Optimising inventory management and collaborative supply chains: A robust data envelopment analysis-based approach

In supply chain management (SCM), a pivotal challenge is minimising inventory costs alongside enhancing efficiency among supply chain members. This paper proposes a two-phase supply chain planning model that leverages a centralised strategy and collaborative mechanisms. Phase I develops an inventory model based on the traditional EOQ framework, incorporating additional factors, including traffic congestion, sustainability, price, and shortage costs. The optimal solutions from Phase I are utilised in the inverse data envelopment analysis (InDEA) in Phase II to analyse the merging processes within a supply chain. The InDEA framework is further extended through a scenario-based robust model within the framework of multi-choice goal programming approach, incorporating decision-maker preferences and handling uncertainties. Our study demonstrates the effectiveness and applicability of our approach through a numerical experiment and an in-depth case study, revealing a significant reduction in costs and an enhanced overall customer experience, thus validating the proposed methodology's impact on supply chain efficiency.