Smart scheduling and delivery optimizationSUPPLY CHAIN AND NETWORK PLANNING | CASE STUDY Unilever Challenge Solution Impact Unilever’s Hefei FTC distribution centre faced volatile e-commerce demand and complex parcel decisions, where manual planning led to excess inventory, slow response times and rising logistics costs.The company implemented two solutions: 1. An inventory system – powered by AI and machine learning – that integrates 12 data sources and analyses 10+ demand and performance variables to automate real-time replenishment and production decisions; in turn, improving stock balance, lead times and service levels. 2. A dynamic auto parcel optimization engine that uses real-time order data and 100+ packaging parameters to simulate scenarios and recommend cost-efficient bundling and shipping methods.The transformation enhanced overall operational agility and efficiency in the following ways: –39% improvement in SKU forecast accuracy. –75% reduction in slow-moving stock. –24% reduction in logistics cost per order. Our commitment to innovation is rooted in serving consumers faster, smarter and more reliably. The factory-to-consumer site in Hefei shows how smart scheduling and AI-powered delivery optimization are transforming fulfilment, enabling us to respond rapidly to changing demand and consistently deliver the right products with greater speed. By combining advanced technology with deep consumer insight, we are building agile, collaborative supply chains designed to perform at scale in an increasingly dynamic market. Graham Sommer, Global Head of Customer Operations, Unilever 4141 Intelligent Industrial Operations Outlook 202641