Research on demand response strategy for multi-echelon supply chain inventory optimization based on data analysis

Abstract

Inventory management in multi-echelon supply chains is increasingly challenged by severe demand volatility, dispersed stock across various echelons, and highly unstable replenishment lead times. Although modern enterprise informatization has successfully produced rich operational records—such as sales transactions, inbound and outbound movements, procurement plans, and detailed shelf-life records—these valuable datasets are often significantly underused for demand-responsive decision-making. Consequently, the negative outcomes, including frequent stockouts, costly overstocks, and slow inventory turnover, remain well documented within the extensive bullwhip-effect literature. To address these critical inefficiencies, this paper develops a comprehensive four-component framework designed for translating routinely collected warehouse data into actionable demand-response strategies across all supply chain echelons. The proposed framework comprises: (i) a unified demand-data collection schema that extends far beyond basic inventory registration to systematically include lead time, supplier, and consumption attributes; (ii) a robust upstream–downstream information-sharing protocol that effectively reduces feedback lag and mitigates information asymmetry; (iii) a hierarchical inventory-classification scheme combining traditional ABC analysis with dynamic shelf-life and turnover criteria; and (iv) an advanced dynamic replenishment mechanism featuring multi-condition alert triggers that replaces rigid fixed-cycle replenishment with agile, event-driven adjustments. We empirically illustrate the practical viability of this framework using four distinct case examples drawn from manufacturing, maintenance, and consumer-goods warehouses. By reporting baseline practices, targeted interventions, and observed performance outcomes for each scenario, the framework contributes a highly practitioner-oriented integration of established inventory-control principles with optimized information-flow design for complex multi-echelon settings.