TY - JOUR
T1 - On perishable inventory in healthcare
T2 - random expiration dates and age discriminated demand
AU - Dalalah, Doraid
AU - Ojiako, Udechukwu
AU - Chipulu, Maxwell
N1 - Copyright © 2020 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of Simulation on 21 December 2020, available at: http://www.tandfonline.com/10.1080/17477778.2020.1851614.
PY - 2022/9/30
Y1 - 2022/9/30
N2 - The aim of the study is to explore how best to mitigate against inventory volatility in perishable inventory, which is characterized by random premature expiration, random demand, irregular supply, age differentiated demand and custom replenishment guidelines. Through the adoption of simulation-optimization along with new settings and replenishment policies, the optimized quantity level of daily orders could be determined for this combination of inventory restrictions. Owed to their custom medical compatibility guidelines, and their notable accelerated expiration, blood platelets were considered here. As study outcome, the emergent model presents a perspective of supply chains and their healthcare imperatives that will enable healthcare supply chain managers not only to discern, but also to interpret and facilitate the management and implementation of optimal inventories.
AB - The aim of the study is to explore how best to mitigate against inventory volatility in perishable inventory, which is characterized by random premature expiration, random demand, irregular supply, age differentiated demand and custom replenishment guidelines. Through the adoption of simulation-optimization along with new settings and replenishment policies, the optimized quantity level of daily orders could be determined for this combination of inventory restrictions. Owed to their custom medical compatibility guidelines, and their notable accelerated expiration, blood platelets were considered here. As study outcome, the emergent model presents a perspective of supply chains and their healthcare imperatives that will enable healthcare supply chain managers not only to discern, but also to interpret and facilitate the management and implementation of optimal inventories.
KW - blood platelets
KW - differentiated demand
KW - optimisation
KW - perishable inventory
KW - random ageing
KW - simulation
UR - https://eprints.soton.ac.uk/445085/
U2 - 10.1080/17477778.2020.1851614
DO - 10.1080/17477778.2020.1851614
M3 - Article
AN - SCOPUS:85097905970
SN - 1747-7778
VL - 16
SP - 458
EP - 479
JO - Journal of Simulation
JF - Journal of Simulation
IS - 5
ER -