Integration of capacitated lot sizing decision and supply chain network design model addressing sustainable and risk factors

Purpose This study aims to develop a multi-product, multi-period and multi-echelon joint capacitated lot sizing (CLS) and supply chain network design (SCND) model that addresses sustainability factors and supplier risk disruptions while effectively managing operational costs across the supply chain (SC) network. The model considers various issues, including carbon footprint, social factors, risk, inventory and multicontainer logistics. Design/methodology/approach This study is conducted in two phases. First, it calculates social and risk scores for suppliers using a synergetic application of the best-worst method (BWM) and technique for order preference by similarity to ideal solution (TOPSIS) methodologies. Second, these quantified scores are integrated into the proposed mixed integer linear programming model. To ensure the model’s robustness, a sensitivity analysis (SA) is performed by varying parameters such as social sustainability score, carbon footprint and risk. Findings In this study, gender equality has emerged as the most critical social factor, whereas fair wages rank lowest. In the risk dimensions, intellectual property risk emerges as the most significant, whereas social and political risks are found least critical. SA reveals that increasing social sustainability scores raises costs but lowers emissions and risks. Reducing carbon footprint initially decreases emissions but can increase costs beyond a threshold. Similarly, lowering risk scores results in higher costs but does not significantly impact social sustainability. The model effectively optimizes material flows, plant and warehouse selection and inventory levels while balancing economic, environmental and social objectives. Originality/value To the best of the authors’ knowledge, this is one of the few studies to propose a novel linear programming based joint CLS and SCND model that addresses sustainability (economic, social and environmental) and supplier-based risk factors simultaneously. The proposed mathematical model is relevant to policymakers and organizational managers in strategizing to mitigate various conflicting issues such as carbon footprint, social issues, risk and inventory costs while taking operational decisions.