Archive | January 2018

Three Types of SCM Definitions

Today, I present Mentzer et al.’s (2001) must-read article, Defining Supply Chain Management. The authors demonstrate that, “although definitions of SCM differ across authors […], they can be classified into three categories”: (1) SCM as a management philosophy (= supply chain orientation), which involves a systems approach to viewing the supply chain as a whole, a strategic orientation toward cooperative efforts, and a customer focus; (2) SCM as an implementation of a management philosophy, which involves seven activities such as “mutually sharing information”; and (3) SCM as a set of management processes, which includes processes such as “customer relationship management” and “order fulfillment”. The article also contains a useful definition of SCM as “the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole”.

Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D. & Zacharia, Z.G. (2001). Defining Supply Chain Management. Journal of Business Logistics, 22 (2), 1–25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x

Five Ways to Fix Statistics in Supply Chain Research

The P value debate has revealed that hypothesis testing is in crisis – also in our discipline! But what should we do now? Nature recently asked influential statisticians to recommend one change to improve science. Here are five answers: (1) Adjust for human cognition: Data analysis is not purely computational – it is a human behavior. So, we need to prevent cognitive mistakes. (2) Abandon statistical significance: Academia seems to like “statistical significance”, but P value thresholds are too often abused to decide between “effect” (favored hypothesis) and “no effect” (null hypothesis). (3) State false-positive risk, too: What matters is the probability that a significant result turns out to be a false positive. (4) Share analysis plans and results: Techniques to avoid false positives are to pre-register analysis plans, and to share all data and results of all analyses as well as any relevant syntax or code. (5) Change norms from within: Funders, journal editors and leading researchers need to act. Otherwise, researchers will continue to re-use outdated methods, and reviewers will demand what has been demanded of them.

Leek, J., McShane, B.B., Gelman, A., Colquhoun, D., Nuijten, M.B. & Goodman, S.N. (2017). Five Ways to Fix Statistics. Nature, 551 (2), 557-559. DOI: 10.1038/d41586-017-07522-z

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