Artificial Intelligence applications for responsive healthcare supply chains: a decision-making framework - Virmani, Naveen; Singh, Rajesh Kumar; Agarwal, Vaishali; Aktas, Emel (Institute of Electrical and Electronics Engineers (IEEE), 2024)
Post-COVID-19, the healthcare sector is extensively digitalizing operations by applying emerging technologies such as Artificial Intelligence (AI). It is evident from previous research that the adoption of AI in the healthcare supply chain results in unmatched benefits. Therefore, the present study identifies the enablers for adopting AI in healthcare supply chains and validates them using the Fuzzy-Delphi technique. Furthermore, enablers are prioritized, and the dyadic connections between them are investigated using the fuzzy-DEMATEL approach. In the last phase, the Graph Theory Matrix Approach was applied to assess the readiness of a case organization to adopt AI across various healthcare functions. The sensitivity analysis confirms the reliability of the results. Competitive and mimetic pressures, together with government policies and support, were the most influencing enablers. Furthermore, Scalability and Traceability of information flow across the healthcare supply chain are found to be the most influenced factor by other enablers.
Keywords: Artificial intelligence (AI); Healthcare Supply Chain Management (HSCM); Enablers; Responsiveness; Fuzzy-Delphi; Fuzzy-DEMATEL; Graph Theory Matrix Approach (GTMA)