every large firm has or buys one, and Poppelaars' point that AI accelerates exactly that part holds across the corpus. The undersupplied layers are the ones no job title owns: getting feedback from the floor or the fleet into the model, keeping master data alive after the consultants leave, and writing an evaluator that a planner would accept. Data integration is the most frequent blocker but also the most crowded with integrators and ERP vendors, so it is where the work exists and the differentiation does not. The action loop and the evaluator are where the corpus shows almost nobody working and where the only successes (TNO's benchmarked trials, Southwest's year of contests, the OCP rescue) came from people who sat with planners for months. Simulation and digital twins are absent from user demand in all eight lanes, which makes them either an open field or a solution looking for a problem; the evidence here cannot tell which, and the users' stated wish is fast recalculation rather than a twin. The value signal is weakest in manufacturing SMEs and strongest where a missing person or a known fuel bill makes the saving countable, so manufacturing roles should be judged by whether a baseline exists before the model does. The practical test for a role: ask who owns layers 1, 3 and 6; if the answer is "the planner" or nobody, that is the job, whatever the title says.

4. Failure stories

Matrix view: Vertical × problem type

Fifteen short ones. "Vendor-told" means the only account is from a seller; "second-hand" means relayed by someone who was not there. Where the source page is gone, it says so.

Region	Vertical	Problem type	What happened	Why	Source
Netherlands	Rail	Crew rostering	NS "Rondje om de kerk" (2001): efficiency-driven crew rosters, crews bound to at most two routes a day; strikes in 2000 and 2001; afterwards NS "had meer personeel nodig, terwijl tegelijkertijd het zieketeverzuim enorm steeg"; hundreds of trains cancelled daily for months; NS dropped it.	"enorme weerstand onder conducteurs en machinisten" (enormous resistance among conductors and drivers). Sources are 2015 retrospectives; method or software not stated.	treinreiziger.nl
Flanders	Public transport	Rostering, timetabling	De Lijn, October 2024: spontaneous strike at Leuven and Tielt-Winge depots over January rosters tied to a network overhaul.	Union: "de samenstelling van de diensten is totaal onlogisch" and "geen inspraak geweest is van de chauffeurs" (duties illogical, drivers had no say). Tool not named.	vrt.be
Morocco	Process industry, mining	Production scheduling, vessel assignment	OCP Jorf Lasfar: commercial MILP optimiser ran 10 hours to a poor schedule; planners went back to manual scheduling for over five years; rescued from 2020 by local researchers, now feeding SAP, claimed +5 % turnover.	Objective weights "found by trial and error", poor documentation, hard-coded constraints, tacit rules not modelled, planners who had "witnessed the ineffectiveness".	gerad.ca PDF
Saudi Arabia	Public sector	Timetabling, pedestrian routing	Hajj stoning-ritual scheduling (MIP and QP, ministry-funded, academic-built) credited with no crowd deaths 2007 to 2014; by 2015 the authors and ministry "no longer handled scheduling and routing for that ritual"; 2015 disaster.	Hand-over from external builders; the paper does not say why it failed. Edelman-finalist status from search snippets only (INFORMS page returned 403).	iaorifors.com
China	Discrete manufacturing (injection-moulding machines)	Production scheduling, APS	Competitor's APS implemented "分车间实施 ... 上线一个车间收取一个车间项目费用" (workshop by workshop, charged per workshop); kitting across casting, machining, sub-assembly and final assembly was "假齐套" (fake); project could not be sustained; the client's PMC and IT head resigned.	Sold and scoped per workshop, so coordination stayed offline. Told by a competing salesman.	sohu.com
China	Logistics	Routing	Routes 8 % shorter, complaints up 30 % after a route optimiser went in.	Receiving hours, estate access and driver familiarity not modelled. Vendor-told (科箭), against interest, company unnamed.	qt-asia.com
China (France site)	Parcel logistics	Routing	Cainiao's France parcel station: third-party routing "调度时间过长，有大量包裹调度失败" (too slow, many parcels unassigned), heavy overlaps, "技术支持响应速度慢，服务费用高" (slow support, high fees); replaced by the in-house team.	"没有人工调度员，完全依赖算法结果；国外地址解析准确率低" (no human dispatcher, poor address parsing abroad). Buyer's account of a vendor product.	sohu.com
Russia	B2B parts distribution	Routing	Relog routing SaaS: six-month prepayment, bugs, five-minute solves, unresponsive manager, traffic data claimed as Yandex turned out to be 2GIS; contract ended, refund took two months and a formal claim. Yandex Routing kept.	Onboarding, integration and support quality; first-person buyer account.	vc.ru
Russia	Discrete manufacturing (steel radiators)	Production scheduling	MES project: plant had no routings, technologists built them with stopwatches; product labels for per-order costing abandoned after a customer complained about jammed printers; director disabled traceability.	"Люди очень болезненно относятся к тому, что за ними следят" (people react very painfully to being watched); "техническая реализация ... это лишь полдела" (technical implementation is only half the job).	habr.com
Russia	Discrete manufacturing (large plant)	Production scheduling	Customer's previously chosen planning system "не смогла рассчитать план по исходным данным" (could not compute a plan from the source data); 7-level BOMs, 69,969 chained operations; detailed scheduling of 208,103 operations took about 13 hours.	Solve time as the limiting factor. Vendor-told (CS Group / Dassault) against the previous vendor.	habr.com
Germany	Machining SME	Production planning (ERP planning module)	ERP bought eight years earlier, used only for invoices and delivery notes; no production planning or stock management; employee asks Reddit how to restart it.	"die fehlenden Stücklisten und Arbeitsgänge" (missing BOMs and routings) with nobody who has machining knowledge to maintain them; replies: doomed "ohne Unterstützung aus höheren Ebenen". First-person; read via the local Reddit archive tool, post truncated at the end.	reddit.com
US	Airlines	Crew recovery	American Airlines irregular-operations recovery: the team "wrote the math and handed it over without engaging users. Data was scattered, and users doubted a computer could solve the problem. After six months, there was no traction."	No user engagement; the later success at Southwest came from a year of head-to-head "contests" against human planners. Buyer-side speaker on a vendor blog.	nextmv.io
Portugal (told in English)	Car rental	Pricing and revenue (reservations)	Consultant-built reservation decision support "considerably more efficient than the existing solution" was dropped.	Data inputs laborious and "the IT department wasn't willing to support it." Second-hand (relayed from a Carravilla talk on OR Stack Exchange).	or.stackexchange.com
Spain	Manufacturing SMEs	General (subsidised Industry 4.0 consulting)	Activa Industria 4.0 cut on 2025-01-29 from EUR 24.42 M and 3,300 SMEs to EUR 14.8 M and 2,000 SMEs.	"la previsión inicial ... ha sido notablemente superior a la realidad": free consulting under-subscribed by about 40 %. Stall at the will and awareness stage.	boe.es PDF
France	Industrial company (unnamed)	APS / S&OP	APS licence not renewed after three years because planners and sales never used it, told by its S&OP manager.	The page returned 404 on 2026-10-16; the story survives only in a search-engine snippet, so the wording could not be verified. Kept here as the one named-process French stall; the French lane could not check the wording.	blog.lean-supply-chain.fr (404)
Québec	Healthcare	Nurse rostering	CIUSSS Mauricie-et-Centre-du-Québec, February 2023: after self-scheduling failed to fix staffing, nurses in 7-day units were ordered to work "une fin de semaine sur trois"; the union reported about thirty resignations linked to the change (not confirmed by the employer).	Not a software failure: a rostering problem where the "solution" was a blunt rule and labour relations dominated. "le statut quo n'est plus possible".	ciusssmcq.ca · radio-canada.ca
Chile	Mining	General (fleet, maintenance, AI)	Pilots that do not scale: 74 % of miners name resistance to change as the main brake, 61 % a business-technology gap; pilots of three to six months too short, sites too different, supplier and operator objectives misaligned.	"El principal desafío no está en la adopción inicial de tecnología, sino en su escalamiento." Consultancy and association sources; Deloitte/CESCO full report behind a form, sample size unknown.	deloitte.com · cesco.cl

What is missing from this table is as telling as what is in it: no German, Polish, Spanish, Latin American or Gulf company was found describing its own abandoned optimisation project in its own words. The German, French and Spanish lanes each searched for this directly ("gescheitert", "abandonné", "retour à Excel", "dejó de usarse") and got vendor and consultant pages back.

5. By vertical and problem type

Matrix view: Vertical × problem type

One consolidated table. "Mature" means named in-house teams, long-running deployments or a prize; "stalls" means the regional files found Excel, hand planning, parallel running or rollback there. "None found" is a gap in this run, not evidence of absence.

Problem type	Verticals and places where it is mature	Where it stalls	Dominant bottleneck	Product or consulting	Strongest evidence
Routing (VRP, last mile)	Platform logistics and e-commerce, in-house (JD, Cainiao, Meituan, SF; Yandex, Ozon, Dodo); parcel (DHL, GOR prize 2016); vehicle logistics (上汽安吉, usage above 90 % since 2016); Czech large firms 68 %	Small urban operators who route by hand (France cyclologistics), small fleets (Spain), mid-size hauliers where 80 % of the plan changes intraday (Rotterdam), Czech SMEs 18 %, the Gulf (no geocodes), drivers everywhere	Driver and dispatcher acceptance; address and time-window data that "never end"; local rules not encodable	Product (SaaS and small vendors, 5k to 20k EUR a seat in Germany) with in-house glue code; in-house at platforms	TNO 2022 · r/logistics
Workforce rostering / staffing	Airlines (US, after year-long validation; Etihad buys crew products per search snippets, the job pages returned 404); rail crew (DB); hospitals with capacity-management teams (Gelre, Radboudumc, ZGT)	Hospitals generally (NL "vrijwel overal nog met de hand"; NHS mandated but "immature"; Québec labour relations); public transport (NS 2001, De Lijn 2024, Mebis 2025); shift work under works councils (DE)	Staff acceptance, fairness and opacity; fit of standard packages to local rules; nobody in house to maintain a bespoke model; co-determination and GDPR explainability	Both: products (ORTEC Harmony, e-rostering suites) bought for compliance and integration; bespoke MILP from universities for the hard constraints; in-house at airlines and rail	STAtOR 2026 · Drake 2017
Production scheduling / machine assignment	Head discrete manufacturers: Chinese auto parts, batteries and electronics above RMB 400 m revenue; Russian electrical, machine building, building materials (Preactor base); Czech automotive-supply forging with EDI into APS; French groups (Renault, STMicro, Lhyfe); energy incumbents	SMEs everywhere: 70 % MS Office in Austria (2011), 71 % Excel in large Czech firms (vendor sample), 2 % APS in Poland (2016), "APS goes live, planners do not use it, back to Excel" in China, highest shelfware rate of any planning module in the US (15 %, 2003), machining SME with no routings (DE)	Master data (BOMs, routings, standard times), missing feedback loop, planner tacit knowledge not in the model, trust	Product (APS, mostly MES-attached in Germany) plus integrator; large firms self-build; consultancies advise SMEs against full APS	Fárek 2025 · Zhihu APS thread
Inventory and lot sizing	E-commerce in-house (JD); Czech large firms 84 %; FMCG IBP (vendor cases only)	SMEs: Czech 43 %; Spanish automotive planner sets stock "a ojo" and calls the formulas useless; Chinese lead times live in planners' heads; mid-size retailers find the suites "overkill"	Inbound supply and supplier lead-time data; conflicting departmental KPIs; solution instability under small data changes (GOR 2025)	Product (IBP suites, "true optimization ... you just have to be able to afford it"); in-house at JD	NCP 4.0 2023 · r/supplychain
Network and facility design	Inside incumbents and platforms: Flix (GOR prize 2024), DB, GRTgaz (Biozone, Minopex), Lhyfe, JD (warehouse network), OCP (Dynamic Ideas model, Edelman finalist 2021)	Nothing on SMEs in any region; the FloraHolland thesis stalled at simulation; a capex study rejected by the CEO over the discount rate (English)	Senior sponsorship and the right economic assumptions; ownership after consultants leave (OCP)	In-house with university partners, or consulting for one-off studies	GOR prize page · OCP Interfaces 2022
Cutting and packing	Craft-level cheap products (BESTOPT about 250 EUR in 2009, pays back on one order, woodworker.de); Cainiao carton selection; vehicle loading at 安吉物流; DGA cutting stock with explainability (France)	New entrants face "Insellösung" and liability objections (DE 2026 thread); garment cutting-table rules missing from generic APS (CN); not found at all in NL, ES, PL, CZ, RU, Gulf, English	Integration with existing systems and liability for wrong cuts; sector-specific rules absent from generic products	Product	woodworker.de 2026
Pricing and revenue	Rail, airlines, TV advertising: SNCF OPTIPLACE in production since summer 2024, ÖBB yield (3 OR specialists), Sixt (GOR prize 2018), Amadeus, TF1 with LocalSolver; FCC auctions (Hoffman)	No SME or mid-market evidence anywhere; X5's retail pricing work hidden under NDA; car-rental reservations killed by IT	Not documented from the user side beyond IT willingness to run it	In-house, or vendor-built for one large client	ÖGOR PMO 107 · ROADEF 46
Maintenance planning	DB (GOR prize text), EDF nuclear outage planning (ROADEF 2026 industrial session)	No user voice in any region; appears only as "predictive maintenance" in Industry 4.0 subsidy lists (Argentina KIT 4.0, Opolskie glossary) and Gulf IoT counts	Unknown; one English in-house example turns on IT turf and leadership backing	In-house	ROADEF 2026
Timetabling	DB, ÖBB, SNCF (prototypes industrialised); Hajj 2007 to 2014; port slot booking (TPS Valparaíso, voluntary, no numbers)	Hajj after hand-over; sports leagues ("only scheduled once a year"); De Lijn's network overhaul produced the rosters drivers struck over	Hand-over from external builders; frequency of use too low for a product	In-house plus academic; government-funded academic builds	Hajj, Interfaces 2016
General planning (APS, S&OP, IBP)	Owned by over 90 % of surveyed firms (English baseline); multinationals have APS (Vlerick study); Chinese head customers above RMB 400 m	Everywhere below the large tier: finished by about 10 %, 65 % miss ROI, 67 to 81 % plan in Excel (English); 37 % pure Excel in German supply chains; S&OP "devient un rituel" (France); "APS沦为高级Excel" (China); "Недовнедренная ERP" across Russian plants	Data found broken at integration testing; governance ("refuse d'arbitrer"); parallel Excel plan; no plan owner ("planning is the last stop for the old, weak, sick and disabled")	Product plus consulting integrators; consultant dependence named as a post-go-live problem (Chalmers)	McKinsey APS survey · AbcSupplyChain

By vertical

Logistics and transport. Ahead wherever the user is the builder: Chinese and Russian platforms, parcel incumbents, Rotterdam container trucking with subsidised pilots that matched planners' punctuality. Behind in road haulage SMEs: Dutch transport and storage is the one sector with no AI growth 2023 to 2025 (CBS: 7 %, 4 %, 7 %), Spanish logistics SMEs claim above-EU-average AI use (42 % vs 30 %) but the industry's ask is for drivers and immigration, not optimisation, and Gulf last mile runs on landmarks and WhatsApp pins. The pull is labour shortage (53 % of German logistics firms lack dispatchers, 30,000-driver deficit in Spain) and, in the Nordics, CO2 data in tenders.

Discrete manufacturing. Behind below the large tier in every region, and the most richly documented failure vertical: Excel at the scheduling layer in Austria, Czechia, Poland, China, Spain, Argentina and the US; APS bought as an MES add-on in Germany; "back to Excel" as the standard ending in Czech and Chinese sources; the first project phase is master data. Ahead only in head firms with on-time delivery pressure (Chinese auto parts and batteries, Russian electrical and machine building, Czech automotive forging, French groups). The pull is the planner shortage (46 % of US manufacturers, Deloitte via vendor page; "Erfahrene Produktionsplaner sind schwer zu finden"), and state subsidies that mostly buy hardware (France: 60 % of guichet money went to industry-3.0 equipment; China: "补贴集中在采购机器人、自动化设备上").

Process industry. Ahead where the owner has had planning economists and LP for decades: Aramco refinery LP (job snippets only; the careers page timed out), OCP, BASF (GOR prize 2008), tobacco and steel in China (vendor and veteran accounts). The OCP story shows both the maturity and the failure mode in one company. Otherwise thin: one Dutch animal-feed proof of concept with no deployment, one French chemicals ERP anecdote, Czech chemical specialisation at 12 % Industry 4.0.

Retail and e-commerce. Ahead in China (JD, Cainiao, Meituan, 永辉 site selection) and Russia (Ozon, X5), all in-house. In the English baseline mid-size retailers shop among suites and find them overkill. Nothing user-side in German, Dutch, French or Spanish; Mexico's retail logistics voices were behind 403 pages. Banking and retail lead Gulf AI adoption per McKinsey, but nothing on optimisation specifically.

Healthcare. The vertical with the clearest problem and the weakest use: Dutch rosters made by hand almost everywhere, NHS e-rostering mandated by the Carter Review but "immature", Québec nurses given a blunt weekend rule, Spanish nurses who built their own app in 2014. Ahead only in hospitals with capacity-management teams (Gelre, Radboudumc, Twente's CHOIR partners). The pull is acute: staff shortage, 200+ organisations contacting a Dutch student, Spanish public tenders (EUR 578,512 Defence hospital shift system; EUR 507,387 Galicia demand system). Operating-theatre scheduling was never searched (German lane).

Energy and utilities. Ahead in France (EDF, RTE, GRTgaz, Lhyfe with an in-house OR team of five sitting "dans les mêmes bureaux" as field teams) and in Chinese power-dispatch solver marketing (ORSC ASA2026). No buyer-side voice in German, Dutch, Spanish, Slavic or English sources; Stadtwerke dispatch was never searched.

Public sector. Rijkswaterstaat's road LP had to be rebuilt yearly "zodanig dat deze beter is te volgen" (1994); Hajj scheduling worked until hand-over; the DGA cutting-stock case needed explainability "pour que les chefs s'en emparent"; inspector rostering was "shot down" by unions. The Gulf's state push (SIRI, ITTI, NIDLP) is public-sector will applied to factories, with consulting as the documented default and no user voice.

Mining and agriculture. Chilean gran minería has run fleet dispatch for decades; medium miners are presumed not to (vendor deck); the whole sector is stuck at scaling pilots. OCP is the one honest implementation story in the Arabic lane. Peruvian agro-export has a growing, optimisation-shaped problem (harvest, cold chain, port slots) with planning effectiveness around 65 % (secondary citation) and weak connectivity. Nothing in any other lane.

Services: rail, airlines, telecom. The mature vertical everywhere it was looked at: DB, ÖBB, SNCF, NS, Flix, Amadeus, Orange, US airlines, China Southern via a research institute. Scale plus explicitly combinatorial problems (timetables, crews, yield) plus a culture of head-to-head validation. It is also where acceptance failures are loudest (NS 2001, De Lijn 2024, American Airlines' first attempt).

6. By location: readiness signals

Matrix view: Region × pull and readiness

The region-by-region readiness table that stood here is now matrix 4, Region × pull and readiness in section 2, one row per region with the same adoption proxies, the dominant bottleneck, the three kinds of pull (labour shortage, regulation or market, subsidy or state), the buyer default, the user voice found and the verdict. "User voice" says how much first-person buyer or planner material the lane found: rich, thin or silent. Numbers are the proxies the lanes could open; none measures OR adoption directly. The ranked answer below is unchanged.

Is there a market much more ready for adoption, with clear signals?

Ranked by how much evidence the lanes actually found, not by how large the opportunity might be.

1. Scheduling-heavy service incumbents, everywhere: rail, airlines, energy. The evidence is organisational: named in-house teams (ÖBB's three yield specialists, GRTgaz's eight, Lhyfe's five, DB across five divisions), prototypes industrialised (SNCF OPTIPLACE 2024), prize lists (GOR, Prix Indus'RO) and a validation culture (Southwest's year-long contests). These buyers already have awareness, data and will; what they buy is specific products, academic partnerships and people. This is the readiest market and the one where the buyer does the integration.

2. Czech large manufacturers and the SME gap behind them. The only region with a recent, independent, size-split measurement: 68 % vs 18 % on routing analytics, 84 % vs 43 % on inventory, with SMEs stating higher intent than large firms and OP TAK subsidies explicitly for non-production digital tools. The practitioner literature (17 years in IT Systems) is honest about the data stall. Evidence: NCP 4.0 2023 PDF.

3. Road logistics under dispatcher and driver shortage, Germany first. 53 % of 502 German logistics firms lack dispatchers, 73 % lack know-how, 51 % already use some AI and 55 % see it as the answer to the shortage; the route-planning product market exists at 5k to 20k EUR a seat. The person who would resist the tool is the one missing. Spain has the same shortage (20,000 to 30,000 drivers) but its industry asks for immigration, so the signal is latent. The Nordic CO2-in-tenders pull is five people in one Reddit thread, two of them vendors: a lead, not a measurement.

4. Healthcare rostering under staff shortage: Netherlands and Spain's public tenders. 200+ organisations contacted one Dutch student; Regioroster is funded on the hypothesis that preference rosters recover 15 to 20 % hours; Spain's Defence hospital (EUR 578,512) and Galicia (EUR 507,387, bids due 2026-10-26) are tendering planning systems. But this is also where acceptance failures are loudest (NS, De Lijn, Québec) and where the Dutch lane says software is bought and "nauwelijks (goed) gebruikt". Ready to buy; not ready to use without an in-house capacity team.

5. Chinese platform logistics and head manufacturers. Adoption is highest here but it is in-house and domestic (信创 solver push, 杉数 project deals in millions of RMB). For an outside seller this is evidence of readiness, not of a market.

6. Chilean large mining. Budget and awareness are present (60 % already report gains, decades of fleet dispatch) and the bottleneck is scaling across sites; medium miners are "presumed" not to have fleet systems (vendor deck). Consulting-heavy, association-documented, no user voice.

7. The Gulf, on paper. State money and assessment pipelines (479 and 620+ factories with a diagnosed gap and a roadmap) are real and measurable, and consulting is the documented buying default. But the lane found not one planner describing a scheduling or routing tool in use, in either language, and routing is blocked at the address layer. This is will without evidence of use.

Where readiness evidence is absent, and that is the finding: Poland (no user voice, one number from 2016), Spanish-language forums (nothing), Mexico (every practitioner page returned 403), Egypt (403), South Slavic (one query, vendor pages), Taiwan (thesis host unreachable), India, Ireland and Australia (nothing OR-specific in English), Flemish and French discrete manufacturing (surveys of preconditions only), and small French and Québec manufacturers, where the barometer itself says low adoption reflects low interest rather than barriers. No lane found a market where small or mid-sized buyers are asking for optimisation in their own words.

7. Where buyers can be heard

Matrix view: Region × pull and readiness, user-voice column

Places where planners, in-house OR people and honest practitioners talk, by region, with dates where the lanes found them. Forums are rare outside Russian, Chinese and English; the European OR societies' practice sessions and magazines are the substitute.

Region	Venue	What it is, when	URL
Germany, Austria, Switzerland	GOR AG "Praxis der Mathematischen Optimierung" (PMO)	Numbered practitioner workshops hosted by companies, about two a year: 107th at ÖBB Vienna 2023-09-28/29; 111th Munich 2026-11-26/27 "Optimization in the era of Generative AI"; 112th Ingolstadt 2027-03-11/12 "Shaping the Mobility of the Future"; one listed Bad Honnef 2027-12-02/03. The German lane calls it the single best place to hear buyers and in-house OR people.	gor-ev.de · PMO 107 PDF
	GOR, OR News, Unternehmenspreis, OR 2027	About 1,050 members (the information page also says 1,100 and, in an older section, 1,300); 17 working groups; OR News three times a year; OR 2027 Linz 2027-08-30 to 09-02 with ÖGOR and SVOR; biennial company prize	gor-ev.de · prize page
	ÖGOR, SVOR/ASRO	ÖGOR annual meeting each September, working groups on health care and on theory and practice of optimisation; SVOR Swiss OR Days each June (Bern 2026-06-18/19), Bulletin, thesis prize; nothing on company adoption on the Swiss site	oegor.wordpress.com · svorasro.wordpress.com
	woodworker.de; Trovarit "ERP in der Praxis"; Bitkom logistics survey	Real tradespeople on cutting optimisers (2009 to 2026); biennial ERP user satisfaction study (1,538 ratings 2026/27); logistics survey series (2019: 514 firms, 2026: 502) presented at IAA Transportation	woodworker.de 2009 · Trovarit press text · bitkom.org
Netherlands and Flanders	STAtOR (VVSOR magazine)	3 to 4 issues a year, PDFs free; regular practitioner column by John Poppelaars; the richest Dutch source of honest practitioner writing on this question (Decroix et al. Dec 2024, Hamster and Poppelaars July 2026)	vvsor.nl archive
	NGB / VVSOR OR section; LNMB; Dutch Day on Optimization; ORBEL; VVSOR annual meeting	LNMB/NGB conference 12 to 14 January 2026 Soesterberg (third day on generative AI and OR, networking lunch with industry); Dutch Day on Optimization 12 November 2026 Groningen; ORBEL 41 Ghent 2 to 3 February 2027 (awards sponsored by OMP and N-SIDE, both vendors); VVSOR annual meeting 8 April 2027	vvsor.nl · lnmb.nl · orbel.be
	TKI Dinalog; VIL; CHOIR (Twente); Supply Chain Magazine NL	Dinalog: 250+ projects, publications hub on digitalisation and AI, MIT vouchers until 2026-12-31; VIL member projects; CHOIR "in practice" symposia; S&OP Flavour Day 18 November	dinalog.nl · vil.be · utwente.nl · supplychainmagazine.nl
France and Québec	ROADEF: congress, Prix Indus'RO, Bulletin	ROADEF 2026 Tours 24 to 26 February 2026 with a "Retours d'expérience industrielle" session (Renault, RTE, EDF) and a sponsor session; Prix Indus'RO each June at CNAM Paris, audience vote among three deployed projects; Bulletin one to two issues a year with "repORtage" on company OR teams; an informal group of company OR managers meets about twice a year (no URL in the source)	roadef2026 · roadef.org · bulletin page
	Journées de l'optimisation (GERAD/CIRRELT); trade press	JOPT 2026 at HEC Montréal 11 to 13 May 2026 (Hexaly, GIRO, IVADO Labs logos); Le Journal de la Supply Chain, myRHline for workforce planning, Supply Chain Magazine (paywalled); no active French-language planner forum found	gerad.ca · lejournaldelasupplychain.fr · myrhline.com
Spain and Latin America	SEIO (BEIO, working groups); SMIO; ICHIO; SADIO/JAIIO; ALIO	SEIO bulletin has an "Opiniones sobre la profesión" category and working groups on scheduling, transport, software and location; SMIO annual CSMIO congress and EPIO school; ICHIO OPTIMA biennial (2025: 145 talks) and three coloquios a year; JAIIO's industrial informatics symposium; ALIO's domain redirected to a GoDaddy parking page on 2026-10-16	seio.es · smio.org · ichio.cl · sadio.org.ar · alio.org
	Trade press and forums	Cadena de Suministro, Logística Profesional (Spain); Revista Logistec, MundoMaritimo, REDIMIN, CESCO (Chile); Forocoches threads on industrial engineering (Spain); Reddit in Spanish yielded nothing	cadenadesuministro.es · cesco.cl · forocoches.com
Poland	Meetup Produkcyjny (Nexelem); Warsaw Industry Week; trade press	Plant-hosted meetups for production managers (Sanok Rubber 2026-03-27, next September 2026); Smart Factory World Katowice 2026-09-29; Warsaw Industry Week 2026-11-03 to 05; log24.pl, logistyka.net.pl, itwiz.pl; PTBOiS is academic (conference page not opened)	nexelem.com
Czechia and Slovakia	IT Systems "Plánování a řízení výroby" (systemonline.cz); NCP 4.0; Svaz průmyslu a dopravy	The one Czech venue where APS practitioners and vendors argue in public since 2008; NCP 4.0 annual "Analýza českého průmyslu" and National Industrial Summit; SP ČR "Cena za Průmysl 4.0" and "Rozjeď AI ve výrobě" 2025-06-26	systemonline.cz · ncp40.cz · spcr.cz
Russian-speaking	Habr and qna.habr.com; vc.ru; Telegram; selunion.ru events	Habr hubs and company blogs (Yandex, Ozon Tech, Dodo, X5, K2Tech), comment pages are the richest user voices; vc.ru "Сервисы" and "Транспорт" for first-person SMB accounts; Telegram "{ PRO Планирование \| IBP }" (697 subscribers); "Логистика 360" series (Vladivostok 2026-06-04, Minsk 2026-09-24), "Operational Efficiency" conference 2026-10-07	habr.com · vc.ru · t.me · selunion.ru
Gulf	IEOM GCC conference; SCLG Dubai; Saudi Logistics Academy	IEOM GCC 3rd edition Tabuk 2 to 4 February 2026 (academic-heavy); SCLG membership body of 200+ corporates (own site not opened); no Saudi, Emirati or Egyptian OR society confirmed; no Arabic planner community found; Reddit empty on the topic	ieomsociety.org
China	运筹OR帷幄; ORSC divisions; 数据魔术师 / 思谋网; DataFun; e-works; Zhihu and CSDN threads	运筹OR帷幄: 44,070 Zhihu followers, 2,614 articles, 40+ WeChat groups, monthly meetups; ORSC 企业运筹学分会 19th annual meeting 2026-10-23 to 25 Xi'an with site visits; ASA2026 Fuzhou 2026-04-17 to 19; 数据魔术师 workshop (2023 Wuhan); DataFunSummit OR forum; e-works APS conference (2026 page not opened); Zhihu APS questions where vendors, planners and consultants argue	zhihu.com column · conf.orsc.org.cn · scmor.com · DataFunSummit · Zhihu APS question
English	OR Stack Exchange; r/OperationsResearch, r/supplychain, r/logistics; Hacker News; EURO Practitioners' Forum; INFORMS; The OR Society	The 2019 OR SE "reasons OR industry projects fail" thread is the densest practitioner source found; r/OperationsResearch about 5k members; r/logistics is vendor-infiltrated; EURO Practitioners' Forum 6th conference April 2026 Warsaw (Broihan talk "Confessions of an OR Consultant"); INFORMS Edelman Award; OR Society Impact magazine (index only)	or.stackexchange.com · r/supplychain · news.ycombinator.com · gams.com

8. Sources

Matrix view: Region × pull and readiness, adoption column

Every URL on this page, grouped by region, one line each. All accessed 2026-10-16 by the regional lane named, unless marked "not opened" or "404". Vendor sources keep their mark.

Germany, Austria, Switzerland

bitkom.org : Bitkom press release 2026-09-10, 502 logistics firms, skills shortage and obstacles. Accessed 2026-10-16.
repositum.tuwien.at : Lewandowski, TU Wien dissertation 2011, PPS/MES in Austrian mid-sized manufacturers, 22 interviews plus 200+ surveyed. PDF extracted locally. Accessed 2026-10-16.
isi.fraunhofer.de : Fraunhofer ISI PI 71, 2015 manufacturing survey, PPS software 67 %. PDF extracted locally. Accessed 2026-10-16.
logistik-heute.de : Logistik Heute 2024-04-18 on IDC survey for Kinaxis (vendor-commissioned), 37 % Excel. Accessed 2026-10-16.
iml.fraunhofer.de : Fraunhofer IML press release 2024-04-09, warehouse staff-planning survey (Italian sample), Excel first. Accessed 2026-10-16.
kfw.de : KfW Digitalisierungsbericht Mittelstand 2025 (April 2026), spend and size effects. PDF extracted locally. Accessed 2026-10-16.
kfw.de : KfW Fokus Volkswirtschaft 554, 2026-07-29, AI in the Mittelstand, data analytics 3 %. PDF extracted locally. Accessed 2026-10-16.
deutscherpresseindex.de : Trovarit "ERP in der Praxis 2026/27" press text, 2026-09-21, 1,538 ratings. Accessed 2026-10-16.
boeckler.de : Böckler Impuls 2024/5 reporting WSI-Mitteilungen 1/2024 on algorithmic shift planning. PDF extracted locally. Accessed 2026-10-16.
gabler.de : Gabler Wirtschaftslexikon, OR entry by Marco Lübbecke, in-house OR groups dissolved; no revision date. Accessed 2026-10-16.
gor-ev.de : GOR information page, members, working groups, conferences. Accessed 2026-10-16.
gor-ev.de : GOR working groups including PMO sessions 111 and 112. Accessed 2026-10-16.
gor-ev.de : GOR company prize winners 2006 to 2026, DB and Flix texts. Accessed 2026-10-16.
or2025.de : GOR press release 2025-09-03, Minner lecture, 1,050 members. PDF extracted locally. Accessed 2026-10-16.
gor-ev.de : ÖGOR/GOR PMO 107 workshop at ÖBB 2023-09-28/29, programme, ÖBB OR team. PDF extracted locally. Accessed 2026-10-16.
oegor.wordpress.com : ÖGOR events archive. Accessed 2026-10-16.
svorasro.wordpress.com : SVOR/ASRO homepage, Swiss OR Days. Accessed 2026-10-16.
scs.fraunhofer.de : Fraunhofer SCS route-planning software market study 2015/2016 summary (vendor survey). PDF extracted locally. Accessed 2026-10-16.
skillbyte.de : skillbyte GmbH (consultancy), 2026-08-06, Excel planning boards. Accessed 2026-10-16.
reddit.com : r/de_EDV 2022-01-13, machining SME employee, ERP planning module unused; read via the local Reddit archive tool, post truncated at the end. Accessed 2026-10-16.
software-journal.de : INFORM FELIOS user survey, 29 experts, 2023-12-13 (vendor). Accessed 2026-10-16.
prologistik.com : proLogistik route-planning comparison, admissions on override (vendor). Accessed 2026-10-16.
tetys.de : tetys 2025-02-24, Excel still common, planners hard to find (vendor). Accessed 2026-10-16.
woodworker.de : forum thread 2009, cutting-optimiser users. Accessed 2026-10-16.
woodworker.de : forum thread 2026-07, tradespeople's objections to a new optimiser SaaS. Accessed 2026-10-16.
socialeap.de : Socialeap (online marketing agency) 2026-09-15, route planning guide for the Mittelstand, effort moves from dispatching to data maintenance. Accessed 2026-10-16.
news.it-matchmaker.com : IT-Matchmaker.news (Jürgen Frisch) quoting Dualis (vendor), foremen's knowledge not in APS. Accessed 2026-10-16.

Netherlands and Flanders

vvsor.nl : Telgen, "Besliskunde: beslissen met inzicht", Kwantitatieve Methoden, 1995 per file name. PDF extracted. Accessed 2026-10-16.
vvsor.nl : Timmer (ORTEC), VU inaugural lecture 1994, Rijkswaterstaat LP and route-planning packages. PDF extracted. Accessed 2026-10-16.
tno.nl : TNO "Eindrapport Talking Trucks" 2022 for SmartPort, Van Berkel field study. PDF extracted. Accessed 2026-10-16.
dinalog.nl : Dinalog news on Van Berkel and Squid (English, undated). Accessed 2026-10-16.
vvsor.nl : STAtOR 27(2) July 2026, Hamster on doctor rosters, Poppelaars on AI in OR. PDF extracted. Accessed 2026-10-16.
vvsor.nl : STAtOR 25(3/4) Dec 2024, Decroix et al. (Vlerick), 26 planner interviews. Pages rendered to images and read. Accessed 2026-10-16.
computable.nl : Computable 2018-11-02, evofenedex survey of 260 logistics managers, 6.1/10. Accessed 2026-10-16.
cbs.nl : CBS 2025-12-12, AI use by companies 2023 to 2025, logistics 4 %. Accessed 2026-10-16.
themis.vlaanderen.be : Vlaamse Regering memo May 2024, SME digital transformation programme. PDF extracted. Accessed 2026-10-16.
treinreiziger.nl : Treinreiziger 2015-03-30, retrospective on NS "Rondje om de kerk" 2001. Accessed 2026-10-16.
vrt.be : VRT 2024-10-11, De Lijn roster strike. Accessed 2026-10-16.
computable.nl : Computable 2012-08-06, VieCuri moves to ORTEC Harmony (vendor-adjacent; buyer's reasons only). Accessed 2026-10-16.
computable.nl : Computable 2013-02-06, Agrifirm Belgium replaces manual routing (vendor-adjacent; savings claims not used). Accessed 2026-10-16.
dinalog.nl : Regioroster project, Nov 2025 to Oct 2026. Accessed 2026-10-16.
dinalog.nl : TKI Dinalog home, 250+ projects, EUR 100 m+. Accessed 2026-10-16.
vil.be : VIL "Ready for AI(-agents) in Logistics", Q4 2026 to Q1 2028. Accessed 2026-10-16.
vvsor.nl : STAtOR archive. Accessed 2026-10-16.
vvsor.nl : NGB / VVSOR OR section. Accessed 2026-10-16.
lnmb.nl : LNMB home, event list. Accessed 2026-10-16.
orbel.be : ORBEL, Belgian OR society. Accessed 2026-10-16.
utwente.nl : CHOIR, Universiteit Twente. Accessed 2026-10-16.
supplychainmagazine.nl : S&OP Flavour Day event page. Accessed 2026-10-16.
repository.tudelft.nl : O. Binneveld, TU Delft MSc thesis 2017, Royal FloraHolland Aalsmeer order handling simulation. Accessed 2026-10-16.
dinalog.nl : AI-BIPTO project page, 2021-2024, proof of concept, no deployment reported. Accessed 2026-10-16.

France and Québec

roadef.org : ROADEF white paper 2011, 140 pp., Eurodécision client list. PDF extracted. Accessed 2026-10-16.
roadef.org : ROADEF white paper 2019, market structure and barriers. PDF extracted. Accessed 2026-10-16.
roadef.org : Bulletin ROADEF 45, Dec 2023, GRTgaz RICE, explicability, DGA case. Accessed 2026-10-16.
roadef.org : Bulletin ROADEF 46, Dec 2024, SNCF OPTIPLACE, Lhyfe. Accessed 2026-10-16.
roadef.org : Prix Indus'RO rules and finalists. Accessed 2026-10-16.
roadef.org : Bulletin index page. Accessed 2026-10-16.
roadef2026.sciencesconf.org : ROADEF 2026 programme, industrial session. Accessed 2026-10-16.
nextmv.io : Hexaly founder interview 2025-06-03 (vendor blog, vendor speaker, English). Accessed 2026-10-16.
entreprises.gouv.fr : DGE Théma 24, Nov 2024, ERP 60 %. PDF extracted. Accessed 2026-10-16.
info.gouv.fr : Deloitte evaluation of the Plan de transformation de l'industrie par le numérique, Dec 2023. PDF extracted. Accessed 2026-10-16.
wavestone.com : Wavestone / France Industrie barometer 2024 (consultancy survey, sample size not given). PDF extracted. Accessed 2026-10-16.
stiq.com : STIQ Baromètre industriel québécois 2021. PDF extracted. Accessed 2026-10-16.
cfib-fcei.ca : FCEI Automatisation Québec 2023. PDF extracted. Accessed 2026-10-16.
cfib-fcei.ca : FCEI / Investissement Québec, Oct 2025, n = 352. PDF extracted. Accessed 2026-10-16.
lesboitesavelo.org : Les Boîtes à Vélo, April 2025, about 40 operator interviews. PDF extracted. Accessed 2026-10-16.
decisionbrain.com : DecisionBrain 2026-05-08, "retour à Excel" and 65 % figure (vendor, conceding). Accessed 2026-10-16.
myrhline.com : myRHline 2025-03-04, workforce planning rollback to Excel. Accessed 2026-10-16.
lejournaldelasupplychain.fr : Le Journal de la Supply Chain 2026-06-15. Accessed 2026-10-16.
ciusssmcq.ca : CIUSSS MCQ notice 2023-02-14. Accessed 2026-10-16.
radio-canada.ca : Radio-Canada 2023-05-08. Accessed 2026-10-16.
blog.lean-supply-chain.fr : "S&OP, avant l'outil" (404 on 2026-10-16; wording known from a search snippet only).
symposia.gerad.ca : Journées de l'optimisation 2026. Accessed 2026-10-16.
findle.fr : Findle (APS consultancy) 2026-04-23, APS project scoping, data as first cause of schedule slip. Accessed 2026-10-16.
eurodecision.com : Eurodécision (consultancy) 2021, satire on how to sink an AI project. Accessed 2026-10-16.
woopit.fr : Woopit (vendor) 2023, interview with two OR developers. Accessed 2026-10-16.
decisionbrain.com : DecisionBrain (vendor) 2020, practitioner post on OR practice, testing on relevant data. Accessed 2026-10-16.

Spain and Latin America

seio.es : SEIO BEIO opinion piece 2024-07-23, author unnamed. Accessed 2026-10-16.
seio.es : SEIO working groups. Accessed 2026-10-16.
indesia.org : IndesIA barometer 2024 (industry association), INE reason table. PDF extracted. Accessed 2026-10-16.
indesia.org : IndesIA barometer 2026, manufacturing 0.9 %. Accessed 2026-10-16.
boe.es : BOE 2025-01-31, Activa Industria 4.0 budget cut. PDF extracted. Accessed 2026-10-16.
boe.es : BOE 2026-06-18, Defence hospital shift-planning tender. Accessed 2026-10-16.
galiciapress.es : Galiciapress 2026-09-29, Sergas demand-management tender. Accessed 2026-10-16.
trans.info : trans.info 2026-08-24, transport firm count and 30,000-driver deficit. Accessed 2026-10-16.
bolsamania.com : Europa Press 2026-01-09, UNO 20,000-driver deficit. Accessed 2026-10-16.
cadenadesuministro.es : Cadena de Suministro 2026-10-15, Eurobarometer AI in logistics SMEs. Accessed 2026-10-16.
cadenadesuministro.es : Cadena de Suministro 2026-10-15, foreign workers in transport 18.8 %. Accessed 2026-10-16.
cadenadesuministro.es : site identity. Accessed 2026-10-16.
forocoches.com : Forocoches thread 2014 to 2015, automotive logistics planner. Accessed 2026-10-16.
idom.com : IDOM APS page, Excel admission (vendor). Accessed 2026-10-16.
demandsolutionslatam.com : Demand Solutions Latam, planning expertise in heads (vendor, undated). Accessed 2026-10-16.
sadio.org.ar : Vidoni and Vecchietti, SII 2015 (JAIIO), APS in Argentine SMEs. PDF extracted. Accessed 2026-10-16.
amelica.org : Revista Mutis 2022, Industry 4.0 in Bogotá manufacturing SMEs, n = 100. Accessed 2026-10-16.
deloitte.com : Deloitte Chile and CESCO study page 2026 (full report behind a form). Accessed 2026-10-16.
redimin.cl : REDIMIN 2026-06-24 on the Deloitte/CESCO study. Accessed 2026-10-16.
forbes.cl : Forbes Chile 2026-04-15, Accenture quote on scaling. Accessed 2026-10-16.
cesco.cl : CESCO podcast summary 2026-08-07. Accessed 2026-10-16.
biobiochile.cl : BioBioChile 2025-07-10, Alta Ley supplier digital index. Accessed 2026-10-16.
sonami.cl : 4DIGITAL deck hosted by SONAMI, fleet optimisation in medium mining (vendor). PDF extracted. Accessed 2026-10-16.
mundomaritimo.cl : MundoMaritimo 2022-06-13, TPS Valparaíso container appointments. Accessed 2026-10-16.
laccei.org : LACCEI 2026 paper, Peru agro-export logistics (65 % figure secondary). PDF extracted. Accessed 2026-10-16.
icex.es : ICEX smart agro Peru 2025 summary. PDF extracted. Accessed 2026-10-16.
agrolatam.com : Agrolatam 2025-06-15, Peruvian agro-export growth and logistics cost. Accessed 2026-10-16.
blogdelcontador.com.ar : Argentina KIT 4.0 programme note 2026-04-27. Accessed 2026-10-16.
smio.org : SMIO site. Accessed 2026-10-16.
ichio.cl : ICHIO site. Accessed 2026-10-16.
alio.org : redirected to a GoDaddy parking page on 2026-10-16.
tramitapp.com : TramitApp (vendor), nurse rostering, time goes into redoing the roster. Accessed 2026-10-16.
decidesoluciones.es : Decide Soluciones (vendor) write-up of LogisTalks panel with Logistop, 2022-04-05, low adoption admitted. Accessed 2026-10-16.

Poland, Czechia and Slovakia, Russian-speaking space

benchmark.pl : BPSC survey of 350 Polish manufacturers 2016-04-07, APS 2 % (vendor survey, against interest). Accessed 2026-10-16.
money.pl : Siemens / Ministry "Smart Industry Polska 2017", ERP 39.8 %. Accessed 2026-10-16.
itwiz.pl : ITwiz 2025-08-18 on DPS Group report, ERP 30 %. Accessed 2026-10-16.
opolskie.pl : Opolskie regional survey, n = 150, 2025-11. PDF extracted. Accessed 2026-10-16.
kongresobywatelski.pl : Śledziewska and Włoch, Polish SMEs and Industry 4.0, 2023-12. PDF extracted. Accessed 2026-10-16.
nexelem.com : Polish production-management event calendar. Accessed 2026-10-16.
ciirc.cvut.cz : CIIRC summary of NCP 4.0 Analýza 1/2023, 207 directors. Accessed 2026-10-16.
ncp40.cz : NCP 4.0 Analýza českého průmyslu 1/2023, full PDF. PDF extracted. Accessed 2026-10-16.
ncp40.cz : NCP 4.0 editions list. Accessed 2026-10-16.
spcr.cz : Svaz průmyslu a dopravy, Industry 4.0 page. Accessed 2026-10-16.
adastracorp.com : Adastra workshop survey 2024-12-02, 71 % Excel (vendor, self-selected). Tavily extract. Accessed 2026-10-16.
systemonline.cz : Fárek (ITeuro, Infor partner), IT Systems 6/2025. Accessed 2026-10-16.
technickytydenik.cz : Figura (K2), Technický týdeník 2021-11-07. Accessed 2026-10-16.
systemonline.cz : Komenda (inSophy), IT Systems 9/2014. Accessed 2026-10-16.
systemonline.cz : Komenda (inSophy), IT Systems 7-8/2013. Accessed 2026-10-16.
systemonline.cz : Plaček (Merz), IT Systems 7-8/2008, user view, TRW Automotive case. Accessed 2026-10-16.
issek.hse.ru : HSE ISSEK, digital transformation of machine building, 2026-03-25, 1,700+ firms. PDF extracted. Accessed 2026-10-16.
expert.ru : Expert.ru 2025-07-13, Reksoft estimates, state as driver. Accessed 2026-10-16.
isicad.ru : Samoylov (ASCON-Samara, Preactor integrator), 2019-03-12, Russian APS adoption by industry (vendor-side). Accessed 2026-10-16.
up-pro.ru : BIA Technologies 2023-12-20, APS market after 2022 (sponsored, vendor). Accessed 2026-10-16.
habr.com : Rightstep (consultancy), why APS does not make the plan, 2019-10-25. Accessed 2026-10-16.
habr.com : comment thread under the above. Accessed 2026-10-16.
habr.com : Rightstep, "Недовнедренная ERP в производстве", 2019-10-05. Accessed 2026-10-16.
habr.com : "rebuilder", in-house routing at a wholesale distributor, 2019-01-23. Accessed 2026-10-16.
habr.com : comment thread, driver pay and trackers. Accessed 2026-10-16.
vc.ru : Prozorov, Yandex Routing vs Relog at a parts distributor, 2023-06-26. Accessed 2026-10-16.
rustore.ru : Yandex Routing driver app, rating 2.0 from 55, reviews Aug to Oct 2026. Accessed 2026-10-16.
habr.com : Karpov, MES at a steel radiator plant, 2019-11-20. Accessed 2026-10-16.
habr.com : CS Group / Dassault, DELMIA Ortems load test, 2021-07-26 (vendor). Accessed 2026-10-16.
habr.com : Lozhkins, driver rostering for LTL routes, 20 January, year not shown. Accessed 2026-10-16.
education.ua : Nikushin (FADO, Ukraine), route optimisation payback, 2019-03-22, in Russian. Accessed 2026-10-16.
habr.com : Dodo Engineering, VRP with OR-Tools, 2025-04-29. Accessed 2026-10-16.
habr.com : X5 Tech, retail optimisation on synthetic data, 2022-08-31. Accessed 2026-10-16.
t.me : Telegram "{ PRO Планирование | IBP }", 697 subscribers. Accessed 2026-10-16.
selunion.ru : National Union of Transport and Logistics Experts, event list. Accessed 2026-10-16.
retail.ru : Ozon staffing algorithms, company release 2026-05-07, no baseline. Accessed 2026-10-16.
vc.ru : "Natalia", digitalisation consultant, rules before software, 9 October (year not shown). Accessed 2026-10-16.

Gulf, Egypt, Morocco

ieomsociety.org : Alkhalefah (KSU), IEOM GCC 2026, Saudi manufacturing survey n = 150. Accessed 2026-10-16.
ieomsociety.org : IEOM GCC proceedings index. Accessed 2026-10-16.
thenationalnews.com : The National 2025-11-13 on McKinsey GCC AI survey, n = 139. Accessed 2026-10-16.
thenationalnews.com : The National 2026-05-04, Al Nowais interview, 620 factories, 40 % claim. Accessed 2026-10-16.
swp-berlin.org : SWP Comment 2023, Ansari and Werenfels, consulting spend. Accessed 2026-10-16.
theartnewspaper.com : The Art Newspaper 2025-12-09, consultancy dependence quotes. Accessed 2026-10-16.
gerad.ca : Er Raqabi et al., OCP supply chain for Africa, GERAD 2023, with OCP co-authors. Accessed 2026-10-16.
ideas.repec.org : abstract of Becker, Bertsimas et al., Interfaces 52(1) 2022, OCP. Accessed 2026-10-16.
financialafrik.com : Financial Afrik 2021-01-18, OCP Edelman finalist. Accessed 2026-10-16.
iaorifors.com : IFORS abstract of Helbing et al., Interfaces 46(1) 2016, Hajj. Accessed 2026-10-16.
weforum.org : WEF 2023, Saudi SME modernisation, NPP and KAMIN. Accessed 2026-10-16.
saudipedia.com : Future Factories Program. Accessed 2026-10-16.
aawsat.com : Asharq Al-Awsat 2024-07-25, 479 SIRI assessments. Accessed 2026-10-16.
moiat.gov.ae : MoIAT 2024-06-13, ITTI 400+ companies. Accessed 2026-10-16.
middleeastainews.com : Middle East AI News 2026-06-14 on GASTAT ICT survey 2025 (AI-assisted newsletter). Accessed 2026-10-16.
vision2030.ai : third-party NIDLP tracker, "indicative" figures. Accessed 2026-10-16.
rigzone.com : Rigzone Saudi job listings (Aramco LP posting itself not opened). Accessed 2026-10-16.
joc.com : JOC 2011-04-04, DP World Jebel Ali rules-based berth planning. Accessed 2026-10-16.
raqi.com : Raqi Technologies, Arabic guide for Saudi merchants, addresses. Accessed 2026-10-16.
clarionshipping.com : Clarion Shipping (Dubai 3PL) blog. Accessed 2026-10-16.
softwaredisruption.com : Software Disruption FZCO, Dubai (vendor). Accessed 2026-10-16.
alwatan.com.sa : Al-Watan column "بحوث العمليات", 2022-07-24. Accessed 2026-10-16.

China

cygnusequity.com : 高鹄资本 intelligent APS note, 2022-09 (investment bank, own survey). Accessed 2026-10-16.
paper.people.com.cn : 中国城市报 2026-04-06, 86.92 % figure, subsidies on hardware. Accessed 2026-10-16.
rmlt.com.cn : 学习时报 essay 2024-03-18, RMB 1.5 m average project, pilot cities. Accessed 2026-10-16.
e-works.net.cn : e-works search page with summaries of its 2026 APS series and roundtable (articles not opened). Accessed 2026-10-16.
blog.csdn.net : OR engineer's three employers, 2022-08-14, team-size list. Accessed 2026-10-16.
developer.jdcloud.com : JD Retail supply-chain planning algorithms 2025-05-12 (in-house, self-reported). Accessed 2026-10-16.
sohu.com : 张鑫航 (Cainiao), OR in practice, DataFun 2023-11-18. Accessed 2026-10-16.
scm-blog.com : 刘宝红 on the status of planning, 2026-03-30. Accessed 2026-10-16.
cloud.tencent.com : "APS为什么成功率低？" 2020-07-22, anonymous APS veteran via 数据魔术师. Accessed 2026-10-16.
zhihu.com : 王源 talk write-up, 运筹OR帷幄, edited 2026-01-29. Accessed 2026-10-16.
news.qq.com : 蒙绎泽 (清智优化) ArchSummit transcript 2024-08-29 (vendor, admits). Accessed 2026-10-16.
bbs.csdn.net : CSDN bbs, three kinds of Excel-bound planners (implementer, undated). Accessed 2026-10-16.
bbs.csdn.net : CSDN bbs, APS selection and three break points (implementer, undated). Accessed 2026-10-16.
zhihu.com : Zhihu question on APS becoming "高级Excel"; 悠桦林 and 亿澳斯 answers (vendor). Accessed 2026-10-16.
zhihu.com : Zhihu question on APS failure rate; 悠桦林 answer with client list and unsourced figures (vendor). Accessed 2026-10-16.
zhihu.com : Zhihu question on why APS is hard to land; Lu Jie answer. Accessed 2026-10-16.
zhihu.com : 老贱 column 2026-03-26 (promotes 简道云, partly vendor). Accessed 2026-10-16.
qt-asia.com : 科箭 TMS, route optimisation landing, 2026-09-11 (vendor, admits). Accessed 2026-10-16.
smartcomma.com : 逗号科技 routing case with footnote (vendor, against interest). Accessed 2026-10-16.
goodo-tech.com : 谷斗科技 on landing difficulties 2025-08-29 (vendor, admits). Accessed 2026-10-16.
zhihu.com : 张成义 OR engineer career column (2019, edited 2022), comments. Accessed 2026-10-16.
ifenxi.com : 爱分析 interview with 杉数王曦 (undated, about 2018 to 2019). Accessed 2026-10-16.
andafa.com : 安达发 APS FAQ, duration and price (vendor). Accessed 2026-10-16.
sohu.com : 章月洲 "一个失败的APS案例" 2021-11-11 (APS salesman on a competitor's failure). Accessed 2026-10-16.
36kr.com : 邬爱其, 复旦《管理视野》 "200家工厂" 2025-11-19 (academic essay). Accessed 2026-10-16.
ndrc.gov.cn : NDRC reprint 2021-07-28, 46.5 % of 955 Jiangsu manufacturers report hiring difficulty. Accessed 2026-10-16.
sugli.sjtu.edu.cn : SJTU, ORSC 运筹应用奖 2020, 上汽安吉物流 system usage above 90 %. Accessed 2026-10-16.
orsc.org.cn : ORSC ASA2026 conference report 2026-04-24, domestic solvers. Accessed 2026-10-16.
conf.orsc.org.cn : ORSC 企业运筹学分会 19th annual meeting notice, 2026-10-23 to 25. Accessed 2026-10-16.
zhihu.com : 运筹OR帷幄 column. Accessed 2026-10-16.
scmor.com : 2023 Wuhan 智能优化应用实践研讨会 programme. Accessed 2026-10-16.
zhihu.com : DataFunSummit 2023 OR forum agenda. Accessed 2026-10-16.
zhihu.com : 王磊 (Alibaba Cloud), DataFunTalk 2023-09-26, port scheduling landing difficulties (vendor, in-house hybrid, admits problems). Accessed 2026-10-16.

English baseline

or.stackexchange.com : OR Stack Exchange 2019, nine practitioner answers (via API). Accessed 2026-10-16.
reddit.com : r/OperationsResearch 2024-09-16, 43 archived comments. Accessed 2026-10-16.
reddit.com : r/OperationsResearch 2025-07-19, in-house OR team vs IT. Accessed 2026-10-16.
reddit.com : r/OperationsResearch 2026-01, sports scheduling, Timefold founder reply. Accessed 2026-10-16.
reddit.com : r/supplychain 2026-09-15, 50 archived comments on APS and AI. Accessed 2026-10-16.
reddit.com : r/supplychain 2025-08-29, inventory optimisation platforms. Accessed 2026-10-16.
reddit.com : r/supplychain 2025-07-12, suites "overkill". Accessed 2026-10-16.
reddit.com : r/logistics 2026-06-06, route optimisation ROI and adoption, 32 archived comments, vendors disclosed. Accessed 2026-10-16.
news.ycombinator.com : Ask HN 2025-01-29, "Is Operations Research still a thing?" (via Algolia API). Accessed 2026-10-16.
nextmv.io : Nextmv (vendor blog) interview with Doug Gray (Walmart) and Karla Hoffman. Accessed 2026-10-16.
timefold.ai : Timefold (vendor), De Smet, failure modes. Accessed 2026-10-16.
decisionbrain.com : DecisionBrain (vendor) statistics compilation citing Deloitte 2024 and 2025 (originals not opened). Accessed 2026-10-16.
chalmers.se : Kjellsdotter Ivert and Jonsson 2011, IJPDLM, APS onward-phase problems. Accessed 2026-10-16.
shura.shu.ac.uk : Drake 2017, Health Informatics Journal, NHS e-rostering policies. Accessed 2026-10-16.
supplychainbrain.com : SupplyChainBrain 2003-11-01, AMR shelfware survey of 132 companies. Accessed 2026-10-16.
genpact.com : Genpact (consultancy) 2021, planner overrides. Accessed 2026-10-16.
mckinsey.com : McKinsey (consultancy), APS survey n = 80, Nov 2025, 65 % miss ROI. Via Tavily extract. Accessed 2026-10-16.
linkedin.com : Dave Medd post, McKinsey 10 % figure second-hand. Accessed 2026-10-16.
linkedin.com : Lora Cecere article 2025-12-24, satisfaction numbers, n = 211. Accessed 2026-10-16.
supplychainshaman.com : Lora Cecere blog 2025-12-05, 92 % own ERP/APS, spreadsheets. Accessed 2026-10-16.
abcsupplychain.com : AbcSupplyChain survey 2025-11-25, 164 S&OP professionals, 81.1 % Excel (author sells training). Accessed 2026-10-16.
gams.com : EURO Practitioners' Forum 2026 programme, Broihan abstract. Accessed 2026-10-16.
youtube.com : Microsoft 2026 Edelman presentation; transcript read from a search snippet only (Tavily), the page was not opened.

9. Quotes

The quote blocks moved out of sections 1 and 3, grouped by the subsection they came from and in the same order. Matrix cells and the "Read the quotes" links point here.

1.1 Problem awareness: the buyer cannot name the problem

"¿Y eso qué es?" ... "la mayoría de las empresas siguen sin aplicar estos resultados."

"And what is that?" is what potential clients asked the author when introduced as an OR person; "most companies still do not apply these results."

Spain, manufacturing, scheduling · SEIO BEIO opinion piece, 2024-07-23, author unnamed · seio.es

"Primárním nebezpečím je skutečnost, že zadavatel neví, co vlastně výrobní plánování v kontextu jeho firmy je."

"The primary danger is that the client does not know what production planning actually is in the context of his firm."

Czechia, discrete manufacturing, APS · Ondřej Komenda (inSophy, planning consultant), IT Systems 7-8/2013, diacritics restored · systemonline.cz

"甲方表述不清晰：业务人员本身可能对自己的需求认识并不完全清晰。他们知道现状有问题，知道希望改进，但要精确描述问题的本质、期望效果、约束条件，往往超出了业务人员的能力范围。"

"The client cannot articulate it: business staff may not fully understand their own need. They know the status quo has problems and want improvement, but precisely describing the essence of the problem, the desired effect and the constraints is usually beyond them."

China, airlines and manufacturing, rostering and general planning · 王源 (Shenzhen big-data institute), talk 2025-12-31 written up by 运筹OR帷幄 · zhihu.com

"het management is niet geïnteresseerd in de te gebruiken techniek, maar in het feit of het probleem op een voor hen bevredigende wijze opgelost kan worden." ... "als er al een OR-specialist is ingeschakeld, blijkt veelal dat zijn adviezen of oplossingen niet gebruikt worden."

"Management is not interested in the technique to be used, but in whether the problem can be solved in a way that satisfies them." ... "Where an OR specialist is engaged at all, it usually turns out that his advice or solutions are not used."

Netherlands, cross-sector · J. Telgen (Coopers & Lybrand, then chair of the Dutch OR society), Kwantitatieve Methoden, 1995 per file name · vvsor.nl PDF

"ويتم حل هذه المشاكل في أغلب الأحيان استنادًا إلى التقدير الشخصي والخبرة العملية"

"These problems are mostly solved based on personal judgment and practical experience." The column speaks of OR in the potential tense, as something that could be used.

Saudi Arabia, cross-sector · Salman Al-Harthi, Al-Watan, 2022-07-24, the only Arabic newspaper piece on OR the search found · alwatan.com.sa

"'Operations Research' is an anti-marketing term. To a layman it sounds non-descriptive, old fashioned and kinda boring. To a business stakeholder it sounds almost as confusing, research and not solution-oriented, and unlike AI, just not very exciting."

English baseline · u/KR4FE, r/OperationsResearch, 2024-09-16 · reddit.com

1.2 Will and budget: know-how and time are named before money

"73 Prozent nennen einen Mangel an Fachkräften beziehungsweise Knowhow als größtes Hemmnis" ... "Fehlende marktfähige Lösungen werden von 55 Prozent genannt, fehlende qualifizierte externe Beratung von 54 Prozent"

73 % name a lack of skilled staff or know-how as the biggest obstacle; 55 % a lack of market-ready solutions, 54 % a lack of qualified external consulting; IT security 58 %, lack of time in daily business 57 %, data availability 52 %, money last at 48 %.

Germany, logistics, 502 firms · Bitkom survey, 2026-09-10 · bitkom.org

"se comprueba que la previsión inicial para la iniciativa «Activa Industria 4.0» ha sido notablemente superior a la realidad" ... "de 24.420.000 € y una previsión de 3.300 pymes beneficiarias a una cuantía total máxima de 14.800.000 € y una previsión de 2.000 pymes beneficiarias."

"The initial forecast for Activa Industria 4.0 was notably above reality": free, subsidised Industry 4.0 consulting for industrial SMEs was cut from EUR 24.42 M and 3,300 SMEs to EUR 14.8 M and 2,000 SMEs because demand fell about 40 % short.

Spain, manufacturing SMEs · BOE, 2025-01-31 · boe.es PDF

"越需要转型的企业，越没有能力转型" ... "需要20年才能回本" ... "大部分企业选择了另一条路：精打细算，在管理上抠效率"

"The firms that most need to transform are the least able to"; a firm above RMB 1 bn revenue needs at least RMB 50 million for full digitalisation, which at 2 to 3 % net margin "takes 20 years to pay back", so "most chose another path: squeeze efficiency out of management", including one plant's home-made scheduling system.

China, manufacturing SMEs, 200 factories · 邬爱其, 复旦《管理视野》 via 36Kr, 2025-11-19, management essay not a dataset · 36kr.com

"le manque de personnel qualifié (79 %) et le manque de temps (75 %)" ... "la difficulté à évaluer le retour sur investissement (48 %)"

Québec manufacturers name lack of qualified staff (79 %) and lack of time (75 %) first, difficulty evaluating ROI (48 %), and financing least (33 %). The same barometer finds the least digital firms see the fewest obstacles: low adoption "s'expliquerait davantage par un niveau d'intérêt moins élevé" (is explained more by low interest than by barriers).

Québec, manufacturing, about 500 SMEs · STIQ Baromètre industriel québécois 2021 · stiq.com PDF

"když investují několik milionů do pořízení a implementace APS, problém s efektivitou a plněním termínů se vyřeší" ... "firmě vzroste obrat o několik desítek procent, zisk ale nevzroste prakticky vůbec nebo dokonce klesá"

Firms are told that "if they invest a few million in buying and implementing APS, the efficiency and deadline problem is solved", while in practice "turnover rises by tens of percent but profit does not rise at all or even falls."

Czechia, discrete manufacturing, APS (vendor, against interest) · Komenda (inSophy, APS maker), IT Systems 9/2014 · systemonline.cz

1.3 Data: the first stall, in every language

"你去问一个干了很多年的物料计划员：'这个料正常的采购提前期是几天？'他会告诉你：'你说的是哪个供应商的？A家一般7天，B家要10天，C家时好时坏，得催。'... 这些信息，一条都没有写进Excel。" ... "数据清洗的工作量，通常占整个APS项目工作量的40%到60%。"

"Ask a veteran material planner 'what is the normal lead time for this part?' and he says 'which supplier? A is usually 7 days, B needs 10, C is hit and miss, you have to chase.' None of this is written in Excel." And from the sister article: "Data cleaning is usually 40 % to 60 % of an APS project."

China, discrete manufacturing, APS · two CSDN bbs implementation write-ups, undated · bbs.csdn.net (planners) · bbs.csdn.net (selection)

"Uit deze feedback gesprekken bleek dat de input data vaak relatief is. Een tijdsvenster kan voor een bepaalde klant op een bepaalde dag veel groter zijn dan de input data aangeeft. Sommige chauffeurs zijn bereid een uurtje over te werken, anderen juist niet. Deze flexibiliteit maakt een planning vaak sluitend."

"The feedback sessions showed that the input data is often relative. A time window for a given customer on a given day can be much wider than the data says. Some drivers are willing to work an hour extra, others are not. This flexibility is often what makes a plan close."

Netherlands, container trucking, routing · TNO Talking Trucks report for SmartPort, 2022, field study at Van Berkel Logistics · tno.nl PDF

"Plánování nestojí na algoritmech. Stojí na tom, co mu dáte jako vstup."

"Planning does not rest on algorithms. It rests on what you feed it." Failure starts with stale purchasing dates, unrecorded actual times and dead orders left in the system.

Czechia, discrete manufacturing, APS (vendor partner, against interest) · Robert Fárek (ITeuro, Infor partner), IT Systems 6/2025 · systemonline.cz

"Excel ist nicht das Problem, der fehlende Rückkanal ist es" ... "Der Plan ist damit keine Abbildung der Realität, sondern eine Abbildung dessen, was der Planer zuletzt wusste"

"Excel is not the problem, the missing back-channel is" ... "The plan is not a picture of reality but of what the planner last knew." The Austrian survey behind this: only 44 % of mid-sized manufacturers could capture machine feedback automatically.

Germany and Austria, discrete manufacturing, scheduling · skillbyte GmbH (consultancy), 2026-08-06 · skillbyte.de · TU Wien dissertation 2011 · repositum.tuwien.at PDF

"ich bin AN in einem KMU (Zerspanung), welches vor 8 Jahren ein ERP System eingeführt hat. Allein für sich wäre das System ja ausreichend, nur wird es aufgrund einer unzureichenden Einführung nur begrenzt genutzt (Erstellung Rechnungen, Lieferscheine, aber keine Produktionsplanung oder Lagerverwaltung). Es ist alles händisch nur noch schwer zu machen. Hauptgrund sind m.E. die fehlenden Stücklisten und Arbeitsgänge, welche ich aber nur begrenzt Pflegen kann, da ich kein Zerspaner-Hintergrun[d habe]"

"I work at a machining SME that introduced an ERP eight years ago. The system itself would be sufficient, but because of an inadequate introduction it is used only in part: invoices, delivery notes, but no production planning or stock management. Doing everything by hand is getting hard. The main reason in my view is the missing bills of material and routings, which I can maintain only to a limited extent because I have no machining background." Replies: "ohne Unterstützung aus höheren Ebenen [ist] das Projekt schon zum scheitern verurteilt" (without backing from higher up the project is doomed) and "Nur wenn die GF dahinter steht und Druck macht, funktioniert es" (it only works if top management stands behind it and pushes). The one unmediated German user voice on the data stall found in this run.

Germany, machining SME, production planning · u/Trash133 and replies, r/de_EDV, 2022-01-13, read through the local Reddit archive tool, post text truncated at the end in the archive · reddit.com

"تفتقر العناوين إلى الدقة والتنظيم المعتاد (مثل أسماء الشوارع وأرقام المباني)" ... "طلب تفاصيل إضافية أثناء إتمام الطلب، مثل أقرب معلم بارز"

"Addresses lack the usual precision and organisation, such as street names and building numbers"; merchants cope by "asking for extra details at checkout, such as the nearest landmark." Every Gulf logistics source, vendor and non-vendor, says routing is blocked at the geocode layer.

Saudi Arabia, e-commerce, last mile · Raqi (Saudi e-commerce platform guide for merchants) · raqi.com · same from a Dubai 3PL: clarionshipping.com

"around 65 percent of APS programs fail to achieve their expected return on investment. One of the top five reasons is poor data management." ... "it is quite common, for example, to find inconsistencies in lot-sizing data between production versions and the material master for the same products."

English baseline, cross-industry, APS (consultancy that sells APS implementation; number runs against the APS category) · McKinsey APS survey, n = 80 companies, November 2025 · mckinsey.com. The French lane found the same 65 % figure quoted by DecisionBrain (vendor) and could not locate the McKinsey source; the English lane opened it via Tavily extract.

1.4 Implementation and integration: the messy process around the solver

"de solver was en is nooit het echte probleem. Het knelpunt zit in het rommelige, menselijke proces eromheen, werken met vage eisen, half begrepen aannames, iteratief gepruts met formuleringen, en eindeloze validaties."

"The solver never was and is not the real problem. The bottleneck is the messy human process around it: working with vague requirements, half-understood assumptions, iterative fiddling with formulations, and endless validation."

Netherlands, cross-project · John Poppelaars (OR consultant, Doing the Math), STAtOR July 2026 · vvsor.nl PDF

"ERP里的'工序号'是10、20、30，APS里的'工序号'是1、2、3。集成的时候没做转换 ... 车间按ERP里的工序号执行，先做了30，再做10，最后做20，产品质量直接出问题。"

"ERP operation numbers were 10, 20, 30; APS used 1, 2, 3. No conversion was done ... the shop executed in ERP order: 30 first, then 10, then 20, and product quality failed."

China, discrete manufacturing, scheduling · CSDN bbs APS selection write-up · bbs.csdn.net

"При использовании системы Relog вы столкнетесь с массой багов и ошибок" ... "Созвоны абсолютно бесполезные, очень растянутые" ... "Внедрение, а именно видео-встречи по обучению стоят 17500"

"With Relog you will run into a mass of bugs and errors" ... "The calls are absolutely useless, very drawn out" ... "Implementation, meaning training video calls, costs 17,500 roubles." The 1C module covered only part of the data exchange and took two months of in-house work to extend.

Russia, B2B parts distribution with 5 vehicles, routing · Vladislav Prozorov, vc.ru, 2023-06-26, first-person buyer account · vc.ru

"Integration in bestehende IT-Systeme an erster Stelle, die von 67 % als sehr wichtig eingestuft wurde"

Integration into existing IT ranked first, 67 % "very important", among 29 APS users surveyed at a vendor user meeting (vendor). The independent Trovarit ERP user study (1,538 installations, 2026) puts interfaces and integration among the top two operating problems at about 19 %.

Germany, machine building, APS · INFORM FELIOS user survey, 2023-12-13 (vendor) · software-journal.de · Trovarit press text · deutscherpresseindex.de

"El principal desafío no está en la adopción inicial de tecnología, sino en su escalamiento." ... "depender de pilotos que no escalan"

"The main challenge is not the initial adoption of technology but scaling it"; the sector must stop "depending on pilots that do not scale." The Deloitte and CESCO study has 74 % of miners naming resistance to change as the main brake and 61 % a business-technology gap; a CESCO podcast adds that three-to-six-month pilots are too short and each site differs so pilots do not replicate.

Chile, mining, general · Pablo Camilleri (Accenture Chile) in Forbes Chile, 2026-04-15 · forbes.cl · REDIMIN on Deloitte/CESCO, 2026-06-24 · redimin.cl · cesco.cl

"We faced similar challenges with our IT department which caused us to be frequently at odds with them. They tried to stick us with the name 'shadow IT'." And from the OR Stack Exchange thread: a car-rental reservation system "considerably more efficient than the existing solution, but there was a lot of work involved in providing the necessary data inputs, and the IT department wasn't willing to support it."

English baseline, manufacturing and services · u/directnirvana, r/OperationsResearch, 2025-07-19 · reddit.com · G_B relaying Maria Antónia Carravilla (U. Porto), second-hand · or.stackexchange.com

1.5 Daily use and trust: parallel running, override, rollback to Excel

"Ook hier bleken sommige planners het heft in eigen handen te nemen en gingen zelf over tot (pogingen tot) validering. Dit deden zij, bijvoorbeeld, door in parallel in het oude systeem te blijven werken en te bekijken of de output al dan niet identiek bleek. Uiteraard verhoogde dit hun werklast aanzienlijk" ... "ik vertrouw de developer's manier van werken minder dan het systeem. Ze hebben oude bugs geherintroduceerd."

"Some planners took matters into their own hands and attempted their own validation, for instance by continuing to work in the old system in parallel and checking whether the output was identical. Of course this increased their workload considerably" ... "I trust the developer's way of working less than the system. They reintroduced old bugs." The authors' framing: money is pumped into software that is not used optimally, and end users unknowingly increase their own workload.

Belgium and multinationals, discrete manufacturing, APS · Decroix, Vereecke, Vanderheyden, De Baets (Vlerick), 26 planner interviews, STAtOR Dec 2024 · vvsor.nl PDF

"'系统是给老板看的，Excel才是我真正用来干活的。'" ... "所以很多企业最后的结局很统一：APS上线 → 计划员不用 → 回归Excel。然后老板得出一个结论，'这玩意是智商税'"

"'The system is for the boss to look at; Excel is what I actually work with.'" ... "So many firms end the same way: APS goes live, planners do not use it, back to Excel. Then the boss concludes 'this thing is an IQ tax'."

China, discrete manufacturing, APS (blogger who promotes a low-code tool, partly vendor) · 老贱, Zhihu column, 2026-03-26 · zhihu.com

"Lidé přestanou systému věřit. Vrátí se k Excelu." ... "Musí mu prostě věřit." ... "Když se pak od prvního dne nenaplní, postupně se vrátí zpátky z Excelu."

"People stop trusting the system. They go back to Excel." (Fárek 2025). "He simply has to trust it", because the planner cannot see why the APS decided what it did (Figura 2021). "When they are not met from day one, people gradually drift back to Excel."

Czechia, discrete manufacturing, APS (both vendor-side, against interest) · systemonline.cz (Fárek) · technickytydenik.cz (Figura, K2)

"plusieurs entreprises sondées estiment qu'elles font un meilleur travail d'optimisation que le logiciel" ... "les outils d'optimisation « ne font pas des bons livreurs »"

"Several firms surveyed believe they do a better optimisation job than the software" (which relies on OpenStreetMap, does not "see" relief, and lacks the trade's "jugeotte"); one operator and his client's operations manager share the impression that optimisation tools "do not make good couriers" and make them less able to handle surprises.

France, urban cyclologistics, last mile, about 40 operator interviews · Les Boîtes à Vélo study, April 2025 · lesboitesavelo.org PDF

"某物流企业在引入路径优化系统后，算法给出的路线确实短了8%，但客户投诉率反而上升30%。原因是算法没有考虑客户上午不收货、老旧小区大车无法进入、司机不熟悉路线等约束，导致计划好看但执行不了。"

"After one logistics firm introduced route optimisation, routes were indeed 8 % shorter, but complaints rose 30 %. The algorithm ignored customers who do not receive in the morning, old estates where big trucks cannot enter, drivers unfamiliar with routes: a plan that looks good but cannot be executed."

China, logistics, routing (vendor, against interest) · 科箭 TMS, 2026-09-11 · qt-asia.com

"Так за три года мы потеряли 9 устройств." ... "Знаю случаи когда такая экономия выходила боком."

Drivers destroyed tracking devices: "So in three years we lost 9 devices." A commenter: "I know cases where such savings backfired", because drivers' fuel "savings" were part of their pay; the author confirms the bonus sometimes reached half of pay. The Yandex Routing driver app is rated 2.0 from 55 ratings on RuStore (autumn 2026 reviews: "Логика построения маршрута, по большей части, отсутствует", route-building logic is for the most part absent).

Russia, wholesale distribution and last mile, routing · developer "rebuilder", Habr, 2019-01-23, and comments · habr.com · comments · rustore.ru

"Hier liegt oft das Problem: In vielen Fällen bleibt unklar, wie die im Hintergrund arbeitenden Algorithmen zu ihren Ergebnissen kommen. Selbst Führungskräfte haben oft keinen Einblick."

"That is often the problem: in many cases it stays unclear how the algorithms working in the background reach their results. Even managers often have no insight." Any algorithmic rostering in Germany touches BetrVG § 87 co-determination. A routing vendor's own comparison page admits dispatchers can "übersteuern" (override) proposals and "Das bestehende Optimierungspotenzial wird nicht immer vollständig ausgeschöpft" (vendor).

Germany, shift planning and routing · Böckler Impuls 2024 reporting WSI-Mitteilungen 1/2024 · boeckler.de PDF · proLogistik (vendor) · prologistik.com

"If dispatchers force a rigid route without local driver input, drivers just ignore the sequence or find workarounds. ... change management is where these projects usually stall." A vendor in the same thread: "Driver adoption is the quiet killer."

English baseline, logistics, routing · u/Revolutionary_Tax861 and u/Substantial-Drawer25 (Locus, vendor, disclosed), r/logistics, 2026 · reddit.com

1.6 Monitoring and evaluation: nobody can say what the plan was worth

"由于依赖人工经验，评价结果往往较为定性，缺乏客观性和量化标准。"

"Because it relies on human experience, evaluation tends to be qualitative and lacks objectivity and quantified standards." The same vendor transcript gives two different savings figures (about RMB 15 million and about 14 million) for one tobacco case and admits finance cost data was too coarse. A routing vendor's case footnote: "实际优化比例受人工干预程度影响" (the actual improvement depends on the degree of manual intervention), so its 8 % figure is simulated, not realised.

China, process industry and grocery delivery (both vendor, against interest) · 蒙绎泽 (清智优化), ArchSummit 2024 · news.qq.com · 逗号科技 · smartcomma.com

"那么下班前我们先排一下，结果留着。然后让让Solver运算一个晚上，再看看和昨天的结果有什么改进。我估计没有APS让你这样验证" ... "APS的意义不是排程优化，而是排程自动化"

"Schedule once before leaving, keep the result, let the solver run all night, compare. I doubt any APS vendor will let you do that test." ... "The point of APS is not schedule optimisation but schedule automation." A CSDN write-up proposes "plan execution rate" as the KPI: below 60 % means the APS plan is not executable.

China, discrete manufacturing, APS · anonymous 20-year APS consultant via 数据魔术师, 2020-07-24 · cloud.tencent.com · bbs.csdn.net

"Может исчисляться несколькими десятками процентов затрат"

Savings "can amount to several tens of percent of costs" if mileage was previously written off from the odometer with no route control, but that comes from mileage control, not routing; with mileage already controlled, routing gains are a few percent. The head of logistics also warns: "Получив экономию на топливе, нужно держать руку на пульсе рыночной заработной платы водителей" (having saved on fuel, keep a hand on the pulse of market driver wages).

Ukraine, distribution fleet, routing · Yevhen Nikushin (FADO), TradeMaster, 2019-03-22, in Russian · education.ua

"Le S&OP n'échoue pas à cause de l'outil. Il échoue quand l'entreprise refuse d'arbitrer." ... "Il devient un rituel. Une séquence mensuelle où l'on regarde les problèmes" ... "Dans ce contexte, aucun outil ne peut sauver le processus."

"S&OP does not fail because of the tool. It fails when the company refuses to arbitrate." ... "It becomes a ritual, a monthly sequence where problems are looked at" ... "In that context no tool can save the process."

France, cross-sector, S&OP · Le Journal de la Supply Chain, 2026-06-15 · lejournaldelasupplychain.fr

"most have not tied AI road maps to clear performance metrics"; more than two thirds of 139 GCC organisations "haven't moved beyond pilots", 11 % report measurable financial returns. The UAE's claim that ITTI-assessed factories raised productivity 40 % comes with no baseline or method.

Gulf, cross-sector (consultancy survey reported by a newspaper; numbers against the consultancy's sales story) · The National on McKinsey and the GCC Board Directors Institute, 2025-11-13 · thenationalnews.com · thenationalnews.com (ITTI)

"many companies are unclear on governance, and don't know how to measure a good plan." Of 211 respondents, less than 30 % were satisfied with digital-transformation implementations and less than 20 % with AI strategies.

English baseline, supply chain planning · Lora Cecere (independent analyst), 2025-12 · supplychainshaman.com · linkedin.com

1.7 Keeping it alive: the champion leaves, IT will not maintain it, the consultants are gone

"Voor elke andere afdeling of zorginstelling moeten eerst alle specifieke randvoorwaarden opnieuw in kaart worden gebracht en in het model worden verwerkt. Daar heb je mensen voor nodig met de juiste kennis en kunde. En als je kijkt naar hoeveel roosterproblemen er in ziekenhuizen spelen, en hoeveel mensen met die kennis we hebben, hebben we er daar lang niet genoeg van." ... "Zorginstellingen hebben zelf vaak niet de mensen in huis die dit zelf kunnen."

"For every other department or institution all the specific constraints have to be mapped again and built into the model. You need people with the right knowledge for that. Looking at how many rostering problems hospitals have and how many people with that knowledge we have, we are nowhere near enough." ... "Care institutions often do not have the people in house who can do this themselves." Her Gelre model survives only because the hospital has an integrated capacity-management team.

Netherlands, healthcare, rostering · N. Hamster (Twente student contacted by 200+ organisations after a bachelor thesis), STAtOR July 2026 · vvsor.nl PDF

"OCP Planners preferred to rely on their expertise and manual scheduling, especially since they had witnessed the ineffectiveness of an obsolete DLP optimizer for more than five years." The commercial optimiser had "millions of constraints, and hundreds of thousands of variables", ran 10 hours to a poor schedule, documentation "not very detailed nor well-updated"; researchers reverse-engineered it from .lp files and found "many missing, redundant, useless, and hard-coded variables and constraints." The rescue needed "local researchers who know the company's culture" and weekly meetings "sometimes 2-3 times a week."

Morocco, process industry and mining, production scheduling · Er Raqabi et al., GERAD G-2023-29, with OCP co-authors, 2023 · gerad.ca PDF

"业务逻辑的任何一个变化都会被传导到算法模块中，使得系统变得极其脆弱且难以维护" ... "工作机会也少，一般情况是公司需要的时候着急招聘，解决完问题就没事干了，很尴尬。"

"Any change in business logic propagates into the algorithm module, making the system extremely fragile and hard to maintain" (王源). And from an OR engineer on the job market: "Few jobs too; usually a company hires in a hurry when it needs you, and once the problem is solved there is nothing to do. Awkward."

China, airlines and cross-sector · zhihu.com (王源) · comment under a 406-upvote Zhihu column on OR careers, 2020-12-23 · zhihu.com

"einen Single Point of Failure: Meist versteht nur eine Person die Datei" ... "Ein besonders wichtiger Punkt war, wie selbst kleine Änderungen in den Daten zu grundsätzlichen Veränderungen in den Lösungen führen können"

Most Excel plans have "a single point of failure: usually only one person understands the file" (skillbyte), and the same applies after a bespoke model is handed over. The GOR's 2025 science-prize lecture on inventory optimisation stressed "how even small changes in the data can lead to fundamental changes in the solutions" as an implementation obstacle, alongside meaningful data, scalability and understandability.

Germany, discrete manufacturing and inventory · skillbyte.de · GOR press release on Stefan Minner's lecture, 2025-09-03 · or2025.de PDF

"Да и «точно настроенные» системы имеют свойство превращаться в неизменяемые."

"And 'finely tuned' systems tend to turn into unchangeable ones." The one bespoke routing module in the Russian sources ran seven years because its single author stayed; map tiles had to be refreshed every six months and the hardware "был мягко говоря устаревшим" (was, to put it mildly, obsolete).

Russia, discrete manufacturing and distribution · Habr commenter LyakishevIV under the Rightstep APS article · habr.com · habr.com (rebuilder)

"If the champions leave the organization or are transferred to some other part of the organization, the project may easily get shelved." ... "management wants the project implemented but the IT guys are opposed (possibly because it will create headaches ... when the OR people are gone and the IT people have to maintain it)." The Chalmers field study of Swedish APS users lists "dependence on a consultancy firm" and "use of parallel systems" as onward-phase problems. In the Gulf: "There is no institutional memory ... when the event is over, they leave, and the knowledge for the next time needs to be created from scratch."

English baseline and Gulf · prubin on OR Stack Exchange · or.stackexchange.com · Kjellsdotter Ivert and Jonsson 2011 · chalmers.se · curator quoted in The Art Newspaper, 2025-12-09 (culture sector, same mechanism) · theartnewspaper.com

3.1 Monitoring infrastructure, hardware and software

"Gedurende de dag verandert na de eerste rit nog 80% van de planning, naar schatting van de planners."

"During the day, after the first trip, 80% of the plan still changes, by the planners' own estimate." Information reaches the planners via "WhatsApp, mobiele en vaste telefoon", radio, camera images and a counter where drivers ask questions.

Dutch, container trucking · TNO, Talking Trucks, 2022, p. 23. publications.tno.nl

"Die Fertigungsaufträge werden bei einem Drittel der online befragten Betriebe elektronisch zum Arbeitsplatz übermittelt. Bei den restlichen Unternehmen wird dafür der Zwischenschritt eines Ausdrucks auf Papier benötigt. Bei den Rückmeldedaten können 44 % prozess- und auftragsrelevante Maschinendaten automatisiert erfassen."

"One third transmit orders electronically to the workstation; the rest print on paper. 44% can capture machine feedback automatically."

German, Austrian manufacturing SMEs · Lewandowski, TU Wien dissertation, 2011. repositum.tuwien.at

"Excel ist nicht das Problem, der fehlende Rückkanal ist es"

"Excel is not the problem, the missing back-channel is". "Der Plan ist damit keine Abbildung der Realität, sondern eine Abbildung dessen, was der Planer zuletzt wusste" (The plan is not a picture of reality but of what the planner last knew).

German, consultancy · skillbyte GmbH, 2026-08-06. skillbyte.de

"Pokud se neevidují poruchy nebo opravdu všechny zakázky bez výjimky, celý plán se rozbije."

"If breakdowns or truly all orders without exception are not recorded, the whole plan breaks."

Czech, ERP project manager · Petr Figura (K2), Technický týdeník, 2021-11-07. technickytydenik.cz

The plant had no operation routings, only ingredient lists; technologists went to the floor with stopwatches to build them. "Система работает, но картину портит брак." (The system works, but scrap spoils the picture.) The director later disabled product traceability after a customer complained about jammed label printers.

Russian, steel radiator plant · Vasily Karpov, Habr, 2019-11-20. habr.com

Drivers destroyed tracking devices: "Так за три года мы потеряли 9 устройств." (So in three years we lost 9 devices.) A commenter under the Rightstep APS article: "если нет точного оперативного учета, то вы как директор завода им не управляете" (if there is no precise shop-floor accounting, then you as plant director are not managing it).

Russian, wholesale distributor · Habr, 2019-01-23, habr.com; comment thread habr.com

GPS tablets dropping signal: "Значит пошатнется основополагающая функция GPS контроля" (Then the foundational function of GPS control is shaken), that function being the recording of real mileage.

Ukrainian, distribution fleet · Yevhen Nikushin (FADO), TradeMaster, 2019-03-22. education.ua

"设备定位数据质量差 ... 外集卡司机未按规定打开定位程序"

"poor equipment positioning data ... external truck drivers not switching on the positioning app as required". "人的容错能力很强，然而算法不是" (Humans tolerate errors well; algorithms do not.)

Chinese, container port · 王磊 (Alibaba Cloud), DataFunTalk, 2023-09-26. zhuanlan.zhihu.com

"销售口头通知就能改单、插单，线下调整完不同步到系统 ... 系统计划失去指导意义，沦为摆设"

"sales changes or inserts orders verbally, offline adjustments are not synced to the system ... the plan loses meaning and becomes decoration".

Chinese, garment factories, vendor admitting · 亿澳斯 on Zhihu. zhihu.com

"il est difficile d'envisager la mise en place d'un process 4.0 sans avoir au préalable organisé et systématisé le suivi et la collecte des données"

"it is hard to imagine a 4.0 process without first organising and systematising data tracking and collection". Québec's STIQ barometer: "Avoir des robots dans une usine tout en planifiant la production avec des bons de travail papier créés avec un chiffrier électronique ne peut être décrit comme une usine 4.0."

French, government evaluation · Deloitte for the French government, Dec 2023, info.gouv.fr; STIQ 2021, stiq.com

The plan "se rompe al primer contacto con la planta de producción" (breaks on first contact with the shop floor) and "tan pronto como la primera máquina se avería, comienza la reprogramación" (as soon as the first machine breaks down, rescheduling begins). Vendor source.

Spanish, Latin America, vendor · Demand Solutions Latam. demandsolutionslatam.com

"deliveries navigate by landmark, WhatsApp pin, and driver memory"; "Most operators know these numbers monthly and approximately". Vendor source.

Gulf, Dubai software vendor · Software Disruption FZCO. softwaredisruption.com

3.2 Data integration, ingestion and silos

"ich bin AN in einem KMU (Zerspanung), welches vor 8 Jahren ein ERP System eingeführt hat. ... nur wird es aufgrund einer unzureichenden Einführung nur begrenzt genutzt (Erstellung Rechnungen, Lieferscheine, aber keine Produktionsplanung oder Lagerverwaltung). ... Hauptgrund sind m.E. die fehlenden Stücklisten und Arbeitsgänge"

"I work at a machining SME that introduced an ERP eight years ago ... used only in part: invoices, delivery notes, but no production planning or stock management ... the main reason in my view is the missing bills of material and routings". Reply: "Nur wenn die GF dahinter steht und Druck macht, funktioniert es" (it only works if top management stands behind it and pushes).

German, machining SME employee · r/de_EDV, 2022-01-13, read via archive. reddit.com

Trovarit's 2026/27 ERP user study (1,538 ratings): interfaces and integration named a top problem by about 19%. Bitkom 2026 (502 logistics firms): data availability named by 52% as an obstacle. A route-planning guide: the effort "wandert ... nur von der Disposition in die Datenpflege" (just migrates from dispatching to data maintenance).

German · Trovarit via deutscherpresseindex.de; Bitkom bitkom.org; socialeap socialeap.de

"APS nedělá zázraky a plánuje na základě dat, špatný vstup znamená špatný výstup."

"APS does no miracles, it plans from data; bad input means bad output." "Plánování nestojí na algoritmech. Stojí na tom, co mu dáte jako vstup." (Planning does not rest on algorithms. It rests on what you feed it.) Failure starts with stale purchasing dates, unrecorded actual times, and dead orders left in the system.

Czech, Infor partner CEO · Robert Fárek, IT Systems 6/2025. systemonline.cz

"Nějaký software využívá alespoň v některé části výrobního procesu 93 procent velkých a 74 procent malých a středních podniků. Druhou otázkou je celková digitalizace výroby a vzájemná integrace dat, zde jsou výsledky mnohem horší."

"Some software is used in part of production by 93% of large and 74% of SMEs. Overall digitalisation and data integration is another matter, with much worse results." Only 43% of large and 23% of small firms integrate collected data into one central database.

Czech, 207 manufacturing directors · NCP 4.0, Analýza českého průmyslu 1/2023. ncp40.cz

The routing vendor's 1C module covered only part of the data exchange and took two months of in-house work to extend; geocoding went to a third party at 15 kopecks per address because the platform's own was too dear for 30 to 40 addresses a day. Expert.ru on machine building: the key problem is "недостаток систематизированных данных" (a lack of systematised data).

Russian, five-vehicle parts distributor · vc.ru, 2023-06-26, vc.ru; Expert.ru 2025-07-13, expert.ru

"ik heb veel stress omdat mijn data niet correct zijn, maar dat heeft niks te maken met het systeem. Zelfs al zou je Excel gebruiken, als je ook daar niet de juiste waarden hebt, zullen er fouten zijn."

"I have a lot of stress because my data is not correct, but that has nothing to do with the system. Even in Excel, without the right values there will be errors."

Dutch, APS planner, Vlerick interviews · Decroix et al., STAtOR Dec 2024, p. 10. vvsor.nl

A key lesson at Van Berkel: "soft information" such as customer and driver preferences had to be recorded and digitised before any algorithm could run. TNO: "de input data vaak relatief is" (the input data is often relative). Flanders, SME survey: 53% have no digital tool to share information between departments or manage the supply chain; 72% do no data analysis.

Dutch, container trucking · Dinalog dinalog.nl; TNO PDF above; Vlaamse Regering May 2024 themis.vlaanderen.be

The trap is "considérer la data comme un sujet qui se traitera" during implementation; "C'est la première cause de dérapage calendaire." (Treating data as something to sort out later is the first cause of schedule slip.)

French, APS consultancy conceding · findle.fr, 2026-04-23. findle.fr

Some operators cannot import orders because the client's system "n'a jamais été prévu pour s'interfacer et s'est construit sans cette nécessité" (was never designed to interface); one built its own route optimiser on the Excel files its clients send and "« nettoie » ces fichiers des informations inutiles"; EDI connections took "6 mois" for one major operator, "contrainte humaine plutôt que technique" (a human rather than technical constraint). Eurodécision's satire of AI projects: "Puis se rendre compte que l'on n'aura jamais accès aux données" (then realise you will never get access to the data).

French, cycle-logistics operators, 40 interviews · Les Boîtes à Vélo, April 2025, pp. 46, 101-103, lesboitesavelo.org; Eurodécision eurodecision.com

Among Spanish firms not using AI, "dificultades con la disponibilidad o calidad de los datos necesarios" is cited by 52.56% (under 10 employees), 47.67% (10-49), 44.59% (50-249) and 48.16% (over 250). Chilean mining (CESCO): the industry "deberá dejar progresivamente atrás el trabajo en silos" (must progressively leave silo working behind). A Latin American planning vendor: tool availability and operator qualification "raramente aparecen en el ERP" (rarely appear in the ERP).

Spanish, national statistics · INE via IndesIA 2024, indesia.org; CESCO cesco.cl; Demand Solutions Latam, link above

"你去问一个干了很多年的物料计划员：'这个料正常的采购提前期是几天？'他会告诉你：'你说的是哪个供应商的？A家一般7天，B家要10天，C家时好时坏，得催。'... 这些信息，一条都没有写进Excel。"

"Ask a veteran material planner the normal lead time for a part and he says: which supplier? A is usually 7 days, B needs 10, C is hit and miss, you have to chase. None of this is written in Excel." Project managers' most common answer to "what is your biggest risk?": "数据" (data). "数据清洗的工作量，通常占整个APS项目工作量的40%到60%" (data cleaning is usually 40% to 60% of an APS project). An integration failure: ERP operation numbers 10, 20, 30 against APS numbers 1, 2, 3, no conversion, and the shop executed 30 first, then 10, then 20.

Chinese, APS implementer · CSDN bbs, bbs.csdn.net and bbs.csdn.net

Saudi merchants: "تفتقر العناوين إلى الدقة والتنظيم المعتاد (مثل أسماء الشوارع وأرقام المباني)" (addresses lack the usual precision and organisation, such as street names and building numbers). At OCP the rescuing researchers found "many missing, redundant, useless, and hard-coded variables and constraints in the '.lp' files" and "created local databases to manage the PSIMVA models and understand the instances data."

Gulf and Morocco · Raqi raqi.com; Er Raqabi et al., GERAD G-2023-29, gerad.ca

"around 65 percent of APS programs fail to achieve their expected return on investment. One of the top five reasons is poor data management." Organisations "start an APS deployment program assuming their data is fit for purpose, only for gaps in hierarchies, parameters, and master records to become visible once integration testing begins." OR Stack Exchange: "Garbage in, garbage out. If the organization is working with incorrect / corrupted data (and nobody realizes it, or nobody can fix it), the analysis is probably doomed." A Swedish TMS integration: "Their team spent two weeks just cleaning route data and time windows before writing integration code."

English, consultancy survey, n = 80 APS programmes · McKinsey mckinsey.com; OR SE or.stackexchange.com; r/logistics reddit.com

3.3 The action loop: open or closed

"sommige planners [bleken] het heft in eigen handen te nemen en gingen zelf over tot (pogingen tot) validering. Dit deden zij, bijvoorbeeld, door in parallel in het oude systeem te blijven werken en te bekijken of de output al dan niet identiek bleek. Uiteraard verhoogde dit hun werklast aanzienlijk"

"some planners took matters into their own hands and attempted their own validation, by continuing to work in the old system in parallel and checking whether the output was identical; this increased their workload considerably". The system "bleek zeker niet in staat om elke taak op zich te nemen, noch had het de vereiste contextgevoeligheid" (was certainly not able to take over every task, nor did it have the required context sensitivity). The authors: "enerzijds wordt geld gepompt in software die niet optimaal gebruikt wordt; anderzijds verhogen eindgebruikers onbewust hun werklast."

Dutch, APS planners, 26 interviews · Decroix et al., STAtOR Dec 2024. vvsor.nl

"Roostersoftware bestaat wel, maar lijkt in de praktijk nauwelijks (goed) te worden gebruikt."

"Rostering software exists, but in practice it seems hardly used, or hardly used well." VieCuri bought its rostering product for integration with the ERP and compliance with the collective agreement, not for optimality.

Dutch, hospitals · Hamster, STAtOR July 2026, vvsor.nl; Computable 2012, computable.nl

Dispatchers "können ... algorithmische Vorschläge 'übersteuern'" and "Das bestehende Optimierungspotenzial wird nicht immer vollständig ausgeschöpft" (dispatchers can override algorithmic proposals; the optimisation potential is not always fully realised). Vendor admission. "Automatisierte Tourenplanung liefert einen Vorschlag, den ein Disponent im Zweifel korrigieren muss" (automated route planning delivers a proposal that a dispatcher has to correct when in doubt). In APS: "Meister und Schichtleiter müssen dort Einfluss nehmen können" (foremen and shift leaders must be able to intervene). In Austrian SMEs the ERP's coarse plan is handed to Excel, where "Optimierungsverfahren zu diesem Zeitpunkt nicht mehr zur Verfügung stehen" (optimisation methods are no longer available at that point).

German, routing · proLogistik prologistik.com; socialeap, link above; IT-Matchmaker.news news.it-matchmaker.com; TU Wien 2011, link above

"Trop souvent, les planificateurs délaissent l'outil complexe pour revenir à leurs tableurs Excel habituels, créant ainsi une planification parallèle opaque."

"Too often planners abandon the complex tool for their usual Excel sheets, creating an opaque parallel plan." Vendor conceding. Workforce planning: "le retour arrière se fait le plus souvent vers Excel" (the rollback is most often to Excel). S&OP: "Il devient un rituel. ... Les équipes continuent alors à fonctionner comme avant : en escalade, en urgence, en correction permanente." "Le S&OP n'échoue pas à cause de l'outil. Il échoue quand l'entreprise refuse d'arbitrer."

French · DecisionBrain 2026 decisionbrain.com; myRHline myrhline.com; Journal de la Supply Chain lejournaldelasupplychain.fr

An automotive logistics graduate on the shop floor: "Olvídate de fórmulas para calcular los stocks de seguridad y mierdas varias porque no sirven para nada" (forget the safety-stock formulas, they are useless); stock is set "a ojo" (by eye). Planners cover system gaps with "las hojas de cálculo que funcionan en paralelo al sistema ERP" (spreadsheets running in parallel to the ERP). A rostering vendor: the time goes into "rehacer el cuadrante" (redoing the roster), not the first plan.

Spanish · Forocoches 2014 forocoches.com; Demand Solutions Latam, link above; TramitApp tramitapp.com

"Lidé přestanou systému věřit. Vrátí se k Excelu."

"People stop trusting the system. They go back to Excel." The planner "Musí mu prostě věřit." (He simply has to trust it.) Rightstep on Russian plants: "Производство управляется «вручную», ПЗ используются только для учета!" (Production is managed by hand, production orders are used only for accounting); planning "Никак не реализовано, или реализовано в MS-Excel". The one bespoke rostering model that survived did so as a calculator: "Калькулятор в комбинации с умелым пользователем показал свою полезность и отработал свои инвестиции." Drivers' side, routing app rated 2.0 of 5: "Логика построения маршрута, по большей части, отсутствует." and "Приезжаешь на выгрузку, ничего не нажимаешь, а заявка автоматом закрывается." (You arrive to unload, press nothing, and the job closes automatically.)

Czech and Russian · Fárek, link above; Figura, link above; Rightstep habr.com; Lozhkins habr.com; RuStore rustore.ru

Planners: "'系统是给老板看的，Excel才是我真正用来干活的。'" (The system is for the boss to look at; Excel is what I actually work with.) "APS上线 → 计划员不用 → 回归Excel。然后老板得出一个结论，'这玩意是智商税'" (APS goes live, planners do not use it, back to Excel; the boss concludes it is an IQ tax). Cainiao on its own dispatchers: "算法方案一般需要经过人工微调，人工常常因为个人习惯做出不必要的调整，影响全局效果。" (Algorithm plans go through manual fine-tuning; people make unnecessary adjustments out of habit, hurting the global result.) A proposed KPI: "计划执行率 ... 如果计划执行率低于60%，说明APS排的计划不可执行" (plan execution rate; below 60% the APS plan is not executable).

Chinese · 老贱 zhuanlan.zhihu.com; Cainiao, DataFun 2023 sohu.com; CSDN bbs, link above

"OCP Planners preferred to rely on their expertise and manual scheduling, especially since they had witnessed the ineffectiveness of an obsolete DLP optimizer for more than five years." The Hajj scheduling system worked 2007 to 2014; by 2015 the ministry and the authors "no longer handled scheduling and routing for that ritual." DP World's berth tool, 2011: rules-based, so the team could "focus our manual planning expertise on exceptions".

Morocco and Gulf · GERAD G-2023-29, link above; IFORS abstract iaorifors.com; JOC 2011 joc.com

"Over 92% of companies in this survey report the use of ERP and APS, but the technologies are not used as designed. ... Instead of using the expensive optimizers in these systems, planners work on spreadsheets" and then "write the data to the planning systems as a system of record." Genpact: "some planners simply do not trust system recommendations, which leads to system overrides and manual processing"; the APS failure "isn't a matter of under-delivery so much as under-utilization." Routing: "If dispatchers force a rigid route without local driver input, drivers just ignore the sequence or find workarounds." "Driver adoption is the quiet killer." American Airlines: the team "wrote the math and handed it over without engaging users. ... After six months, there was no traction." Chalmers, three Swedish manufacturers: "use of parallel systems and inadequate use of the APS system's potential".

English · Cecere supplychainshaman.com; Genpact genpact.com; r/logistics, link above; Nextmv interview with Doug Gray nextmv.io; Chalmers research.chalmers.se

3.4 Simulate as well as optimise: a digital twin of the system

A TU Delft simulation study of Royal FloraHolland's order handling found the coordinating system "is based on a fixed relation between auction and distribution which limits the control over the process" and proposed new controllers; the record gives no sign the redesign was implemented. Stalled at simulation. For the Rijkswaterstaat road programme, Timmer (1994) describes an LP whose solution "is vaak moeilijk te verkopen" (is often hard to sell), then rebuilt year by year "zodanig dat deze beter is te volgen" (so that it is easier to follow).

Dutch · TU Delft repository repository.tudelft.nl; Timmer vvsor.nl

On a pilot calling in sick: it can be handled by a stochastic model, a standby pool, an attendance bonus, or publishing rosters earlier; "有时候，最简单的方法反而是最有效的方法" (sometimes the simplest method is the most effective). And on the requirements that would feed any twin: "要精确描述问题的本质、期望效果、约束条件，往往超出了业务人员的能力范围" (precisely describing the essence of the problem, the desired effect and the constraints is usually beyond business staff). A grocery routing vendor's footnote: its 8% saving is "对比人工配载与算法仿真结果得出" (manual loading compared with the algorithm's simulated result), so simulation here is the only yardstick, not a planning tool.

Chinese, research institute · 王源 via 运筹OR帷幄 zhuanlan.zhihu.com; 逗号科技 smartcomma.com

HSE's 2025 survey of more than 1,700 machine-building organisations: digital twins at 12%, big data 16%, AI agents 3%. The LTL driver-rostering model that survived did so as a scenario calculator in a skilled planner's hands rather than as an autonomous optimiser (section 3.3). A Czech vendor-run survey: "Až 45 % firem dokonce plánuje v horizontu kratším, než je jeden týden" and 28% need more than four hours to build an operational plan, so re-planning under a what-if is out of reach for a quarter of large Czech plants. Vendor sample.

Russian · HSE ISSEK issek.hse.ru; Adastra adastracorp.com

A consultancy's minimum for mid-sized plants is three capabilities, "Rückmeldung, schnelle Neuberechnung und gemeinsame Sicht" (feedback, fast recalculation, shared view), rather than a full APS. [French] SNCF's seat-assignment optimiser went to production in summer 2024 after "une phase de marche à blanc" (a dry-run phase). [Spanish] Argentina's 2026 KIT 4.0 subsidy lists "simulation" among fundable items; no user account of one.

German · skillbyte, link above; ROADEF bulletin 46 roadef.org; KIT 4.0 blogdelcontador.com.ar

3.5 Accounting for uncertainty

"Een tijdsvenster kan voor een bepaalde klant op een bepaalde dag veel groter zijn dan de input data aangeeft. Sommige chauffeurs zijn bereid een uurtje over te werken, anderen juist niet. Deze flexibiliteit maakt een planning vaak sluitend."

"A time window can be much wider than the data says. Some drivers will work an hour extra, others not. This flexibility is often what makes a plan close." "Het voordeel van menselijke planners is dat ze ... rekening kunnen houden met hoge complexiteit (zoals variabele klantwensen)."

Dutch, container trucking · TNO 2022, link above

"plusieurs entreprises sondées estiment qu'elles font un meilleur travail d'optimisation que le logiciel"

"several firms believe they optimise better than the software": couriers know opening hours and exceptions, tools "ne « voient » pas le relief" (do not see the relief); one operator and his client's operations manager feel the tools "« ne font pas des bons livreurs »" and make couriers "moins capables de gérer les imprévus par eux-mêmes" (less able to handle surprises on their own). A routing vendor's OR developer, after every change: "je vérifie que tout ne va pas s'écrouler" (I check that nothing is going to collapse).

French, cycle-logistics operators · Les Boîtes à Vélo, p. 47, link above; Woopit woopit.fr

A TMS vendor's case: routes "确实短了8%，但客户投诉率反而上升30%。原因是算法没有考虑客户上午不收货、老旧小区大车无法进入、司机不熟悉路线等约束" (8% shorter, complaints up 30%, because the algorithm ignored customers who do not receive in the morning, estates big trucks cannot enter, drivers unfamiliar with routes). Vendor against interest. Cainiao: before algorithms "没有一个具体完整的文档去说明求解的问题，具体逻辑以及权重关系都不清晰" (no document described the problem; logic and weights were unclear). 王源's pilot example (3.4) is the one explicit discussion of a stochastic model versus a process fix.

Chinese · 科箭 qt-asia.com; Cainiao, link above; 王源, link above

"оптимизационная модель не способна учесть всю картину мира при принятии решения"

"the optimisation model cannot take the whole world picture into account". Breakdowns not recorded break the plan (Figura, 3.1). [German] The GOR prize lecture on inventory: "wie selbst kleine Änderungen in den Daten zu grundsätzlichen Veränderungen in den Lösungen führen können" (how even small data changes can fundamentally change the solutions). Short-notice absences and foremen's experience "lassen sich in der automatisierten Planung jedoch nicht immer ausreichend berücksichtigen". Local rules such as "Lieferzeitfenster in Innenstädten oder saisonale Verkehrsspitzen" are hard to encode. The one forecasting layer named in the German lane is Österreichische Post's node-level parcel volume forecasting, presented at an ÖGOR workshop.

Russian and Czech · Lozhkins, link above; GOR 2025 or2025.de; IT-Matchmaker.news, link above; socialeap, link above; ÖGOR PMO 107 gor-ev.de

"The problem specification often changes very rapidly (e.g. hard constraints become soft)" and "'Unspeakable' constraints. There are some constraints that the client will not be able to give you." "An algorithm might spot the mathematically optimal path, but it does not know that a specific loading bay gets blocked by refuse collection at 10 AM or that a particular road is awful in the snow." On a prediction layer: "The models and outcomes as well as the forecast accuracy results are nowhere better than system generated stat forecasts or human overrides." "everything I ever see with supply is done in a vacuum where everything is perfect." Vendor demos: "Clean addresses, predictable windows, no driver callouts, no customer access restrictions."

English · OR SE, link above; r/logistics, link above; r/supplychain reddit.com

"Tampoco se dispone, en un entorno real, de meses para resolver cada problema, más bien semanas con suerte"

"nor does one have months in a real setting, more like weeks if lucky"; in dynamic settings "la flexibilidad, adaptabilidad y mantenibilidad del código y de los algoritmos" matter more than optimality. A port logistics panellist: "a veces con el uso de reglas de negocio les vale para mejorar enormemente sus procesos y operaciones" (sometimes business rules are enough). [Gulf] OCP: "We had identified tacit operational rules not modeled in the PSIMVA." A Dubai vendor: "capacity decisions for Ramadan and Hajj peaks get made on last year's gut feel".

Spanish · SEIO BEIO 2024 seio.es; Decide/Logistop panel decidesoluciones.es; GERAD, link above; Software Disruption, link above

3.6 Being able to write evaluators

The commercial optimiser's objective was "a weighted sum of several key performance indicators (KPIs) where the weights are found by trial and error"; documentation was "not very detailed nor well-updated"; "Within the company, there was doubt about the DLP's usefulness." The model sat unused for more than five years until researchers reverse-engineered it from .lp files and sat with planners, "sometimes 2-3 times a week".

Morocco · GERAD G-2023-29, link above

"那么下班前我们先排一下，结果留着。然后让让Solver运算一个晚上，再看看和昨天的结果有什么改进。我估计没有APS让你这样验证"

"Schedule once before leaving, keep the result, let the solver run all night, compare. I doubt any APS vendor lets you do that test." A planning vendor: "由于依赖人工经验，评价结果往往较为定性，缺乏客观性和量化标准" (relying on human experience, evaluation tends to be qualitative, lacking objective, quantified standards). The "计划执行率" KPI (3.3) is a proposed evaluator.

Chinese · 数据魔术师 mirror, link above; 蒙绎泽, ArchSummit 2024 news.qq.com; CSDN bbs, link above

TNO ran three live trials with the planners' own plans as the benchmark and found "een vergelijkbare (en zelfs iets verbeterde) punctualiteit en capaciteitsbenutting" (comparable, even slightly better, punctuality and capacity use). Hamster needed "meerdere van deze validatierondes" (several validation rounds) because constraints "waren bij de eerdere gesprekken met de betrokkenen niet naar voren gekomen". Poppelaars: what becomes scarce is "oordeel over probleemformulering, validatie en het opbouwen van vertrouwen bij opdrachtgevers" (judgement about problem formulation, validation and building trust with clients). The Vlerick planners' parallel running (3.3) is an evaluator built by the users because nobody else did.

Dutch · TNO, link above; Hamster and Poppelaars, STAtOR July 2026, link above; Decroix et al., link above

At Southwest, OR PhDs sat with a veteran dispatch supervisor for a year of validation "contests" against human planners before the model won acceptance; a greedy-versus-optimised crew schedule test showed about $100m a year difference. Cecere: "many companies are unclear on governance, and don't know how to measure a good plan." The 2026 Edelman winner's transcript, seen only as a search snippet and not opened: "Before a user interface even existed, we manually passed solutions to planners to get their feedback".

English · Nextmv interview, link above; Cecere, link above; Microsoft Edelman 2026, read from a search snippet only; the page was not opened youtube.com

Rightstep runs a directive plan and a computed plan side by side before trusting the model. The two bespoke models that survived report self-estimated savings: "10 to 12% fuel saving, self-estimated" (seven-year routing DLL) and 14% on long-leg looped routes (rostering calculator); Ozon's warehouse figures come with no baseline. [French] "On ne peut faire confiance à un modèle d'optimisation qu'en le testant sur un ensemble de données pertinentes." (You can only trust an optimisation model by testing it on relevant data.) Vendor. The DGA explainability case: "Il faut donc être capable d'expliquer les sorties des outils RO pour que les chefs s'en emparent" (one must be able to explain the tools' outputs so that the commanders take ownership). [German] "In vielen Fällen bleibt unklar, wie die im Hintergrund arbeitenden Algorithmen zu ihren Ergebnissen kommen. Selbst Führungskräfte haben oft keinen Einblick." (It often stays unclear how the algorithms reach their results. Even managers have no insight.)

Russian · Rightstep habr.com; rebuilder and Lozhkins, links above; Ozon retail.ru; DecisionBrain 2020 decisionbrain.com; ROADEF bulletin 45 roadef.org; Böckler Impuls 2024 boeckler.de

3.7 Clear signals of real-world value

If mileage was written off from the odometer with no route control, savings "Может исчисляться несколькими десятками процентов затрат" (can amount to several tens of percent of costs), but that is from mileage control, not from routing; with mileage already controlled, routing gains are a few percent. "Получив экономию на топливе, нужно держать руку на пульсе рыночной заработной платы водителей." (Having saved on fuel, you have to keep a hand on the pulse of market driver wages.) [Russian] Drivers' fuel "savings" were part of their pay, so "Знаю случаи когда такая экономия выходила боком" (I know cases where such savings backfired); a consultant paused the IT rollout to fix rules first: "Технологии не лечат кривые правила" (technology does not cure crooked rules). [Czech] After an APS sale "firmě vzroste obrat o několik desítek procent, zisk ale nevzroste prakticky vůbec nebo dokonce klesá" (turnover rises by tens of percent, profit practically not at all, or falls).

Ukrainian, distribution fleet · FADO, link above; Habr comments habr.com; vc.ru "Natalia" vc.ru; Komenda 2014 systemonline.cz

McKinsey, n = 80: "around 65 percent of APS programs fail to achieve their expected return on investment." Cecere, 211 respondents: "less than 30% of companies were satisfied". AMR 2003, 132 companies: "manufacturing planning and scheduling modules had the highest rate [of shelfware], at 15%". A survey respondent: "We spent a year setting up a new tool. No one uses it because the process wasn't in place." Where the value is clear it is large and counted: crew and fuel at about 45% of a $20bn revenue base, about $100m a year between a greedy and an optimised crew schedule. Where it is not, the product dies: a sports-scheduling model "It's only scheduled once a year." "If it's not in production, it has no value." A headcount-only prototype: "Because the prototype focused only on using fewer inspectors, the unions shot it down."

English · McKinsey, link above; Cecere LinkedIn linkedin.com; SupplyChainBrain 2003 supplychainbrain.com; AbcSupplyChain abcsupplychain.com; Nextmv, link above; r/OperationsResearch reddit.com; Timefold timefold.ai

"près d'une entreprise sur trois (33 %) n'a pas encore rentabilisé son investissement en automatisation, et 10 % demeurent incertaines"

"nearly one in three has not yet broken even on its automation investment, 10% are unsure"; in manufacturing 12% see payback within a year. STIQ: "la difficulté à évaluer le retour sur investissement (48 %)". "Les écarts entre les TCO communiqués par les éditeurs et la réalité observée trois ans après sont parfois considérables." Lhyfe's definition of success is the exception: "son déploiement en production avec impacts sur les KPIs opérationnels".

French and Québec · FCEI / Investissement Québec 2025 cfib-fcei.ca; STIQ, link above; findle.fr, link above; ROADEF bulletin 46, link above

A planning vendor gives two different savings figures (about RMB 15 million and about 14 million) for the same tobacco case and admits finance cost data was too coarse to estimate savings precisely. "很多老板花几百万上APS，最后只用来替代计划员手里的Excel" (many bosses spend millions on APS and end up replacing the planner's Excel). JD Retail claims "24年实现供应链成本节降5亿" (RMB 500 million in 2024), self-reported. A study of 200 factories: full digitalisation at 2 to 3% net margin "需要20年才能回本" (20 years to pay back), so most "精打细算，在管理上抠效率" (squeeze efficiency out of management), including a home-made scheduler that lifted utilisation from 60% to 85%, "这些都不需要什么高科技" (none of this needs high tech).

Chinese · 蒙绎泽, link above; e-works summary e-works.net.cn; JD developer.jdcloud.com; 36Kr / 复旦 36kr.com

McKinsey GCC, 139 executives: 84% have adopted AI somewhere, "more than two-thirds haven't moved beyond pilots", 11% report measurable financial returns; "most have not tied AI road maps to clear performance metrics". The UAE's ITTI claims "productivity has risen by 40 per cent" with no stated baseline or method. OCP's rescued scheduler claims +5% turnover (USD 240m) "as witnessed by the industrial partner verification letter".

Gulf and Morocco · The National 2025-11-13 thenationalnews.com; The National 2026-05-04 thenationalnews.com; GERAD, link above

Value came from elsewhere: VieCuri bought rostering software to be "zeker ... dat er volgens de cao wordt geroosterd" (sure rostering follows the collective agreement) and for the ERP link; Agrifirm's trigger was "De beslissing om de betaalstructuur van de transporteurs te veranderen" (changing how carriers are paid). Value went negative: after NS's efficiency rosters "had het spoorbedrijf meer personeel nodig, terwijl tegelijkertijd het zieketeverzuim enorm steeg. Gevolg: honderden treinen moesten maandenlang dagelijks geschrapt worden." Telgen: "het management is niet geïnteresseerd in de te gebruiken techniek, maar in het feit of het probleem op een voor hen bevredigende wijze opgelost kan worden." The AI-BIPTO proof of concept reports no deployment, adoption or measured savings.

Dutch · Computable 2012 and 2013, computable.nl, computable.nl; Treinreiziger treinreiziger.nl; Telgen vvsor.nl; Dinalog AI-BIPTO dinalog.nl

The clearest value signal is a missing person: 53% of logistics firms lack dispatchers, so "the planner who would resist the tool is the one missing" (Bitkom 2026). APS users name on-time delivery (75%) as the goal and stock reduction near the bottom (vendor survey). At craft level the signal is trivial to read: a 250 EUR cutting optimiser "bei einem Auftrag schon alleine ca. 200 Euro gespart" (saved about 200 EUR on one order). KfW: the share of Mittelstand firms completing digitalisation projects fell to 30%. [Spanish] Free Industry 4.0 consulting for industrial SMEs was cut by about 40% because "la previsión inicial ... ha sido notablemente superior a la realidad"; cost is cited by 68.61% of micro firms and 72.61% of 10-49 firms as the reason not to adopt; Chilean miners: "El principal desafío no está en la adopción inicial de tecnología, sino en su escalamiento."

German · Bitkom, link above; INFORM survey software-journal.de; woodworker.de 2009 woodworker.de; KfW 2025 kfw.de; BOE 2025 boe.es; IndesIA, link above; Forbes Chile forbes.cl

Consulting vs product

Matrix view: Buying path × region

The vendor admission. A solver-vendor founder on OR Stack Exchange: "I have never once come across a situation where we could help a client adopt an OR solution (assuming they're not just buying a new solver or a maths compiler) without some consulting, a customised solution, and some integration work. ... no-one wants to fork out half a million or more for something that doesn't do everything they (think they) might need." And on the buyer: "they don't feel confident they'll be able to keep using the solution without the people who set everything up in the first place" (Nikos Kazazakis, or.stackexchange.com). The French Hexaly founder says the same from Paris: "the technology is complicated and costly to deploy in companies, especially in small and midsize companies", OR "lacks an established, practical methodology", and "I am not sure we are experiencing momentum now" (nextmv.io, vendor blog, vendor speaker).

Who buys what, by size. The French OR society's white paper describes the market structure plainly: large groups keep in-house OR, "Les autres entreprises, en particulier celles de plus petite taille qui n'ont pas les moyens de maintenir de façon durable une expertise en RO interne font, soit appel à de telles SSII (ou à des laboratoires académiques) pour sous-traiter la résolution des problèmes de RO, soit achètent des logiciels « sur étagère »" (smaller firms that cannot sustain in-house OR expertise either outsource to IT services firms or academic labs, or buy off-the-shelf software). The text is unchanged between the 2011 and 2019 editions (roadef.org 2019, 2011). Eurodécision's own client list in 2011: "Nos clients sont essentiellement de grands groupes" (Air France, RATP, SNCF, Renault, EDF, Total, La Poste). The Prix Indus'RO finalists 2015 to 2025 are all large organisations, no PME.

In-house at the top: large groups and China's platforms. Where OR is routine it is built inside: Deutsche Bahn practises OR "sowohl von Experten in den operativen Bereichen als auch zentral in Serviceeinheiten" across five divisions (GOR prize 2021, gor-ev.de); ÖBB's yield management runs on "a team of 3 Operations Research specialists" handling "more than 1.3 million control activities" a day (ÖGOR PMO 107 PDF); Flix builds "an OR-based software product to support network planners" with five universities; GRTgaz's eight-person RICE team covers "toutes les étapes, de l'expression du besoin ... jusqu'à l'implémentation, les tests, la maintenance et la formation" (ROADEF bulletin 45); SNCF's OPTIPLACE seat assignment went to production in summer 2024 after "une phase de marche à blanc" (bulletin 46). In China a practitioner's team-size list reads: "开水团、阿里、京东等头部公司，运筹团队规模较大；字节跳动、华为、虾皮、滴滴、顺丰等公司（永辉应该在这里），运筹团队规模适中；国家电网、中石油/中石化、瑞幸咖啡、58同城、长城汽车、蒙牛、BOSS直聘等公司，运筹团队人数较少，一般不会超过3个人。" (Meituan, Alibaba, JD large OR teams; ByteDance, Huawei, Shopee, Didi, SF medium; State Grid, PetroChina/Sinopec, Luckin, 58.com, Great Wall Motor, Mengniu, BOSS Zhipin usually no more than three people) (blog.csdn.net). JD Retail claims "24年实现供应链成本节降5亿" (RMB 500 million supply-chain cost reduction in 2024) and that in-house algorithms let it "摆脱对商业运筹求解器的依赖" (shed dependence on commercial solvers), self-reported (developer.jdcloud.com); Cainiao started on Gurobi and built its own solver (sohu.com). In Russia the same holds for Yandex, Ozon, Dodo and X5 (habr.com Dodo, habr.com X5). The Gabler encyclopedia entry by Marco Lübbecke records the German counter-movement: in-house OR groups were "im Wesentlichen aufgelöst" and "in die IT- und Fachabteilungen integriert" (essentially dissolved and absorbed into IT and line departments), while OR algorithms inside standard software spread widely (gabler.de). Telgen reported the same for Amro, NMB, Akzo and PTT in the Netherlands in the 1970s and 80s.

The Chinese files disagree with each other on self-built APS and both readings are kept: e-works says "不少大中型企业选择了自研APS系统" (many large and mid-size firms chose to build their own APS) (e-works.net.cn search-page summary), while the investor note from 高鹄资本 says "部分企业为此选择自研APS，但市场基本没有成功案例、效果不佳" (some firms chose to build APS in-house, but the market has essentially no success cases; results are poor) (cygnusequity.com, investor thesis, own survey).

Project-based vendor economics. 杉数科技's co-founder describes the model at the top of the Chinese market: "根据客户业务需求，以项目制的形式提供定制化的落地解决方案，客单价在数百万元级别" (customised solutions on a project basis, deal sizes in the millions of RMB), and the interviewer notes "项目制、定制化的业务模式存在依靠人头的弊端" (the project-based, customised model has the drawback of scaling with headcount) (ifenxi.com). A Chinese APS vendor admits "传统项目制开发模式难以应对工业场景的复杂性与动态性" (the traditional project-based development model cannot cope with the complexity and dynamism of industrial settings) (谷斗科技, vendor, goodo-tech.com); another says "坦率地说，过去五年我看到的失败案例远比成功案例多" (frankly, in the past five years I have seen far more failures than successes) (悠桦林, vendor, zhihu.com). One vendor's price and duration statement: "大部分APS系统导入时间在4-8个月 ... 50-200万都属于正常范围" (most APS implementations take 4 to 8 months, RMB 0.5 to 2 million is normal) (安达发, vendor, andafa.com). In the English baseline the Timefold founder on a sports-scheduling product: "As a product, capturing 10% of that 3-4M USD value is very unlikely. As as service, maybe. The biggest problem? It's only scheduled once a year." (reddit.com). The Chinese job-market comment in section 1.7 ("once the problem is solved there is nothing to do") is the same economics seen from the engineer's side. The German route-planning market is the small-vendor product version: 35 vendors, most under 100 staff, "Die meisten Tourenplanungssysteme kosten zwischen 5.000 € und 20.000 €" (Fraunhofer SCS 2015/2016, vendor survey, scs.fraunhofer.de PDF).

The product path: bought widely, finished rarely, planned around in Excel. In the English baseline, "Over 92% of companies in this survey report the use of ERP and APS, but the technologies are not used as designed ... planners work on spreadsheets" (Lora Cecere, supplychainshaman.com); 81.1 % of 164 S&OP professionals use Excel or Google Sheets for S&OP, dedicated tools 5.1 %, with one respondent: "Even with SAP, we always end up back in Excel. It's the only thing that really works." (abcsupplychain.com); "only 10% of companies have fully completed their APS deployments" (McKinsey figure, second-hand via Dave Medd's LinkedIn post; the McKinsey page timed out twice, linkedin.com). The Excel layer by region: 37 % of German firms plan supply chains purely on Excel or Excel plus standalone tools vs 18 % global (IDC survey commissioned by Kinaxis, vendor-commissioned, logistik-heute.de); 70 % MS Office at the detailed-scheduling layer in Austrian mid-sized manufacturers in 2011 (TU Wien); Excel ranked first as the staff-planning tool in warehouses, only one in three with a dedicated system (Fraunhofer IML 2024, Italian sample, iml.fraunhofer.de); "71 % firem se stále ještě spoléhá na tradiční metody plánování, jako jsou například excel tabulky" in large Czech manufacturers (Adastra workshop survey, vendor, self-selected, adastracorp.com); "Tylko 2% firm korzysta z systemów APS" among 350 Polish manufacturers in 2016 (BPSC, vendor survey against interest, benchmark.pl); "市场渗透率仅为3%-4%" for intelligent APS in China, customers mainly above RMB 400 million revenue (高鹄); "Roosters worden vrijwel overal nog met de hand gemaakt. Roostersoftware bestaat wel, maar lijkt in de praktijk nauwelijks (goed) te worden gebruikt." in Dutch hospitals (Hamster 2026); "Sigue siendo habitual planificar con Excel o herramientas desconectadas" and "Excel que nadie quiere tocar (y siempre falla)" in Spain (IDOM, vendor, idom.com).

What products are actually bought for. VieCuri hospital moved from Excel to ORTEC Harmony in 2013 because "het systeem gekoppeld kan worden" to its ERP and so it could be "zeker zijn dat er volgens de cao wordt geroosterd" (sure rostering follows the collective agreement): compliance and integration, not optimality (computable.nl, vendor-adjacent, buyer's reasons only). Agrifirm's routing purchase was triggered by "De beslissing om de betaalstructuur van de transporteurs te veranderen" (changing how carriers are paid) (computable.nl). A Czech APS user in 2008 said APS pays off only where machines can make alternative products and where sales is not the bottleneck (Merz, TRW Automotive case, systemonline.cz). A Russian consultancy argues most discrete plants do not need a scheduler at all: "В моей практике, в дискретных производствах, крайне редко APS–scheduler необходим" (habr.com).

Products are oversized below the large tier. "Für viele Mittelständler ist ein vollausgebautes APS überdimensioniert" (skillbyte, which recommends not switching off Excel on a cut-over date); an 850-PO tyre distributor evaluating o9, Logility, Relex and Netstock: "Kinaxis pretty much admitted they were overkill" (reddit.com); Argentine SMEs reject SAP APO-class products and build "aplicaciones ad-hoc vinculadas a sus sistemas empresariales", executed "de forma improvisada ... sin una metodología establecida" (Vidoni and Vecchietti, JAIIO 2015, sadio.org.ar PDF). On the buy side of APS lock-in: "Companies will want to avoid the supplier tie-in that is currently created by having APS platforms that require significant consultancy/configuration/customisation" (u/Psychokraai, reddit.com). A Chinese selection guide's rule: "APS项目成功与否，软件功能只占30%，实施团队占70%" (whether APS succeeds is 30 % software, 70 % implementation team) and check whether "数据清洗" and "约束建模" are itemised deliverables, otherwise "那这些活最后全是你的" (all that work ends up yours) (bbs.csdn.net).

The Excel-plus-skilled-planner layer that neither path displaces. The Austrian dissertation: software support in detailed scheduling "ist ... teilweise recht gering und wird oft durch das Geschick der einzelnen Mitarbeiter kompensiert" (is partly quite low and often compensated by the skill of individual employees); 67 % of firms see the know-how split evenly between staff and software, only 7 % see the software as decisive. A Latin American planning vendor: "la experiencia en planificación reside principalmente en la cabeza de personas individuales" and the parallel spreadsheets work "solo mientras las personas adecuadas estén disponibles" (Demand Solutions Latam, vendor, demandsolutionslatam.com). The Russian driver-rostering MIP survived only as "Калькулятор в комбинации с умелым пользователем" (a calculator in combination with a skilled user) with a claimed 14 % saving (habr.com). The Chinese steel S&OP veteran: his role of translating "业务语言和认知" (business language and understanding) into the system "本身就变成了系统的一部分" (itself became part of the system) (zhihu.com). A German production-planning vendor: "Erfahrene Produktionsplaner sind schwer zu finden" (tetys, vendor, tetys.de). The one thing that pulls this layer out is the planner's absence: 53 % of German logistics firms lack dispatchers (Bitkom 2026), and 46 % of US manufacturers report difficulty hiring planning and scheduling staff (Deloitte 2024, seen only via DecisionBrain's vendor statistics page, decisionbrain.com, original not opened).

field report · 2026-10-16 · Claude's reasoning over the OR adoption report of the same day

Robots and the OR stall: what changes when the executor is a machine

The OR adoption report found that optimisation projects stall at data, daily use and ownership, never at the solver. This page asks what happens to those three stalls when drivers, pickers, operators and porters are replaced by robots, and who then gets to own the optimisation.

Claude's reading, not a dataset Everything on this page is reasoning from the source report plus general knowledge. Quotes and numbers that come from the report are cited to its section. Nothing here was surveyed or interviewed.

0. In short

The report's three stalls all have one cause: a human stands between the plan and the world and quietly absorbs the gap. The planner fills in the missing routing, the driver knows the dock opens at nine, the nurse swaps a shift. Remove the human executor and the gap has to be closed in software, or the operation stops. So robots do not make optimisation and formalisation less important. They make them a precondition of running at all, instead of an improvement on something that already runs.

Three things flip. The plan goes from advisory to imperative, so the stall at "planners drift back to Excel" disappears at the execution layer and reappears one level up, at the fleet console. The data problem goes from "master data nobody maintains" to "intent and rules nobody has written down", because robots measure the world for free but cannot measure what the customer wants. And the ownership problem is solved by being moved: the optimisation sits inside the fleet software, which belongs to whoever controls the robots, and that is usually not the buyer.

The consequence for who can own optimisation solutions: the optimisation layer follows the control interface. Whoever can send a command to the robot owns the planning market around it. Today that is the robot vendor, the autonomy stack, or the platform that built both in-house. An independent optimisation vendor survives only where an open control standard separates the fleet from the planner, or where it owns the one thing the vendor cannot, the buyer's business rules and the cross-vendor exception data. That is the question any industrial-agents thesis has to be tested against.

1. The one mechanism in the source report

The report's finding in short (section 0): in all eight language regions, projects stall at data, then at daily use, then at ownership. Its clearest evidence for why is in section 3: in Austrian plants software support in detailed scheduling "wird oft durch das Geschick der einzelnen Mitarbeiter kompensiert"; the Russian driver-rostering MIP survived only as "a calculator in combination with a skilled user"; the Chinese steel veteran's role of translating business language into the system "itself became part of the system". The human is the adapter between an incomplete model and a messy reality, and the adapter is cheap, so nobody finishes the model.

A robot cannot be that adapter. Everything below follows from that one difference.

2. The three stalls, replayed with robots

Stall (report section)	With a human executor	With a robot executor	What it does to the market
Data (2.3)	Master data, routings, addresses are missing; the planner or driver fills the gap from memory. Gulf last mile "runs on landmarks and WhatsApp pins" (section 5).	Observation data becomes abundant and free: the robot maps the hall, logs every pick, every metre, every wait. Intent data (the order, the due date, the customer rule, the exception) stays as scarce as before and now has to exist before the first move.	The data project is no longer optional. It splits into a vendor part (perception, maps, telemetry) and a buyer part (rules, priorities, exceptions). The buyer part is the bottleneck and the consulting.
Daily use and trust (2.5)	Planners run a parallel plan in Excel and revert on the first odd output. Drivers and nurses reject plans (NS 2001, De Lijn 2024, section 5).	The robot does not keep a spreadsheet. Acceptance moves to the humans who remain: the fleet supervisor with a manual mode, the warehouse worker paced by the robot, the works council. Rollback is no longer "to Excel" but "to people doing the job again", which is far more expensive, so it happens less and hurts more.	Trust is bought through SLAs and uptime rather than through planner buy-in. Co-determination fights move from roster fairness to robot pacing of humans.
Monitoring (2.6)	"Nobody can say what the plan was worth."	Execution is logged to the second; the realised value of a plan is measurable against ground truth, every hour.	This is the stall robots actually remove. Measurable value fixes the budget stall upstream and makes a closed loop possible. See question 14.
Ownership and maintenance (2.7)	The champion leaves, IT will not maintain the model, consultants are gone.	The model lives inside the fleet manager or autonomy stack. The vendor maintains it, across all customers, and often owns the robot too (robots as a service).	Solved by relocation, not by capability. The buyer never owned it. Lock-in is physical: shelving, grid, chargers, maps.
Frequency of use (section 3, Timefold founder: "It's only scheduled once a year")	Many planning problems are too rare to carry a product.	A fleet is re-planned every few seconds: task allocation, traffic, charging, repositioning.	The economics that killed OR products are fixed. Continuous optimisation can be a product. That is why the product exists inside the robot vendor and nowhere else.

3. The questions

Ownership is the hinge of this part. Whoever holds the control interface, the business rules and the telemetry decides who can even run the optimisation loop, so questions A to C are read in the light of D and E: what is left to optimise, who holds the tacit rules and who does the integration all depend on who owns the robot and its data. Formalisation (B) keeps an independent life: where regulation forces explicit, explainable models, the rules have to be written down whatever the ownership model.

D+E. Who owns the robot, the service, the data, the control, and so who can own the optimisation

D. Who owns the robot, the service, the data, the control?

Q11Which ownership model is the buyer actually in, and what does each do to the three stalls?

Model	Who owns robot / software / data / control	What happens to the three stalls	Who can own the optimisation
Hardware purchase (the machine-tool model: industrial arms, bought AGVs)	Buyer owns the robot and nominally the data; vendor owns the controller software; control sits with the buyer's integrator.	Unchanged from the report. The robot is a forklift with no driver: it follows the path it was given. Data, trust and ownership stalls all recur. The report's subsidy finding applies directly: French and Chinese grants "buy hardware" (section 5), and Québec found "robots next to paper work orders" with 33 % not having recouped automation investment (section 6).	Nobody, by default. Integrator glue, Excel on top. The shelfware pattern with wheels.
Robots as a service (per pick, per hour)	Vendor owns robot, fleet software, telemetry and control; buyer owns the order feed and the outcome.	All three stalls are solved by moving them to the vendor. Data is the vendor's problem, trust is an SLA, maintenance is the vendor's. Price: the buyer never sees the model and cannot change its priorities except through the vendor.	The vendor, across all its customers. Fleet learning compounds. This is how robots reach SMEs; probably the only way.
Platform in-house (e-commerce, parcel, large grocers)	One owner for everything, including the OR team.	Matches the report's "OR is routine only in-house at scale" (section 0). Robots strengthen it: the platform that owns the robots, the data and the team compounds, then sells the whole stack outward to competitors who lack it.	The platform, and whoever it decides to sell to. Two verified cases: Amazon bought Kiva in 2012 and no new outside customer was announced afterwards, which opened the gap that 6 River Systems and others filled; Ocado sells its robotic warehouse to other grocers, including Kroger.
Split stack (robotaxi, autonomous trucks)	Vehicle owner, autonomy-stack owner and demand aggregator can be three companies. Control is the autonomy stack's; demand data is the aggregator's; the asset is the fleet owner's.	Dispatch and repositioning, the core OR problem, need demand data and vehicle control in the same place. They are not. So the optimisation is contested between the aggregator and the autonomy company, and the fleet owner is squeezed.	Whoever ends up holding both demand and control. In the Waymo and Uber case, Uber dispatches and runs the depots while Waymo keeps the driving stack (section 7).
Regulated semi-public (hospitals, public transport)	Hospital buys or leases; vendor controls software; data is patient-adjacent and GDPR-bound in Europe.	Vendor fleet learning across hospitals is legally hard in the EU. Explicit rules and local models are favoured. Ownership stays closer to the buyer, who, per the report, cannot staff it ("vrijwel overal nog met de hand", section 5).	Open question. The report's hospital finding (acute problem, weakest use) is likely to repeat with robots.

Q12Who can train on live feedback, and does that decide the market?

Classical OR models do not learn; a human re-parameterises them, which is the maintenance stall. A robot fleet produces plan, execution, measurement and correction in one loop, and whoever holds the telemetry can close it. In the service model that is the vendor, across every customer, and the buyer gets a model trained on other people's floors. In the in-house platform model it is the platform. In the hardware model nobody closes it. So yes: the right to train on live feedback is the moat, and it follows the control interface, not the purchase contract.

Q13Are there two optimisation layers with two different owners?

Probably. The slow layer (network design, layout, shift and capacity plans, daily batch) is the classical OR the report describes, and it can stay with the buyer or a consultant because it is advisory and infrequent. The fast layer (task allocation, traffic, charging, repositioning) is imperative and continuous and belongs to whoever controls the robots. The report's whole adoption pattern applies to the slow layer. The fast layer is the new market, and it is born inside the vendor.

Q14Where do the skilled humans go?

They do not vanish. The dispatcher moves from the depot to the vendor's remote operations centre, handling exceptions for many sites at once. The planner's tacit rules become a constraint-modelling deliverable. The 53 % of German logistics firms lacking dispatchers (section 0) is a pull for robots, not for OR software as such, and the OR arrives embedded, invisible to the buyer. Labour concentrates at the vendor. That is also where the interesting engineering jobs concentrate.

E. How ownership decides who can own optimisation solutions

Q15If the optimisation layer follows the control interface, what room is left for an independent optimisation company?

Three positions, in Claude's reading. First, the open-standard position: where a common fleet protocol separates robots from their master control, a third party can be the master control and sell planning across brands. Its moat is cross-vendor traffic data and the exception taxonomy, which no single robot vendor has. Second, the business-rules position: own the buyer's intent layer (priorities, customer constraints, what to do when an order is late) and feed it to whichever fleet is below. Its moat is being the only place the buyer's rules are written down, exactly the layer the report found to be the undisplaced one. Third, the slow-layer position: design and daily planning for buyers who have robots but no OR team, which is the report's consulting market, unchanged. Positions one and two are new and depend on standards and on the buyer's size; position three is the old market with a new customer.

Q16Does the report's size tiering survive, or do robots change who can afford optimisation?

It survives, with a twist. Large operators build in-house, as before, and now with more to build. SMEs get optimisation only embedded in a service, so they adopt more optimisation than before without ever owning any. The middle tier, the report's unhappy APS buyers, is where the choice is real: hardware purchase and repeat the stall, service and give up the data, or an orchestration layer they can barely staff. That middle tier is the market for position one and two above.

Q17What does "flexible robot" do to the Czech 2008 rule that APS pays off only where machines can make alternative products?

A fixed industrial arm on a line makes one product; the line is the schedule and there is nothing to optimise. A reconfigurable robot cell or a mobile manipulator can do many jobs, which makes every station capable of alternative products, which is precisely the condition under which the report's Czech source said scheduling pays off (section 3). Flexible robotics manufactures the demand for scheduling that fixed automation never had. Companies that build reconfigurable cells are therefore also, whether they know it or not, building a scheduling problem that someone has to own.

Q18Which domain already ran this experiment, and what happened to the optimisation vendor?

Mining. Autonomous haul trucks have run in large open-pit mines for over a decade, and fleet dispatch there was mature long before (the report's Chilean gran minería, section 5). It did not stay independent: Komatsu took control of Modular Mining, maker of the DISPATCH system, in 1996 and bought it outright in 2003, and now delivers autonomous haulage to Rio Tinto. The precedent for warehouses and roads is set: control buys optimisation.

flowchart LR
  A[Human executor: plan is advisory] --> B[Gap absorbed by planner and driver]
  B --> C[Report's three stalls: data, trust, ownership]
  D[Robot executor: plan is imperative] --> E[Gap must be closed in software]
  E --> F[Control interface required]
  F --> G{Who holds the interface?}
  G --> H[Robot vendor, RaaS: owns fleet optimisation and training data]
  G --> I[Platform in-house: compounds, then sells the stack]
  G --> J[Open standard: third-party planner possible]
  G --> K[Hardware only: nobody, stall repeats]

A. Is optimisation as important?

Q1When labour leaves the objective function, what is left to optimise?

With humans the business case was labour: a 14 % saving on driver hours (the Russian case in section 3). With robots labour cost is capex and the robots are fungible, so the business case becomes utilisation: how many picks per robot-hour, how many kilometres empty, how many minutes charging. That is a scheduling and dispatch problem and nothing else. Optimisation stops being a nice saving on top of an operation and becomes the operation's return on the capex. The objective moves to utilisation, energy, wear, congestion and safety, and the horizon moves from the overnight batch to the rolling minute.

Q2Does the robot vendor win by solving a hard planning problem, or by redesigning the physical system so the problem becomes easy?

Goods-to-person grids, fixed conveyor loops and single-robot restaurants all pick a physical architecture that makes the combinatorics trivial. A dense storage cube with simple robots can run on a plain heuristic. So part of the optimisation value is captured not by a better solver but by a layout decision made once, by the vendor, at design time. The report's cutting-stock finding is the small version of this: a 250 EUR product pays back on one order because the problem is bounded. Question for any robotics company: is its planning problem hard because the world is hard, or because the layout was chosen badly?

Q3Is optimality still the right target, or do robustness and stability take over?

The report records "solution instability under small data changes" as a trust problem (inventory row, section 5). With robots instability has physical consequences: a re-plan that reshuffles a hundred vehicles creates traffic jams and oscillation. So the quality criteria shift from gap-to-optimal toward latency, stability under perturbation, and graceful degradation when a robot stops. Classical MIP is still the right tool for the design and the daily batch; the second-by-second layer is heuristic, rolling-horizon, often learned. The two layers have different owners (question 13).

Q4At what fleet size does optimisation matter at all?

One serving robot in a restaurant, one porter robot in a hotel: no contention, no optimisation, appliance economics. Optimisation appears only where many robots share scarce resources (aisles, elevators, chargers, docks, road space). Hospitality is therefore an appliance market. Warehouses, yards, mines and robotaxi fleets are optimisation markets. Hospitals are in between: a handful of transport robots sharing elevators with beds is a small but real scheduling problem, while the hospital's actual OR problems (rostering, theatre scheduling, bed management) are untouched by the robot.

B. Is formalisation as important?

Q5Where does the tacit knowledge go when there is no planner to hold it?

The report's one undisplaced layer is "Excel plus a skilled planner" (section 3). A robot cannot compensate for a rule that was never written down. So every exception the human handled by judgement has exactly three places to go: into an explicit rule, to a remote human in an operations centre, or into a failure. The design of that exception path is the real product. Formalisation moves from "the planner's head" to "the vendor's exception taxonomy", and the Chinese buyer's guide rule from section 3 becomes literal: if constraint modelling is not an itemised deliverable, "all that work ends up yours", except that now the robots stand still until it is done.

Q6Does the robot formalise the world or learn it, and why does that decide ownership?

Two roads. Classical: the rules are written explicitly, the plan is auditable, the buyer can in principle own and edit the model. Learned: a foundation model for robots absorbs demonstrations and telemetry, handles unstructured situations without an explicit rule, and the "formalisation" lives implicitly in weights trained on fleet data. The second road is less work for the buyer and moves all the ownership to whoever holds the weights and the data. Which road wins is partly regional: the report shows co-determination and GDPR already forcing explainable rostering in Germany and France (section 0). The same forces push European robot deployments toward explicit, auditable optimisation, while platform operators in the US and China can run learned fleet policies on their own data. This is Claude's inference, not something the report measured.

Q7Does regulation make formalisation mandatory regardless of what the market wants?

Safety certification of machinery and of AI components requires a specification you can test against. A rule in a planner's head is not certifiable; a rule in code is. Robots in hospitals, on roads and next to workers therefore drag formalisation along by law, not by ROI. For the regional picture: this is the same regulation pull the report lists (NHS e-rostering mandate, CO2 data in Nordic tenders), now applied to the execution layer. The specific EU rules, including a correction on where the AI requirement now sits, are in section 7.

C. Is data integration different?

Q8Who is the integration layer once the planner is gone?

Today the planner is the integration: reads the ERP, reads WhatsApp, calls the customer, types into Excel. A robot needs machine-to-machine integration, in both directions, in real time: order system to warehouse system to fleet manager to robot, and back with completion events. That is harder, and it is where interface standards appear, because the buyer does not want each robot brand to bring its own control tower. The fight over whether fleets speak a common protocol or a proprietary one is the fight over who gets to plug in a planner (question 15).

Q9What does the robot's exhaust data do to the report's "nobody can say what the plan was worth"?

It ends it. Every task has a timestamp, a path and an outcome. The realised cost of a plan is known, so plans can be compared head to head without a year of parallel running (the airline validation story in section 5). This is the one stall robots remove outright, and it is the precondition for the closed loop in question 14. It also changes sales: the vendor can prove value in weeks, which the report's consulting model never could.

Q10Does integration get easier because the robot vendor brings a whole stack, or harder because the stack is closed?

Both, in sequence. First deployment is easier: the vendor brings perception, maps, fleet control and a dashboard, and the buyer's "integration" shrinks to an order feed. The second vendor is where it gets hard: two fleets, two maps, two control towers, no shared traffic management. The report's APS warning, "supplier tie-in created by platforms that require significant consultancy and customisation" (section 3), returns with a physical component. The buyer's defence is a standard or an in-house orchestration layer, which only the large tier can staff. Same tiering as the report.

4. Domain by domain

Domain	Which human the robot replaces	Optimisation problem it creates or sharpens	Who holds control today	Effect on the report's stalls
Robotaxi, autonomous ride-hail	Driver	Empty-vehicle repositioning, charging, dispatch under an operational-design-domain map; the bike-share rebalancing problem at city scale	Autonomy stack; demand data with the aggregator	Driver acceptance stall gone. Address-data stall becomes a map-coverage stall owned by the autonomy vendor. Dispatch ownership contested.
Sidewalk and last-mile delivery robots	Courier	Multi-robot routing with battery and sidewalk constraints, remote-operator staffing	Robot operator (usually also the fleet owner)	Low value per trip; the "only scheduled once a year" economics reversed. Remote humans are the new tacit layer.
Autonomous trucks and yard tractors	Driver, yard jockey	Hub-to-hub scheduling, yard slotting, hand-over between autonomous and human legs	Autonomy vendor for the stack; carrier for the network	The Rotterdam finding (80 % of the plan changes intraday, section 5) becomes a hard requirement for re-planning rather than a reason to keep a human.
Warehouse AMRs and grids	Picker, forklift driver	Task allocation, traffic, charging, slotting; the most mature new market	Fleet manager of the robot vendor; standards contested	All three stalls solved inside the vendor for its own fleet; recur at the second vendor. Monitoring stall removed.
Fixed industrial robots	Machine operator	None new. The line is the schedule.	Integrator	Report unchanged. Subsidised hardware next to paper work orders.
Flexible cells, mobile manipulators	Operator and material handler	Machine assignment and scheduling become real because every station can make alternatives (Q17)	Cell vendor's software; MES	Creates the APS demand that SMEs never had. Master-data stall (routings, standard times) is now blocking, not annoying.
Hospital logistics robots	Porter, transport staff	Elevator and corridor sharing, delivery scheduling; small fleets	Robot vendor; building systems	Integration becomes building-level. The hospital's main OR problems (rostering, theatres) are not touched. The report's hospital finding stands.
Hospitality robots	Server, room-service runner	Negligible at one to three robots	Vendor	Appliance market. Not an optimisation market.
Mining autonomous haulage	Haul-truck driver	Fleet dispatch, already mature	Truck OEM's autonomy and dispatch systems	The decade-old precedent: the truck OEM owns the dispatch system (Q18, section 7).
Agriculture (harvest, field robots)	Seasonal picker	Harvest timing, cold-chain and port slots, the Peruvian agro-export problem in section 5	Equipment OEM	Weak connectivity makes the exhaust-data advantage unreliable. Robots arrive before the data layer.

5. The regional pulls, replayed

Labour shortage (German dispatchers, Spanish drivers, Dutch and Spanish hospital staff, Québec manufacturers). In the report this pulls OR software. With robots available it pulls robots, and the OR comes embedded. The buyer never meets an optimisation decision. Expect the shortage regions to adopt more optimisation and own less of it.

Regulation (co-determination, GDPR, mandated rostering, CO2 in tenders). Pushes toward explicit, auditable models and away from learned fleet policies trained across customers. Europe is likely to keep more of the optimisation in the open, classical form, and therefore more of it ownable by buyers and independent vendors. The US and China platforms are likely to keep it learned and closed. Claude's inference.

State money (OP TAK, SIRI, ITTI, Chinese pilot cities, Spanish tenders). The report found subsidies buy hardware. That reproduces the hardware-purchase model from the table in Q11, the one model in which robots change nothing about the three stalls. A subsidy regime that funds robots but not the rules layer should produce exactly the Québec result: robots next to paper.

6. What this does to an industrial-agents thesis

A common thesis is agents as an operating system for manufacturing and logistics. The questions above sharpen it in four ways.

First, the "OS" is the control interface. The report shows the buyer could not maintain a model; the robot era question is whether the buyer gets to choose the model at all. A company that owns the interface between the buyer's rules and many vendors' fleets is the OS. A company that only owns one fleet is a vendor.

Second, robots are where the deeper-not-faster heuristic is satisfied by construction: a hard evaluator (the floor), a closed loop (plan, execute, measure), new data every hour. The report's planning market never had a hard evaluator; that was stall 2.6. So agentic optimisation that actually does previously impossible things is more likely in robot-executed operations than in human-executed ones.

Third, the screening question for any company in the space: who holds control, who holds the rules, who can train on the telemetry? If the answer to all three is the robot vendor, the optimisation work is inside the vendor. If a standard separates them, the interesting work is in the orchestration layer.

Fourth, the regional point: Europe's regulatory pull toward explicit models is a reason the classical OR skill set keeps its value here longer than in platform markets. That is an argument for Germany as a sweet spot, from a direction that thesis usually does not use.

Operations research in the field: LLMs, adoption and robots

LLMs and optimisation, sorted by what defines them

1. Four ways AI can advance OR adoption

2. The dimensions table

3. The four buckets, and China

3A. LLMs as the front end for standard OR

Lineage

Groups and people

The bets

Evidence, strongest first

3B. Algorithm discovery

Lineage

Groups and people

The bets

Evidence, strongest first

3C. Better solvers: ML and GPUs

Lineage

Groups and people

The bets, ordered by how much classical OR they keep

3D. Richer models of reality

Lineage

Groups and people

The bets

Evidence, strongest first

China

Why China has its own solver scene

People

Bets

Evidence

The contrast with the West, and what it means for a European lab

4. Across the buckets

Who has really tested any of this in business

The benchmark reality

People movements

5. Where the white space is

6. Sources

A. Front end

B. Algorithm discovery

C. Better solvers

D. Richer models of reality

China

History, one timeline

OR adoption: where it stalls and where it is ready

1. Bottlenecks by stage

1.1 Problem awareness: the buyer cannot name the problem

1.2 Will and budget: know-how and time are named before money

1.3 Data: the first stall, in every language

1.4 Implementation and integration: the messy process around the solver

1.5 Daily use and trust: parallel running, override, rollback to Excel

1.6 Monitoring and evaluation: nobody can say what the plan was worth

1.7 Keeping it alive: the champion leaves, IT will not maintain it, the consultants are gone

2. Matrix views

Matrix 3. Vertical × problem type

Matrix 4. Region × pull and readiness

Matrix 6. Buying path × region

3. What solving actually depends on

3.1 Monitoring infrastructure, hardware and software

3.2 Data integration, ingestion and silos

3.3 The action loop: open or closed

3.4 Simulate as well as optimise: a digital twin of the system

3.5 Accounting for uncertainty

3.6 Being able to write evaluators

3.7 Clear signals of real-world value

3.8 Summary table

3.9 Claude's opinion, not a dataset

4. Failure stories

5. By vertical and problem type

By vertical

6. By location: readiness signals

Is there a market much more ready for adoption, with clear signals?

7. Where buyers can be heard

8. Sources

Germany, Austria, Switzerland

Netherlands and Flanders

France and Québec

Spain and Latin America

Poland, Czechia and Slovakia, Russian-speaking space

Gulf, Egypt, Morocco

China

English baseline