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A Simulation Model Of Multi‐Echelon Retail Inventory With Cross‐Channel Product Returns
Author(s) -
Muir William A.,
Griffis Stanley E.,
Whipple Judith M.
Publication year - 2019
Publication title -
journal of business logistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.611
H-Index - 79
eISSN - 2158-1592
pISSN - 0735-3766
DOI - 10.1111/jbl.12209
Subject(s) - inventory management , business , product (mathematics) , channel (broadcasting) , industrial organization , perpetual inventory , channel coordination , inventory turnover , contingency , supply chain , inventory theory , marketing , supply chain management , economics , microeconomics , computer science , operations management , finance , profitability index , computer network , linguistics , geometry , mathematics , philosophy
This study explores the relationship between a retailer's product returns processing structure and Multi‐Echelon inventory system performance under cross‐channel and same‐channel product returns policies with nonstationary demand. Our research contributes to the growing body of reverse logistics literature, offering insight into how a retailer's logistical returns management strategy can impact inventory effectiveness in cross‐channel retail environments. Adopting a contingency framework, we develop research hypotheses which we test through experimentation on a Multi‐Echelon retail inventory system within a discrete‐event simulation. Model parameters are derived from data collected from a large U.S. retailer of consumer durable and nondurable goods. We find an amplifying effect of a cross‐channel returns policy on the positive relationship between a decentralized returns processing structure and inventory effectiveness. Further, through our analysis of nonstationarity in demand and resulting returns, we uncover strong main and interaction effects that seasonal demand variation can have on inventory outcomes, even under only moderate levels of seasonality. Our results highlight the need for firms to align logistical structures for returns processing with the returns policy and the external environment, while also lending credence to calls within the logistics literature for improved modeling of nonstationary demand in inventory management research.