AI Demand Forecasting and Supply Chain Optimization

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AI Demand Forecasting and Supply Chain Optimization

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The world's largest food manufacturer, Nestlé, has embedded SAS analytics into key processes to help sense consumer demand more accurately and reliably across the globe. Nestlé has leveraged the SAS product for "some time" but the announcement at the annual SAS Global Forum sees Nestlé adopting the platform on a vast scale and making major investments, explained Dan Mitchell, SAS' Director of Global Retail and CPG Practice. Nestlé sells thousands of different products worldwide, in a business that spans a complex web of suppliers, distributors, retailers and consumers. The forecasting challenge is amplified by the scale of the company's operations. How many bottles of water or bars of chocolate or tins of powdered formula will sell, how much should retailers order, how many materials and ingredients are required to meet this expected demand, without creating waste? Nestlé turned to SAS to create a consistent planning platform across 450 demand planners around the world. This consistency has been achieved despite the inherent inconsistency in the retail data Nestlé is able to access — whether receipts, shipment manifests or other documents, all with different qualities and latencies. "SAS is the engine for demand planning at Nestlé," said Vineet Khanna, senior vice-president of Corporate Supply Chain at Nestlé. "SAS is widely used for predictive analytics at Nestlé. We have trained 450 users at Nestlé worldwide to help make better demand-planning decisions. Our ability to implement SAS in various complex environments led us to expand our use of SAS beyond demand planning and supply chain and embrace the latest SAS technologies." Annette Green, SAS vice-president of the DACH Region and Nestlé executive sponsor, said Nestlé was a legend in the consumer packaged goods world for understanding local demand for each of its products. "It's inspiring to implement SAS successfully at the scale Nestlé requires. This is the foundation for Nestlé's next step with artificial intelligence (AI), machine learning and other advanced analytics." The results seen so far include: improved lag-1 forecast from 74% to 81%; ability to measure the impact of promotions and price changes; 70% statistical forecast adoption; -50 basis points reduction in forecast bias; improved case fill rate from 98.1% to 99.2%; reduction of 1.2 days of finished goods inventory; and 50 basis points improvement in customer service levels. Davis Wu, Global Lead for Demand Planning and Analytics at Nestlé, received the 2019 User Feedback Award for his work examining how AI and machine learning can be used for demand forecasting and planning. His influence helped SAS create Assisted Demand Planning, a new capability inside SAS Forecast Server that uses machine learning techniques to improve forecasting.