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.
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
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
"Tap into all your demand signals. Organize. Visualize. Analyze. Predict. Orchestrate. Optimize." The availability and collection of data are compelling companies to invest in demand signal management solutions to take advantage of the vast amount of information to support their planning processes. However, many have not gotten the return on
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
Today, we live in a polarized world that divides family members, friends, and business colleagues. It effects everything we do from the way we communicate with one another to how we handle business challenges. I have seen long time business colleagues have passionate discussions to defend their supply chain position
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
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
With all the enhancements in demand management over the past decade, companies are still faced with challenges impeding the advancement of demand-driven planning. Many organizations are struggling with how to analyze and make practical use of the mass of data being collected and stored. Others are perplexed as how to
I was recently asked by a customer if they should move the responsibility for creating the statistical baseline forecast. They were considering moving it from their regional country offices to their global headquarters. In addtion, they were considering changing the role of their regional demand planners to only make adjustments to
Downstream data have been electronically available on a weekly basis since the late 1980s. But most companies have been slow to adopt downstream data for planning and forecasting purposes. Let's look at why that is. Downstream data is data that originates downstream on the demand side of the value chain. Examples
