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How to Find the Right Supply Chain Strategy? An Analysis of Contingency Variables
Author(s) -
Falkenhausen Christian,
Fleischmann Moritz,
Bode Christoph
Publication year - 2019
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12355
Subject(s) - contingency , variety (cybernetics) , supply chain , business , product (mathematics) , demand forecasting , supply and demand , variable (mathematics) , set (abstract data type) , industrial organization , marketing , economics , computer science , microeconomics , mathematical analysis , philosophy , linguistics , geometry , mathematics , artificial intelligence , programming language
Contingency variables are characteristics of the business environment that influence the competitive priorities supply chains should pursue for maximizing profits. But which contingency variables should managers focus on when developing a supply chain strategy? On the one hand, if important variables are omitted, the selected strategy may fail to fulfill the needs of the business environment. On the other hand, considering irrelevant variables unnecessarily complicates the strategy formation process, hence preventing well‐suited strategies from being found. As a first step toward resolving this trade‐off, our study analyzes the effects hypothesized to be underlying a set of frequently cited contingency variables referred to as “DWV3” (product lifecycle Duration, delivery time Window, demand Variability, demand Volume, product Variety) as well as contribution margins. We test the hypotheses on archival data from a leading chemical manufacturer using multilevel regression. Our findings indicate that demand variability, the delivery time window, and contribution margins are important for strategy development because they indicate to what extent companies should invest in market mediation. Volume, variety, and lifecycle duration are less important for this purpose, but may instead be used for analyzing the causes of demand variability.