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.
Retailers face unprecedented challenges with supply chain volatility, inflation, oil price fluctuations, labor shortages and geopolitical activities, making it difficult to plan across the organization. With retail evolving, coupled with persistent supply chain issues, this adds complexity to anticipating and planning for shifts in consumer demand. The emergence of an
Today’s consumers don’t want to be talked to; they want to have a conversation. They want to be marketed to as individuals, not as faceless members of the masses. Consumer packaged goods (CPG) organizations, in particular, recognize the value of these conversations. This dialogue – via loyalty programs, promotions, social
You're not alone if you’re still seeing local grocery stores with empty shelves. Food shortages are still lingering in 2023. Increases in consumer demand, labor shortages and shipping capacity restraints continue to interrupt supply chains, particularly for grocery retailers. These problems have persisted throughout the pandemic, as seen with the shortages
The landscape of supply chains has changed rapidly due to unforeseen disruptions. These changes include supply chain bottlenecks, inflation and geopolitical activities across retail and consumer goods industries. Retail supply chains are under immense pressure to keep up with these rapid changes. Innovators have been quick to take advantage of
Getting demand right – or getting it wrong – can have a significant impact on customer perceptions of your brand, particularly in this age of instant gratification. The need for agile, accurate demand planning has never been greater. Predicting forward-looking demand signals and shifting consumer demand patterns to recommend balanced, profitable commercial
Consumers are pulling back and shifting their purchases in the wake of inflationary pressures caused by high prices for fuel, freight costs, consumer goods and nonessential products. Demand is shifting faster than many retailers and consumer goods companies anticipated. Inflation continues to rise forcing consumer spending to shift once again
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
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
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
The past 20 months of disruptions caused by COVID-19 have been a wake-up call for retailers and consumer goods companies. Unpredictable market trends have caused havoc with categories, brands and products making it harder to predict supply requirements. All of these changes have given rise to the need for consumption
The social and economic impact of COVID-19 has dramatically affected supply chains and demand planning across all industries. Then there’s the Amazon effect, which has led to sky-high consumer expectations of the ordering and delivery process. Demand planners for retailers and consumer goods companies have quickly realized they have no
The need for agile, accurate demand planning has never been greater. When considering migrating your demand management application to a cloud-native solution, you might experience platform management challenges ranging from lacking the resources needed to oversee application operations, to manipulating maintenance tasks that may distract from growing the business. Why
Demand management concepts are now over 30 years old. The first use of the term "demand management" surfaced in the commercial sector in the late 1980s and early 1990s. Before that, the focus was on a more siloed approach to demand forecasting and planning that was manual and used simple
Thanks to COVID-19, companies have experienced how challenging it can be to plan and maneuver their supply chains around an unforeseen disruption. While the pandemic was a once-in-a-lifetime event (we hope), the unfortunate truth is that less severe events have overwhelmed or undermined demand and supply planning in the past
What if you had a technology solution that creates a real-time link between the customer demand signal and what's happening on the ground? What if plans that are being steered centrally could finally be connected to every shipping lane, while simultaneously, creating cost saving carrier adjustments? The first-of-its kind integration
Depending on who you talk to, you'll get varying definitions and opinions regarding demand sensing. Anything from sensing short-range replenishment based on sales orders, to the manual blending of point-of-sales (POS) data and shipments. But a key component for retailers and CPG companies is accurately forecasting short-term consumer demand to
Rapid demand response forecasting techniques are forecasting processes that can incorporate key information quickly enough to act upon in real time by agile supply chains. Retailers and consumer goods suppliers are urgently trying to determine how changes in consumer behavior will affect their regions, channels, categories, brands and products during
Is it getting harder and harder to find empty Excel spreadsheets cells, as you run out of columns and rows? Do your spreadsheet cell labels have more letters than the license plate on your car? Do you find yourself waking up in the middle of the night in cold
Almost everyone enjoys a good glass of wine after a long day, but did you ever stop to wonder how the exact bottle you're looking for makes its way to the grocery store shelf? Analytics has a lot to do with it, as SAS demonstrated to attendees at the National
How do you explain flat-line forecasts to senior management? Or, do you just make manual overrides to adjust the forecast? When there is no detectable trend or seasonality associated with your demand history, or something has disrupted the trend and/or seasonality, simple time series methods (i.e. naïve and simple
There's been a lot of hype regarding using machine learning (ML) for demand forecasting, and rightfully so, given the advancements in data collection, storage, and processing along with improvements in technology. There's no reason why machine learning can't be utilized as another forecasting method among the collection of forecasting methods
When it comes to forecasting new product launches, executives say that it's a frustrating, almost futile, effort. The reason? Minimal data, limited analytic capabilities and a general uncertainty surrounding a new product launch. Not to mention the ever-changing marketplace. Nevertheless, companies cannot disregard the need for a new product forecast
We have entered the “second machine age.” The first machine age began with the industrial revolution, which was driven primarily by technology innovation. The ability to generate massive amounts of mechanical power made humans more productive. Where the steam engine started the industrial revolution, the second machine age has taken
If you think machine learning will replace demand planners, then don’t read this post. If you think machine learning will automate and unleash the power of insights allowing demand planners to drive more value and growth, then this article is a must read.
“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
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 Recently, I read a fascinating book titled The Undoing Project: A Friendship That Changed Our Mind by Michael Lewis (W.W. Norton & Company, 2017). Lewis
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
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,
Let me start by posing a question: "Are you forecasting at the edge to anticipate what consumers want or need before they know it?" Not just forecasting based on past demand behavior, but using real-time information as it is streaming in from connected devices on the Internet of Things (IoT).
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
