Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

摘要

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.