pom-methods
GitHub用于在运营管理论文中根据研究问题匹配分析方法（如优化、实证、实验等），确保方法严谨且契合管理贡献，避免方法不当或偏离运营主题。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pom-methods -g -y
SKILL.md
Frontmatter
{
"name": "pom-methods",
"description": "Use when selecting or auditing the method for a Production and Operations Management (POM) manuscript — analytical modeling (optimization, stochastic models, game theory), empirical identification, behavioral experiments, simulation, or operations data science. Matches method to the operations question; it does not execute the analysis (pom-data-analysis)."
}
Method Fit (pom-methods)
When to trigger
- The method may not match the operations question (level, uncertainty, causality)
- You must choose between an analytical model, an empirical design, an experiment, or a data pipeline
- A reviewer says "the method does not support the claim" or "this is a methods paper, not OM"
POM mandates no single method — but the method must fit and be rigorous
POM explicitly places no restriction on research methods, while being historically anchored in analytical modeling. Pick the method the question demands, and route to the matching Department.
| Operations question / claim | Method |
|---|---|
| Optimal policy under cost/service objective | Optimization (LP/MIP/convex, dynamic programming); characterize the policy |
| Decisions under demand/lead-time uncertainty | Stochastic modeling, queueing, inventory theory, MDP/ADP |
| Strategic interaction (suppliers, competitors, platforms) | Game theory (Nash/Stackelberg); prove equilibrium existence/uniqueness |
| Causal operational effect from field data | Empirical OM: DiD, IV, RD, matching with a clear identification strategy |
| Human operational decision bias | Behavioral/experimental OM (lab/online); randomization, manipulation checks |
| Systems too complex for closed form | Discrete-event simulation; validation, warm-up, replications, CIs |
| Prediction feeding an operational decision | Operations data science (ML / forecasting), tied to a decision/loss |
What each track must defend
- Analytical: assumptions grounded in operations reality; solution concept; structural results; sensitivity/comparative statics; managerial interpretation. Robustness = relaxing key assumptions, not just numerical examples.
- Empirical OM: identification (what makes the effect causal), measurement in decision-relevant units, unit of analysis, external validity to other operations settings.
- Behavioral OM: design, incentives, randomization, manipulation and attention checks, and operational realism (does the lab task map to a real operations decision?).
- Operations data science: validation design, leakage checks, and — critically — the operational value: does a better prediction change a feasible policy (predict-then-optimize)?
- Simulation: parameter provenance, validation against known cases, sensitivity, and a replication package.
The POM bar on method
A method exists to serve an OM contribution judged interesting to practicing managers. If the paper's value is mainly a technical advance with thin OM decision content, a methods journal may fit better. Keep heavy derivations, solver details, and extra robustness for the unlimited online e-companion so the 32-page main document stays focused.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. POM spans analytical and empirical OM; apply the chain below to its empirical-OM papers, and note when a contribution is analytical / optimization.
detect_design→recommend→ fit withas_handle=true→audit_resultto enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna/sun_abraham+bacon_decompositionhonest_did_from_result. IV:effective_f_test+anderson_rubin_ci. RDD:rdrobust+mccrary_test.
- Experiments: randomization-based inference and
romano_wolffor the many-outcome family-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Checklist
- Method matches the operations question and the target Department
- Analytical: solution concept + structural results + assumption-relaxing robustness
- Empirical: identification strategy and decision-relevant measurement stated
- Behavioral: randomization, manipulation checks, operational realism
- Data science: validation, leakage, and decision/operational value
- Derivations/extra material slated for the e-companion
Methods pass for Production and Operations Management
Use this as a second-pass capability check. First lock the operational decision, the performance metric, and the implementable lever; then test whether the manuscript addresses POM reviewers who want operational insight tied to production, service, supply-chain, or platform decisions.
- Primary move: Name assumptions, diagnostics, robustness, falsification, and failure modes; do not accept a method section that hides the decisive validity threat.
- Decision ledger: return
claim / evidence / blocker / next editrows so the next pass can patch the manuscript directly. - Neighbor test: compare against Management Science for broader OR/MS theory, Operations Research for method-first optimization, MSOM for manufacturing/service operations depth; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
- Verification floor: before submission-ready advice, re-open
resources/official-source-map.mdfor volatile rules and name the one unresolved fact that could change the recommendation.
Output format
【Method family】optimization / stochastic / game-theory / empirical / behavioral / simulation / data-science
【Operations question】<decision problem>
【Validity risks】assumptions / identification / measurement / leakage / validation
【Practice tie】how the method yields a manager-usable result
【e-companion plan】proofs / extra analyses to move online
【Next step】pom-data-analysis
Version History
- 1839142 Current 2026-07-05 14:13
