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A discrete event simulation model for coordinating inventory management and material handling in hospitals

Amogh Bhosekar (), Sandra Ekşioğlu (), Tuğçe Işık () and Robert Allen ()
Additional contact information
Amogh Bhosekar: Clemson University
Sandra Ekşioğlu: University of Arkansas
Tuğçe Işık: Clemson University
Robert Allen: Prisma Health

Annals of Operations Research, 2023, vol. 320, issue 2, No 4, 603-630

Abstract: Abstract Inventory management of surgical instruments and material handling decisions of perioperative services are critical to hospitals’ and operating rooms’ (ORs) service levels and costs. However, efficiently coordinating these decisions is challenging due to their interdependence and the uncertainties faced by hospitals. These challenges motivated the development of this study to answer the following research questions: (R1) How does the inventory level of surgical instruments, including owned, borrowed and consigned, impact the service level provided by ORs? (R2): How do material handling activities impact the service level provided by ORs? (R3): How do integrating decisions about inventory and material handling impact the service level provided by ORs? Three discrete event simulation models are developed here to address these questions. Model 1, Current, assumes no coordination of material handling and daily inventory management operations. Model 2, Two Batch, assumes partial coordination, and Model 3, Just-In-Time (JIT), assumes full coordination. These models are verified and validated using real life-data from a partnering hospital. A thorough numerical analysis indicates that, in general, coordination of inventory management of surgical instruments and material handling decisions has the potential to improve the service level provided by ORs. More specifically, a JIT delivery of instruments used in short-duration surgeries leads to lower inventory levels without jeopardizing the service level provided.

Keywords: OR in health services; Simulation; Inventory management; Automated guided vehicles; Data analytics (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-020-03865-5

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