Abstract
Retail supply chains generate huge volumes of data that can provide valuable insights if analyzed effectively. This paper explores how retailers can leverage Big Data analytics techniques on supply chain data to gain enhanced visibility into their operations. We examine three use cases of data-driven supply chain visibility: (1) predictive replenishment to anticipate future demand and optimize inventory levels; (2) personalized assortment optimization to tailor product selections for local customer segments; and (3) optimized order fulfillment to improve delivery times and reduce transportation costs. We analyze how retailers can apply machine learning algorithms and statistical analysis on point-of-sale data, inventory data, customer data and external data sources to uncover hidden patterns and drive data-driven decisions in these areas. The results include reduced excess inventory, fewer stock-outs, higher in-store product availability, lower fulfillment costs and improved customer experience. Data-driven supply chain visibility allows retailers to transition from a reactive, speculative business model to a predictive, personalized model that enhances competitiveness.
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