Designing a new bi-objective resilient supply chain network with additive manufacturing capability and smart contracts

Supporting managerial choices about demand fulfillment, cost control, and inventory control in supply chains requires effective risk management. This study creates a bi-objective optimization model that aims to reduce a supply chain network’s overall cost while also increasing its resilience by lowering unmet demand in the event of disruptions. The model integrates Additive Manufacturing (AM) as a versatile tool to reduce disruptions through on-demand production of critical parts and multi-level decision-making among manufacturers, distributors, and suppliers. The framework examines inflation-related exchange rate risks under three scenarios in order to address financial uncertainty. Blockchain-based smart contracts further improve supplier selection and transparency. The model uses the Benders decomposition algorithm to solve the resulting mixed-integer program efficiently, the ε-constraint method to handle the bi-objective formulation, and robust optimization to manage risk. A household supply chain network case study shows how integrating AM greatly increases resilience and cost effectiveness. When designing resilient networks, the results emphasize how crucial it is to take risk factors, multi-level supply chain structures, and technological enablers into account simultaneously.