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Examining the Link between Retailer Inventory Leanness and Operational Efficiency: Moderating Roles of Firm Size and Demand Uncertainty
Ist Teil von
Production and operations management, 2019-09, Vol.28 (9), p.2338-2364
Ort / Verlag
Los Angeles, CA: SAGE Publications
Erscheinungsjahr
2019
Link zum Volltext
Quelle
EBSCOhost Business Source Ultimate
Beschreibungen/Notizen
Retail inventories have been consistently dropping, relative to sales, since the 1990s. Whether these lean inventory developments translate to better retailer operational performance is still an open question. We empirically examine associations between inventory leanness and operational efficiency for a sample of public US retailers from 2000 to 2013. Via a stochastic frontier analysis that accounts for retailer heterogeneity and time parameters, we find support for the hypothesis that operational efficiency has an inverted U‐shape relationship with inventory leanness, suggesting an optimal inventory leanness level beyond which retailer operational efficiency degrades. This relationship, however, is heavily moderated by firm size and demand uncertainty. The former reflects a retailer's abilities to exploit economies of scale and scope, whereas the latter reflects the unpredictability in a firm's operating environment. Our evidence suggests that when increasing inventory leanness, small retailers exhibit efficiency degradation, whereas larger retailers are likely to exhibit efficiency improvement, with diminishing returns. We also find that under high demand uncertainty, being less lean is associated with higher operational efficiency, regardless of firm size. The findings show that depending on firm size and demand uncertainty, retail managers should take special care when pursuing inventory leanness. As part of post hoc robustness tests, we assess how different retail categories vary in their operational efficiency scores and conduct interviews with retail executives who further ground our econometric investigation and point to more nuanced moderators for future studies. We conclude by discussing the implications of our industry model estimation for managers and researchers.