Strategic prioritization of sustainable Lean-Green initiatives in the farming industry using an integrated BWM-Fuzzy TOPSIS framework

Purpose This study proposes a structured decision-making framework for prioritizing sustainable Lean-Green (LG) initiatives in the farming sector. Given the increasing emphasis on environmentally responsible and efficient operations, the framework aims to guide decision-makers in aligning project selection with both sustainability goals and operational excellence. Design/methodology/approach A hybrid multi-criteria decision-making (MCDM) approach, combining the Best-Worst Method (BWM) and Fuzzy TOPSIS, was developed to rank LG project alternatives. Seven main criteria and thirty sub-criteria were identified through expert consultation and a comprehensive literature review. The proposed model was applied to evaluate five key production lines within a major Indian tractor manufacturing plant in the farming sector. Findings The analysis identified the tractor assembly line as the most suitable alternative for the LG project, demonstrating strong alignment with both sustainability and productivity objectives. The result affirms the model's robustness in addressing complex decision-making scenarios that involve multiple, often conflicting criteria. Research limitations/implications While the study offers a systematic and data-driven approach to sustainable project prioritization, it is constrained by its industry-specific context and static evaluation. Broader implementation may require sectoral adaptation and incorporation of dynamic decision parameters. Originality/value This study is among the first to propose an integrated BWM–Fuzzy TOPSIS decision-making framework for the strategic prioritization of sustainable Lean–Green initiatives in the agricultural equipment sector. By combining a consistency-driven weighting approach with a fuzzy ranking mechanism, the study addresses the complexity and uncertainty inherent in multi-criteria project selection, providing practical guidance for manufacturing organizations in emerging economies.