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The Effect of Distribution Processes on Replenishment Lead Time and Inventory
Author(s) -
Bradley James R.
Publication year - 2017
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12775
Subject(s) - upstream (networking) , residual , supply chain , aggregate (composite) , lead time , computer science , markov chain , downstream (manufacturing) , distribution (mathematics) , distribution center , sequence (biology) , process (computing) , operations research , operations management , economics , mathematics , algorithm , business , marketing , computer network , mathematical analysis , materials science , machine learning , commerce , biology , composite material , genetics , operating system
We investigate the interrelationship of distribution center picking policies and supply chain inventory performance. In particular, we show how the picking sequence in the upstream supply chain link affects the inventory levels of items being replenished to a downstream link for a common “ship‐when‐full” trailer dispatching policy. Perturbing the picking sequence affects items’ inventory levels asymmetrically which causes the aggregate system inventory to vary. We separate the items in replenishment into those units in transit and those awaiting shipment from the upstream distribution step: we call the latter the residual replenishment . We show that the process governing aggregate residual replenishment is Markov and has a stationary distribution that is discrete uniform. Computing the item‐level residual distribution is intractable and so we develop analytical models from which we derive hypotheses for the effectiveness of stable vs. random picking sequences, how item residual replenishment varies with stable picking sequences, and how the aggregate inventory level changes with picking sequence. These suggest a heuristic sequencing algorithm for minimizing inventory, which performs well in simulation tests over a large testbed of parameter sets.