Improving supply chain efficiency with geometric progression export policy and carbon emission mitigation: A comparative analysis of VMI-CS and conventional models

In today’s competitive marketplace, improving supply chain efficiency while balancing sustainability constraints has become a critical challenge. This study investigates the geometric progression-based export policy to improve supply chain performance in an integrated inventory model. Despite extensive investigation on inventory control, regular approaches usually fail to account for the combined effect of export techniques, sustainability constraints, and uncertainty on supply chain costs. This study examines two inventory control strategies: vendor managed inventory-consignment stock and the conventional approach, which aims to maximize profitability and manage demand variability and inventory cost uncertainties. It also integrates carbon emissions into sustainable supply chain practices. The demand rate is assumed to be an exponential function of the selling price and employs hexagonal fuzzy numbers to address uncertainty in inventory costs. Also, this work provides a significant and comprehensive integration of pessimistic and optimistic fuzzy numerical analysis on the stated geometric export policy approaches to manage uncertainties in the supply chain. A pattern search optimization technique is incorporated to assess the efficacy of the proposed non-linear dynamic export strategies. The study provides valuable insights for decision-makers by demonstrating how flexible inventory policies, coordinated shipments, and sustainable logistics strategies can improve overall profitability.