Masters Theses

Orcid ID

0000-0002-2426-9720

Date of Award

8-2021

Degree Type

Thesis

Degree Name

Master of Science

Major

Industrial Engineering

Major Professor

Mingzhou Jin

Committee Members

Mingzhou Jin, Ron D. Ford, Zhongshun Shi

Abstract

Inventory record inaccuracy (IRI) often arises in retail environments due to unaccounted stock loss. Theft, misplacement, spoilage, and transaction errors will reduce the true inventory values without changing the inventory record. As previous inventory replenishment policies assume perfect record accuracy, increasing IRI can cause unexpected stockout events, mistimed reorders and replenishment freezes. Solutions to rectifying IRI vary from the use of improved tracking technologies to prevent it initially occurring at all to recounting programs which estimate true inventory value. Unfortunately, in retail environments, high‑tracking technology is unsuitable and continuous counting programs are too costly. To address the limitations of current solutions, we offer a Periodic Replenish and Recount Policy (PRRP) which accounts for stochastic stock loss and minimizes total costs including recounting. The theoretical foundation of PRRP allows for the discovery of both an optimal order quantity as well as optimal count frequency for a given inventory system. We find that in instances of stochastic stock loss, PRRP balances the trade-offs between shortage, surplus and counting costs.

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