Healthcare Logistics Optimization Framework for Efficient Supply Chain Management in Niger Delta Region of Nigeria

Abstract

Optimizing logistics allocation and utilization is essential for effective healthcare management. Apparently, less consideration is given to it in most hospitals in Nigeria where less resources are allocated to health sector in yearly budgetary. Hospital consists of several patient classes, each of which follows different treatment process flow paths over a multiphase and multidimensional requirement with scarce resources and inadequate space. Despite the small budget provision made for healthcare resources, patient’s demands for better service is rapidly experiencing upsurge. Hence, efficient and optimal solutions are required to lessen costs of healthcare service towards enhancing Quality of Care (QoC) and Quality of Experience (QoE) in most public healthcare sector. However, certain control coefficients like the absence of a Dedicated Logistics Department (DLD) in the medical facilities actually limit the efforts of stakeholders. This paper proposed a Computational framework to assess various strategic and operational decisions for optimizing the multiple objectives using Type-1 Fuzzy Logic Model. In phase I, we explore healthcare resource allocation plan. In phase II, we determine a resource utilization schedule by patient class for daily operational level. While in Phase III, we develop a framework capable of evaluating and optimizing healthcare logistics using control coefficients of Logistics Optimization (LO), Integration of Information/Cognitive Technologies (ETA), and Collaboration of all Logistics Stakeholders (COL). We assigned weights between 1 and 10 to the coefficients and modeled the effects on efficient supply chain. Finally, we further explore the effects of separate strategies and their combination to identify the best possible resource supply chain. The computational experiment was considered on the basis of data obtained from a study conducted on a typical public healthcare department. Results shows that our approach significantly evaluate and optimized healthcare logistics.