Jessica Curtis has 15 years of experience improving business decisions and driving results through forecasting, demand planning, and supply chain optimization. She is an Advisory Solutions Architect in the Retail, Consumer Goods, and Ag practice at SAS, where she helps clients solve business challenges with data-driven analytics. Prior to joining SAS, she led forecasting and analytics teams in the retail industry, adding value to strategic and operational decision-making across merchandising and supply chain. Jessica has a B.A. in Mathematics and Psychology from Wake Forest University and a Master’s in Applied Statistics from Penn State. As a mom of three kids under six, she understands the benefits of staying agile and balancing many challenges.
Consumer goods manufacturers have faced significant challenges over the past few years due to rapidly changing demand and supply disruptions in their end-to-end supply chain. As a result, manufacturers have realized the need to strengthen their resilience and have prioritized assessing their manufacturing capacity to maximize output and automation. To
Often the biggest challenge when implementing a successful forecasting process has nothing to do with the analytics. Forecast adoption – incorporating forecasts into decision-making – is just as high a hurdle to overcome as the models themselves. Forecasting is more than analytical models Developing a forecasting process typically begins with
