Assessing AI adoption for enhancing healthcare supply chain resilience: A novel hybrid interval-valued q-rung orthopair fuzzy MCDM

With ongoing market competitions, advancements in technology, and diverse products and services, supply chains (SC) have become increasingly sophisticated and complicated. The complexity of SC makes the ability to withstand the external uncertain changes critically important, which is known as SC resilience. Healthcare supply chains (HSC) are a critical subset facing similar or even more severe challenges. HSC resilience is the ability of the system to quickly adapt and recover from disruptions, thereby ensuring continuous healthcare product and service delivery. Recently various measures have been applied to enhance the resilience of SC. Among them, the adoption of artificial intelligence (AI) contributes most. However, the diversity of AI technologies requires an assessment of their contributions in enhancing SC resilience. To address this, this paper proposes a hybrid resilient assessment multi-criteria decision-making (MCDM) framework. The hybrid method combines the strength of interval-valued q rung orthopair fuzzy set (IVq-ROFS) in flexibly handling vagueness, the superiority of preference ranking organization method for enrichment evaluation (PROMETHEE) in ranking process, and the effectiveness of prospect theory in dealing with bounded rationality of decision-makers. Besides, a set of HSC resilience evaluation criteria are introduced from the perspective of technical contribution. In practice, the hybrid method is validated by identifying the impact of AI technologies on HSC resilience. Sensitivity analysis and comparison analysis are also conducted to prove the robustness and superiority of the method.