Author

Charlie Chase
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Executive Industry Consultant/Trusted Advisor, SAS Retail/CPG Global Practice

Charles Chase is the executive industry consultant and trusted advisor for the SAS Retail/CPG global practice. He is the author of Next Generation Demand Management: People, Process, Analytics and Technology, author of Demand-Driven Forecasting: A Structured Approach to Forecasting, and co-author of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation, as well as over 50 articles in several business journals on demand forecasting and planning, supply chain management, and market response modeling. His latest book is Consumption-Based Forecasting and Planning: Predicting Changing Demand Patterns in the New Digital Economy. To learn more, please see his Author page.

Analytics | Machine Learning
Charlie Chase 0
How life science and health care supply chains can adapt to disruption

Robert Handfield, PhD, is a distinguished professor of Supply Chain Management at North Carolina State University and Director of the Supply Chain Resource Cooperative. In an episode of the Health Pulse Podcast, Handfield gave his views regarding the challenges health care and life science companies have encountered over the past two years

Analytics | Customer Intelligence | Data Visualization | Marketing
Charlie Chase 0
How CDP technologies offer sales and marketing teams powerful insights

In today's environment, data is exceedingly important but also increasingly harder to get and manage. A reliable customer data platform (CDP) can provide significant value to retail and consumer packaged goods (CPG) companies. Customer data platforms are used to consolidate and integrate customer and consumer data into a single data source. CDP

Advanced Analytics | Analytics | Data Management | Data Visualization
Charlie Chase 0
Misnomers regarding outliers and their usefulness in statistical modeling

Outliers provide much-needed insights into the actual relationships that influence the demand for products in the marketplace. They are particularly useful when modeling consumer behavior where abnormalities are common occurrences or unforeseen disruptions that impact consumer demand. But why do demand planners cleanse out outliers, when many are not really

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