Resource Reallocation for Improving Sustainable Supply Chain Performance: an Inverse Data Envelopment Analysis

In recent years, globalization has highlighted the vital role of sustainable supply chains (SSCs) in the development of organizations and production systems to increase profits. The high-performing SSCs not only increase the organizations’ profit but also guarantee responsiveness and sustainability in the networks. Thus, SSC performance evaluation is of cardinal importance in managing the entire network. Due to the importance of supply chain management, various versions of data envelopment analysis models have been introduced in the literature to evaluate supply chain performance. The inverse data envelopment analysis (IDEA) models have been employed to analyze the sensitivity of parameters and assess the risk of a system to changes in inputs and outputs. The IDEA model measures the impact of these changes on the remaining inputs and outputs by applying changes to one or more inputs or outputs, provided that the efficiency remains constant or improves. In this study, we develop a network IDEA model to evaluate SSCs performance considering the nature of network systems. This model considers different stages of an SSC network based on the importance and priority of each stage over the others. Then, a two-phase method is introduced to solve the proposed model to estimate the inputs and outputs while efficiency improves or remains unchanged. An important feature of the proposed model is to consider the relationships between the internal stages in the IDEA model. Furthermore, the proposed model guarantees the integer values for all parameters. The applicability of the IDEA model is demonstrated using a real case study.