Title: A regression model to identify supply chain cost drivers in healthcare and make cost predictions

Authors: Jean C. Essila

Addresses: Seidman College of Business, Grand Valley State University, 50 Front Ave. SW, Grand Rapids, MI 49504-6424, USA

Abstract: Supply chain (SC) is again in the news because of SC disruptions about to jeopardise the 2021 Christmas season in the USA. Although abundant literature on healthcare supply chain management (SCM) exists, most research projects focus on finding cost-reduction strategies. Little research exists on what actually drives cost increases in a specific segment of the healthcare SC. This study analyses SC costs in the primary care sector as an attempt to uncover SC actual cost drivers in that segment beyond the well-known traditional classification so that SCM professionals can prioritise their efforts in resolving one or two most important factors that account for a significant portion of the total cost in that important sector. Using regression analysis and a test of statistical significance, the study determined that in the healthcare sector, inventory (with a p-value of 0.0001), and not transportation (traditionally known as the largest SC cost in all industry SC), is the biggest SC cost driver in primary care SCM. The result might lead to a positive managerial practice change in that segment of the healthcare SCM.

Keywords: supply chain management; SCM; healthcare supply chain; inventory models; supply chain costs; cost drivers.

DOI: 10.1504/IJBIR.2024.143202

International Journal of Business Innovation and Research, 2024 Vol.35 No.4, pp.497 - 516

Received: 18 Nov 2021
Accepted: 20 Nov 2021
Published online: 09 Dec 2024 *

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