<![CDATA[This research develops a dynamic system model to optimize inventory policies in multi-echelon supply chains within the sport footwear industry, addressing challenges from the bullwhip effect and supply chain disruptions. The sports footwear sector faces unique inventory management challenges due to complex demand patterns influenced by seasonality, fashion trends, and competitive dynamics. Our comprehensive system dynamics model captures the intricate relationships between four supply chain echelons: retailers, distributors, manufacturers, and raw material suppliers. The model integrates machine learning algorithms—specifically Long Short-Term Memory (LSTM) neural networks—for adaptive demand prediction and employs genetic algorithm optimization to determine optimal inventory parameters under various disruption scenarios. Using real-world data from a leading sports footwear manufacturer, we validated the model under normal operations and three distinct disruption scenarios: raw material shortages (45% reduction for 6 weeks), manufacturing capacity constraints (30% reduction for 8 weeks), and transportation disruptions (doubled lead times for 4 weeks). Results demonstrate that our proposed hybrid model reduces overall inventory costs by 18.7% compared to traditional policies while maintaining a 97.2% service level. The integration of machine learning for demand forecasting reduced prediction errors by 43.6% compared to conventional methods, directly mitigating the bullwhip effect by decreasing the order variability coefficient from 0.89 to 0.61 at the supplier level. Furthermore, the model enhanced supply chain resilience by reducing recovery time by 42% following major disruptions. This research contributes to the theoretical understanding of complex supply chain dynamics and practical applications for inventory management in volatile industries, offering a robust framework for decision-making under uncertainty.]]>
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