Research on Inventory Sustainable Development Strategy for Maximizing Cost-Effectiveness in Supply Chain

• Published in: Sustainability 2024-06, Vol.16 (11), p.4442
• Main Authors: Tao, Sunyu, Liu, Shenglin, Zhou, Huijie, Mao, Xiaoyan
• Format: Article
• Language: English
• Subjects: Algorithms; Carbon; Consumers; Consumption; Cost analysis; Cost control; Cost reduction; Decision making; Efficiency; Electronic commerce; Environmental protection; Forecasts and trends; Inventory; Inventory control; Inventory management; Logistics; Machine learning; Mathematical optimization; Operations management; Optimization; Order quantity; Profits; Retail industry; Retail stores; Sensitivity analysis; Suppliers; Supply chain management; Sustainable development
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su16114442

This study addresses the challenges of the expanding global online retail market by developing an innovative inventory management strategy that promotes sustainable development. We propose an integrated planning model that utilizes a newly enhanced Parallel Chicken Swarm Optimization (PCSO) algorithm alongside the established Economic Order Quantity (EOQ) model and the (s, S) inventory policy. Our model demonstrates its effectiveness through simulation experiments, showcasing its ability to dynamically adjust inventory thresholds based on daily forecasted demand, which significantly optimizes replenishment strategies. Notably, our model achieves a total cost reduction while maintaining a high service level of 0.761467 and reduces computational time to 82.32 s, highlighting its efficiency and adaptability. The innovation of this research lies in the application of the PCSO algorithm to dynamically manage inventory in real-time, aligning with sustainable inventory management practices. This study significantly contributes to the field by enhancing the economic and environmental sustainability of supply chain operations.