# Pom Data Analysis

> 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).

- Skill: `thedixitjain/pom-data-analysis` (Agent Skill)
- Install (CLI): `npx skillmds add thedixitjain/pom-data-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/pom-data-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/thedixitjain/pom-data-analysis

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# 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`](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
## Checklist

- [ ] Analytical: proofs complete (in e-companion), structural results + calibrated numerics + sensitivity
- [ ] Empirical: identification stated; placebo/robustness; SE clustering matches sampling
- [ ] Experiment: randomization, power, manipulation checks, effect sizes
- [ ] Data science: validation, leakage checks, operational value demonstrated
- [ ] Results expressed in decision-relevant operational magnitude
- [ ] Same-data disclosure prepared for the cover letter


## 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
```

---

**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Production-and-Operations-Management-Skills/skills/pom-data-analysis/SKILL.md`