| name | pom-data-analysis |
| description | Use when executing and reporting the analysis for a Production and Operations Management (POM) manuscript — proving and numerically illustrating an analytical model, or estimating and validating an empirical / behavioral / operations-data-science study. Executes and reports; it does not pick the method (pom-methods) or frame the contribution (pom-contribution-framing). |
Analysis & Results (pom-data-analysis)
When to trigger
- The model is built or the data are collected and it is time to produce results
- You are unsure your numerics, identification, or validation will satisfy reviewers
- A reviewer says "the analysis does not support the inference" or "magnitude is unclear"
Analytical / modeling papers (POM's anchor track)
For optimization, stochastic, and game-theoretic work, the "analysis" is proof plus numerical illustration:
- Proofs. State each result as a numbered proposition/theorem; give clean, complete proofs. Per POM's format, push full proofs and supporting lemmas to the unlimited online e-companion, leaving intuition and the key steps in the main text.
- Structural insight. Report the structure of the optimal policy (base-stock, threshold, (s, S)) and comparative statics — how the decision moves with cost, lead time, or competition.
- Numerical study. Calibrate to realistic operational parameters; report sensitivity across plausible ranges; show the managerial magnitude of the effect, not just its sign.
- Game-theoretic checks. Confirm equilibrium existence/uniqueness; report off-equilibrium robustness where relevant.
Empirical, behavioral, and data-science papers
- Identification (empirical OM). Make the causal logic explicit; report the design (DiD/IV/RD/matching), parallel-trends or instrument validity, placebo tests, and clustered/robust standard errors matched to the operational sampling.
- Experiments (behavioral OM). Report randomization checks, power, manipulation and attention checks, and effect sizes; tie the result to the operational decision (e.g., order quantity, not just a rating).
- Operations data science. Report validation design, guard against leakage, and — decisively — the operational value: does the prediction improve a feasible policy or reduce a real operating cost (predict-then-optimize)?
- Simulation. Document parameter sources, seeds, warm-up, replications with confidence intervals, and sensitivity.
POM-specific reporting risks
- Operational variables named but measured in units a manager cannot act on.
- Statistical significance reported in place of managerial magnitude.
- ML accuracy reported with no link to an operations policy or cost.
- Same data used in prior work without the required cover-letter disclosure.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. 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.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
- Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the
executed chain in the JF execution walkthrough.
Checklist
Evidence pass for Production and Operations Management
Treat this skill as an executable review pass, not a prose hint. First lock the operational decision, the performance metric, and the implementable lever; then judge whether the current manuscript answers the venue's real reader: POM reviewers who want operational insight tied to production, service, supply-chain, or platform decisions.
- Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
- Return a ledger: give
claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
- Sibling guard: compare against Management Science for broader OR/MS theory, Operations Research for method-first optimization, MSOM for manufacturing/service operations depth; if a sibling owns the contribution, recommend re-routing before polishing format.
- Stop condition: do not give submission-ready advice until the pack's
resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.
Output format
【Analysis type】analytical-proof / causal / experiment / simulation / predictive
【Core result】policy structure / estimate / treatment effect / decision gain
【Main threat】proof gap / identification / leakage / measurement / power
【Managerial magnitude】effect in operational units (cost, fill rate, wait time)
【e-companion】proofs / extra analyses moved online
【Next step】pom-contribution-framing
