Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster

Arnab Adhikari, Raunak Joshi and Sumanta Basu

International Journal of Production Research, 2025, vol. 63, issue 2, 497-523

Abstract: Artificial Intelligence (AI)-based applications have been rising across the healthcare supply chain. Successful AI implementation requires proper collaboration among healthcare supply chain members. However, high investment associated with AI innovation often impedes collaboration. In this context, disruption caused by the disasters like pandemics and epidemics can add more complexity. This issue has not received substantial scholarly attention. Here, we design a multi-level AI-enabled healthcare supply chain by incorporating a three-level supply chain structure with a healthcare product manufacturer, a distributor, and a procurement agency, where the manufacturer and distributor invest in AI innovation. Here, we adopt wholesale price (W) and cost-sharing (C) contracts-based mechanisms considering four scenarios WW, WC, CW, and CC, to devise the three-level AI-enabled healthcare supply chain members’ collaboration and coordination strategies with and without disruption. Adopting a Stackelberg game-theoretic approach, we determine the supply chain members’ optimal AI innovation efforts, prices, and profits for all scenarios. We demonstrate the dominance of one scenario over other for the supply chain members’ decisions and profits and propose a scenario ranking framework. We also investigate the impact of the disruption cost-sharing between the manufacturer and retailer, the disruption probabilities, and AI innovation success on the supply chain decisions

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2023.2252933 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:63:y:2025:i:2:p:497-523

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2023.2252933

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().