Analysis of Inventory Management for Dongfeng Auto Parts Company Based on BP Neural Network

This study addresses systemic inventory management challenges confronting Dongfeng Auto Parts Manufacturing Company, where fragmented material classification and unreliable demand forecasting perpetuate operational inefficiencies and cost escalations. The research develops an integrated optimization framework that synergizes BP Neural Networks for advanced pattern recognition in demand prediction. Validated through empirical implementation, the framework demonstrates substantial improvements in forecasting robustness, inventory allocation precision, and working capital utilization. By resolving critical pain points including stock imbalances and forecasting inaccuracies—notably mitigating bullwhip effects in the supply chain—the solution establishes a closed-loop optimization mechanism. This transformation from reactive traditional practices to proactive data-driven operations provides automotive parts manufacturers with an actionable framework for sustainable inventory excellence, enhancing both operational resilience and strategic competitiveness within dynamic market environments.