IN PRACTICE: RE-DEFINING THE RETAIL VALUE CHAIN Let’s look at a fast-growing global fashion retailer. Like many of their peers, they were experiencing excessive store-level inventory at the end of each season. Traditional methods of clearing inventory, such as promotions and mark- downs, were leading to revenue loss and missed opportunities. Pre-season and centrally managed sales forecasts proved to be time-consuming, and inaccurate. Forecasting was largely based on historical data, experience, and intuition of individual sales-planning professionals. Furthermore, long planning horizons and siloed manual planning with delayed market responses, contributed to sub-optimal results. The retailer was looking to increase sales by significantly improving their forecasting accuracy, to leverage intelligent product segmentation in order to tailor supply chain fulfillment strategies, and to optimize their supplier network. With advanced analytics and machine learning, they were able to drop forecasting error rates significantly—from >90 percent to around 30 percent. This had a significant positive margin and brand image impact by eliminating excessive season-end promotions and mark-downs. And 20 percent of their production capacity was reallocated closer to the consumer markets using network and flow path optimization. In total, the retailer was able to achieve >$200m in annual benefits. INTELLIGENT SUPPLY CHAIN REINVENTING THE SUPPLY CHAIN WITH AI 6