Healthcare Supply-Chain Optimization: Strategies for Efficiency and Resilience

Abstract

Effective supply-chain management (SCM) is crucial to the healthcare sector in order to guarantee the prompt delivery of necessary medications, medical equipment, and personal protective equipment (PPE).  The COVID-19 pandemic revealed weaknesses in international healthcare supply chains, such as inefficiencies, delays, and shortages that jeopardised operational resilience and patient safety (Govindan et al., 2020).  By reviewing the literature and suggesting methodological strategies that use advanced analytics, digital technology, and sustainability issues, this paper explores healthcare supply-chain optimisation.  The study identifies key issues such demand uncertainty, inventory mismanagement, procurement delays, and lack of visibility across multi-tier networks through a thorough literature assessment of 30 peer-reviewed papers published up to 2022 (Kumar et al., 2021). The approach uses a mixed-methods design that blends qualitative case-based evaluations with quantitative optimisation models.  Digital enablers like blockchain, the Internet of Things (IoT), and artificial intelligence (AI) are assessed alongside optimisation methods like mixed-integer programming, simulation modelling, and data-driven decision-support systems.  Research results demonstrate the advantages of blockchain for traceability, predictive analytics for demand forecasting, and green logistics for minimising environmental impacts (Marques & de Carvalho, 2020). With a focus on sustainability, efficiency, and resilience, the debate integrates theoretical and practical insights into a multifaceted framework for healthcare supply-chain optimisation.  The study comes to the conclusion that creating strong supply-chain systems requires a combination of collaborative governance, sophisticated technology adoption, and ongoing risk assessment.  The lack of real-time pandemic-era data and longitudinal case evidence are among the limitations.  Future directions call for a closer examination of circular economy models, global-local supply chain integration, and AI-driven platforms.