Integrated DEMATEL-ML approach for implementing lean supply chain in manufacturing sector

Purpose - The manufacturing sector is witnessing the need to continuously improve overall performance by eliminating inefficiencies in the supply chain. The adoption of lean concepts to address wasteful or non-value-adding activities in the supply chain is crucial. This article determines key factors of lean supply chain management (LSCM) for continuous improvement in the manufacturing sector. Design/methodology/approach - The methodology comprises three steps. The first step identifies critical factors of LSCM in manufacturing from prior research and a series of expert consultations. Critical factors are identified and validated that industries can leverage to attain their lean goals. The second step uses the decision-making and trial evaluation laboratory (DEMATEL) method to determine the causal relationship among the factors. DEMATEL analysis categorizes factors into cause and effect, which will assist industry personnel in decision-making. The third step involves further data analysis to visualize the importance of the most critical factors. It develops a machine learning (ML) model in the form of a decision tree that helps in assessing the factors into cause or effect groups via a threshold value of expert ratings. Findings - IT tools, JIT manufacturing and material handling and logistics form the most critical factors for LSCM implementation. Originality/value - The analysis from DEMATEL and ML together will be beneficial for manufacturing practitioners to improve the supply chain performance based on the identified factors and their criticality towards LSCM implementation.