As a retailer, how many conversations do you have in a month about forecasts? Demand, supply side, SKU, region, category, fulfillment… The stark truth is that an ever-elusive, exceptionally accurate forecast isn’t going to happen.
So why do organizations still spend hundreds of thousands of hours tweaking, iterating and obsessing over their forecasts?
A forecast is only as good as the process it supports. If it’s part of an effective planning function that successfully optimizes inventory, determines reorder points and reduces movements, then a forecast with 85 percent accuracy could deliver higher service levels than one with 90 percent accuracy.
In other words, even the most accurate forecast on its own is not enough – it’s what you do with it that counts.
If we take it right back to basics, demand fulfillment is about having the right stock in the right place at the right time. The temptation has always been to get a demand forecast as accurate as possible but, in today’s climate, maximizing availability, margin and meeting On Time In Full (OTIF) targets is actually more about how well you can manage volatility across the entire supply chain. Squeezing an extra two percent accuracy out of a forecast doesn’t offer a meaningful competitive advantage.
If your end goal is to maximize on-shelf availability of stock (and keep costs down while doing so) then the focus should be on your end-to-end supply chain – the forecasting process is just one part of this.
Supply chains typically run on aggregated data, which gives a ‘big picture’ overview. By contrast, customer operations – where demand is generated – are more granular. The focus is on understanding the consumer through demographic and other types of segmentation and conducting category planning.
Consumer data and the intricacies of working with consumers aren’t often considered in the supply chain. But there is real power in using this data to drive supply chain operations and vice versa, using supply chain data to fuel customer operations.
Linking fulfillment and demand data represents a huge opportunity for demand planners. For example, location data can help to plan where to place inventory and optimize transportation and warehouse locations; fulfillment times and time horizon to restocking can be used to manage customer expectations and allocate orders; trends in customer behavior can help a consumer packaged goods business anticipate promotions, or disparities in final order quantities.
One would think an accurate forecast would ensure you have enough stock to keep service levels high without holding too much. But, in reality, a broader overview of what is happening at every stage of supply, including demand generation, is needed to face today’s volatility. A broad range of data from both customer and supply operations can and should be used to optimize for volatility and determine inventory levels.
Leveraging data from the full breadth of the supply chain provides insight that is increasingly valuable, particularly in an environment of unpredictable demand, supply shortages and transportation challenges.
It’s not about how accurate your forecast is, it’s what you do with it that counts. If CPGs have an inventory problem, bringing together data from across the supply chain is how they solve it.
Ira Dubinsky is the Go-to-Market Strategy Director at Peak, a Decision Intelligence platform on a mission to change the way the world works. Connecting data sets from across an organization to provide predictive insight, its platform includes a suite of features that enable customers to build and integrate AI-powered applications that optimize decision making across multiple business functions. It is used by leading brands including Nike, ASOS, PepsiCo, KFC and Sika.