Perhaps this year’s biggest catch phrase was the “Internet of Things” (IoT). IoT is centered on the idea that there is a tremendous amount of sensor data that could be used to run value chains more effectively. Within demand management, however, there are already huge amounts of data that too few consumer goods and food & beverage companies fully leverage – Point of Sale (POS) data. In my conversations with product managers and executives from supply chain software companies, most estimate that less than 20 percent of their customers have made the transition to improving their forecasts with demand sensing data. The consumer goods companies that do leverage POS data tend to be very large multinationals that serve as category captains for the largest retailers. Our discipline should be doing a much better job when it comes to demand sensing.
Yet we know that this data can improve forecasting, particularly short term forecasting that is used to improve replenishment planning. Jeff Bodenstab, a Vice President of Marketing at the ToolsGroup, showed me a slide I like a lot, but which I did modify a bit.
Stages in Demand Management (Numbers indicate required data points and are meant to be explanatory rather than exact)
When companies are at the beginning of their journey their forecasts are not based on granular data. A company might start by working with aggregate data on how many products, at the regional and product family level, are apt to be sold in a given period. Then a supply chain manager uses “splitting formulas” – formulas generally based upon historical sales – to disaggregate, for example, 80,000 cases of cola that will be sold in the Northeast, into specific stock keeping units (SKUs) to be shipped to particular warehouses. SKUs represent the individual products that make up a product family. So a product family might be sodas – the SKUs represent the different flavors of cola and the different sizes and container types those products are sold in.
The next stage would be to forecast at the SKU level at the distribution center level. This is a bottom up forecast where historical SKU shipment data from each DC is used for replenishment planning.
Even more data is needed to plan at the “ship to” level. Here instead of forecasting how much of a SKU will ship in total from a DC, a company is using historical data to forecast how much of a SKU will ship from that DC to every store location in that region that is supported by the DC.
“Each stage requires roughly an order of magnitude more data,” Mr. Bodenstab explained. But by the next stage – the use of POS data for demand sensing – we are getting into Big Data territory. Here, for example, a company might download all the consumption data on which of the company’s SKUs have been sold in the last week or even the preceding day by a given store. There are different ways this forecast can be made, but the more common way is to understand a store’s reordering policies, and the on hand inventory at the store and local DC, and then forecast what the store will order next week. “When forecasts are expressed in daily time-buckets,” Mr. Bodenstab pointed out, “the system can make use of ‘daily profiles’ that more precisely describe customer behavior patterns, within the week or the month.”
When it comes to using POS data, many demand management professionals will tell you it is not easy. Companies that embrace demand sensing will need to implement a Demand Signal Repository to store all this POS and market syndicate data; inaccurate and incomplete data needs to be “cleaned,” and the demand forecasting systems have to scale to be able to handle the data volumes.
But there is good ROI in these projects. Demand sensing leads to better replenishment planning (the right inventory in the right locations), makes it more likely that the products customers are looking for will actually be on the shelf (and the customer won’t try a competitor’s product and end up having a new preference), and improves promotional planning.
If demand management teaches us anything about learning to leverage the Big Data generated by sensors, it is that leveraging IoT data is likely to be a lengthy maturation process.