--- name: msom-data-analysis description: Use when executing and reporting the analysis for a Manufacturing & Service Operations Management (M&SOM) manuscript — proving structural results and running numerical studies for analytical work, or estimating and stress-testing identified effects for empirical work, plus meeting M&SOM's data-and-code replicability policy. Executes the analysis designed in msom-methods. --- # Analysis & Replicability (msom-data-analysis) ## When to trigger - Proofs are drafted and you need a numerical study that earns its keep - Estimates exist and you must defend identification and robustness - A reviewer says "the result is an artifact of the assumptions/specification" - You are preparing the data and code disclosure M&SOM requires ## Analytical work: proofs plus a disciplined numerical study M&SOM's dominant tradition is **analytical/stochastic modeling**, so rigor is judged first on the **proofs** and then on a numerical study that does real work: - **Proofs**: state results as propositions/theorems; full proofs go in the online supplement (≤ 16 pages for a new submission). Verify monotonicity/convexity/threshold or base-stock structure rigorously. - **Numerical study**: use realistic parameter ranges; quantify the *magnitude* of the structural effect, the value of the proposed policy versus benchmarks/heuristics, and the cost of relaxed assumptions. Report instance generation, seeds, and solver/settings so results reproduce. - **Sensitivity**: show which assumptions bind and how conclusions move as primitives (demand variability, lead time, cost ratios) change. ## Empirical work: identified effects, not correlations - Defend the **identification** designed in `msom-methods`: parallel-trends/event-study evidence for DiD, first-stage strength and exclusion for IV, density/covariate-balance for RDD, model fit and identification for structural estimation. - Treat operational decisions (capacity, inventory, staffing, routing) as **endogenous**; address selection and simultaneity explicitly. - **Robustness**: alternative specifications, samples, and operational measures; cluster standard errors at the operational unit; report effect *magnitudes* in operational terms (units, hours, dollars), not just significance. ## Replicability policy (M&SOM / INFORMS) Manuscripts must contain enough detail and references to **permit replication**. You may be asked to provide raw data for editorial review, must be prepared to share it, and must **retain it** for a reasonable time after publication. For **licensed data** (Census, Compustat, CRSP, FactSet, WRDS) provide your **own code plus detailed access/linking instructions** so others can replicate without your redistributing the data. Any reuse of shared data/code beyond replicability verification must **cite the paper and acknowledge the source**. ## Checklist - [ ] Analytical: results stated as propositions; full proofs in the ≤16-page supplement - [ ] Numerical study with realistic ranges; magnitudes vs. benchmarks; seeds/settings reported - [ ] Empirical: identification defended (trends/first-stage/balance); endogeneity addressed - [ ] Robustness across specifications/samples/measures; SEs clustered at the operational unit - [ ] Effects reported in operational magnitudes, not p-values alone - [ ] Data/code disclosure prepared; licensed-data access instructions included; retention noted ## Anti-patterns - "Numerical results show…" with no benchmark comparison or assumption stress-test. - Proofs in the main text crowding out the operational insight (move them to the supplement). - Treating capacity/inventory/staffing as exogenous in an empirical design. - Reporting significance with no operational magnitude. - Promising replication while withholding code/access instructions for licensed data. ## Worked micro-example (illustrative) Referees grade whether the analysis converts a model or dataset into a defensible operational claim: the analytical lane wants a structural result, a benchmark-anchored magnitude, the binding assumption stress-tested, and seeds/solver settings; the empirical lane wants an identified effect in operational units with clustered SEs, not stars alone. Vignette: a data-calibrated inventory policy for an omnichannel retailer's fulfillment center, where ship-from-store substitutes for warehouse stock. Suppose the dual-index policy, benchmarked against the firm's base-stock rule, cuts expected backorders by 22% at a 1.3% inventory increase across 480 SKU-weeks (numbers illustrative). The discipline: make the 22%/1.3% trade the headline a manager weighs, not "the policy is optimal"; show the gain shrinks to an illustrative 9% once cross-store lead-time correlation is added, naming the assumption that carries the result; and report the fitting window, seed set, and solver tolerance so a referee could regenerate the table. ## Referee-pushback patterns and the venue fix - *"The result is an artifact of the assumptions."* → Add the sensitivity panel that varies the binding primitive, show where the conclusion flips, and state the operational regime in which it holds. - *"Capacity/inventory/staffing is treated as exogenous."* → Instrument it, exploit a natural experiment, or model the choice; an OM empirical paper that ignores operational endogeneity is rarely identified. (For Census/Compustat/CRSP/WRDS data, ship your own code plus access instructions; confirm the current data-and-code requirement against the journal's author guidelines.) ## Output format ``` 【Analytical】propositions proven; supplement proofs: yes/no 【Numerical study】ranges / benchmarks / magnitude / seeds ... 【Empirical identification】trends / first-stage / balance ... 【Robustness】specs / samples / measures; SE clustering ... 【Replicability】data+code disclosure; licensed-data access instructions ... 【Next step】msom-contribution-framing ```