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

Charles Chase 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. He is the executive industry consultant and trusted advisor for the SAS Retail/CPG global practice, and writes a quarterly column entitled, “Innovations in Business Forecasting” in the Journal of Business Forecasting. Author page

Internet of Things | Machine Learning
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Is quick response forecasting a reality or just another buzzword?

“Quick response forecasting (QRF) techniques are forecasting processes that can incorporate information quickly enough to act upon by agile supply chains” explained Dr. Larry Lapide, in a recent Journal of Business Forecasting column. The concept of QRF is based on updating demand forecasts to reflect real and rapid changes in demand, both

Advanced Analytics | Analytics | Artificial Intelligence | Data Management | Machine Learning
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Why do we rely on judgment when analytics outperforms it?

Wherever there is uncertainty there has got to be judgment, and wherever there is judgment there is an opportunity for human fallibility. Donald Redelmeirer, physician-researcher Over the holidays, I read a fascinating book titled The Undoing Project: A Friendship That Changed Our Mind by Michael Lewis (W.W. Norton & Company,

Advanced Analytics | Machine Learning
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Is demand sensing and shaping a key component of your company’s digital supply chain transformation?

Depending on who you speak with you will get varying definitions and opinions regarding demand sensing and shaping from sensing short-range replenishment based on sales orders to manual blending of point-of-sales (POS) data and shipments.        Most companies think that they are sensing demand when in fact they are

Advanced Analytics | Analytics | Data Management | Machine Learning
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At the end of the day, it’s all about analytics-driven forecasting

Analytics-driven forecasting means more than measuring trend and seasonality. It includes all categories of methods (e.g. exponential smoothing, dynamic regression, ARIMA, ARIMA(X), unobserved component models, and more), including artificial intelligence, but not necessarily deep learning algorithms. That said, deep learning algorithms like neural networks can also be used for demand forecasting,

Advanced Analytics | Analytics | Machine Learning
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Straight talk about forecasting and machine learning

Are you caught up in the machine learning forecasting frenzy? Is it reality or more hype?  There's been a lot of hype about using machine learning for forecasting. And rightfully so, given the advancements in data collection, storage, and processing along with technology improvements, such as super computers and more powerful

Advanced Analytics | Data Management | Internet of Things
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Omnichannel is changing the way we view demand planning

Omnichannel Analytics are helping companies uncover patterns in big data to improve the customer experience.  Using those insights, companies can anticipate what consumers are planning to purchase and influence that purchase in real time.     Companies are experiencing unprecedented complexity as they look for growth and market opportunities. Their product portfolios are

Data Management | Machine Learning
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Machine learning changes the way we forecast in retail and CPG

Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the

Internet of Things
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The Digital Revolution: Crossing the digital divide is changing the Supply Chain Landscape

The digital revolution has affected all aspects of business, including supply chains.  The Internet of Things (IoT), with its network of devices embedded with sensors is now connecting the consumer to the factory. Technologies such as RFID, GPS, event stream processing (ESP) and analytics are combining to help companies to transform their existing

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You can no longer hide behind MAPE!

There are four key areas that require continuous investment in order to become demand-driven: people, process, analytics, and technology. However the intent of your demand forecasting process along with business interdependencies need to be horizontally aligned in order to  gain sustainable adoption.  Adoption alone doesn't necessarily mean it will be sustainable.       As

Charlie Chase 0
Stop cleansing your historical shipment data!

The real reason companies cleanse the historical demand is that traditional forecasting solutions were unable to predict sales promotions or correct the data automatically for shortages, or outliers. To address the short comings of traditional technology, companies embedded a cleansing process of adjusting the demand history for shortages, outliers, and

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New world of demand management

The global marketplace has been volatile, fragmented, and dynamic and is predicted to continue.  Subsequently, supply processes have become more mature than demand as industries focused on operational excellence over the past two decades.  As a result, there is a larger gap to fill in the redefinition of demand processes