Nestlé has embedded SAS® Analytics into key business processes that will help it sense demand for its consumer goods more accurately and reliably on a global basis.
Improved demand accuracy is an important factor for producing the right products in the right quantities at the right time to avoid excessive stock holding.
“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 is 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,” she said. “This is the foundation for Nestlé’s next step with artificial intelligence (AI), machine learning and other advanced analytics.”
To learn more about the ways Nestlé uses analytics, hear Oliver Gléron, Group Head of Demand and Supply Planning at Nestlé, offer his insights during a panel discussion, Prioritizing and Organizing Digital Transformation in Retail and Consumer Packaged Goods, at SAS Global Forum 2019 on Tuesday, April 30, starting at 10 a.m. CT.
And Marcos Borges, Senior Planning Manager at Nestlé Brazil, will present Nestlé Brazil Case: Demand Planning Using SAS Forecast Server and SAS® Enterprise Guide® during a SAS Global Forum 2019 manufacturing breakout session on Monday, April 29, starting at 1 p.m. CT.
Nestlé visionary receives 2019 User Feedback Award
Davis Wu, PhD, Global Lead for Demand Planning and Analytics at Nestlé, received the 2019 User Feedback Award on Monday, April 29, at SAS Global Forum. Wu was cited as a visionary who examines the ways 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. He also helped guide the SAS product management team to enhance SAS® Visual Demand Planning on SAS® Viya®.