Skill UI

A comprehensive, step-by-step checklist designed for authors preparing manuscripts for submission to Manufacturing & Service Operations Management (M&SOM). It covers crucial elements including double-anonymous anonymization, strict formatting rules (32-page cap, structured abstract), proper departmental routing, and data/code disclosure requirements, ensuring readiness for the scholarOne portal submission.

A comprehensive guide for structuring and presenting complex analytical and empirical results for Operations Management journals (M&SOM). It emphasizes strict formatting rules, adhering to a hard page limit, and ensuring that all exhibits are self-contained and immediately convey the core insight (e.g., plotting policy structures, highlighting operational magnitudes, and using automated tools for generation).

A guide for developing rigorous analytical or stochastic models for Manufacturing & Service Operations Management (M&SOM). It emphasizes translating operational decisions, objectives, and uncertainties into explicit structural results, such as policy forms (e.g., order-up-to levels) and comparative statics, ensuring the core insight remains operational rather than merely descriptive.

This guide provides a critical framework for academic authorship targeting top-tier Operations Management journals. It instructs researchers on how to centralize the operational decision, select the appropriate analytical or empirical 'lane,' and correctly map the research question to a specific editorial department, ensuring the work meets the journal's core scope before any modeling or data analysis begins.

This guide serves as a comprehensive router for developing an Operations Management manuscript intended for M&SOM. It outlines the entire academic writing lifecycle, from initial topic selection and theoretical development through data analysis, contribution framing, writing style refinement, and final submission/rebuttal. It ensures that the core operations decision remains central to the contribution.

This guide helps researchers determine if their problem constitutes a rigorous Operations Research (OR) or Management Science (MS) methodological contribution. It provides a comprehensive fit test covering method novelty, mathematical rigor (proofs, guarantees), and significance to the OR community. Furthermore, it guides users on selecting the precise editorial area (e.g., Optimization, Stochastic Models, Simulation) for submission.

This skill guides agents through comprehensive resource and filesystem operations within the OpenViking CLI. It covers the entire data lifecycle, including adding, importing, exporting, backing up, restoring resources, and performing standard filesystem actions (e.g., list, find, write, delete) under the `viking://resources/` namespace. Use this when the user needs to manage context, search data, or execute complex command sequences.

This skill guides authors to structure and articulate the core contribution of a Production and Operations Management (POM) manuscript. It ensures that the research explicitly addresses both a knowledge gap (theory) and a practice gap (operational levers), helping bridge the gap between technical results and actionable managerial insight required by top academic venues.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

A comprehensive guide for positioning Operations Research manuscripts within the Production and Operations Management (POM) academic landscape. It helps authors compare their work against canonical departmental literature, adjacent top-tier journals (e.g., M&SOM, OR), and rigorously define axes of novelty (setting, mechanism, method). Useful when determining the optimal venue or clarifying the paper's unique contribution.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

This comprehensive checklist guides authors through the final, critical steps before submitting a scholarly article to Production and Operations Management (POM). It ensures strict adherence to formatting rules (e.g., 32-page limit, 1.5 spacing), double-blind anonymization, proper departmental routing, and mandatory disclosures (such as same-data usage). Using this preflight check maximizes the likelihood of a smooth and compliant submission process.