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Ever wondered how your favorite cereal is always available when you need it, or how the store seems to never run out of fresh produce? Behind the scenes, demand forecasting and replenishment technology is working to keep shelves stocked.
Advances in this core technology are making it possible for retailers like Kaufland, a German hypermarket chain, to optimize their operations for increased efficiency, accuracy and insight into shopper behavior.
A balancing act: Forecasting & replenishment
Demand forecasting is the process of analyzing historical sales data, along with a multitude of factors such as seasonality and market trends. Replenishment is about making sure the right amount of product is ordered and delivered to the store at the right time to restock the shelves as products are sold.
Together, demand forecasting and replenishment create a cycle that keeps a store running smoothly. Accurate forecasting helps order the right amount of stock, reducing the chances of overstocking, which can lead to waste, understocking, and unhappy customers.
It’s a delicate balance, but when done right, it means you can get your weekly grocery supplies without a hitch.
For departments like fresh produce, meat, and baked goods that have significant fluctuations in sales each day, accurate prediction down to the individual SKUs are vital. Kaufland relied on software from an SAP rival to calculate forecasts for fresh products, but the algorithms weren’t providing sufficient granularity for all fresh products.
Unified Demand Forecasting
For Kaufland, SAP’s Unified Demand Forecasting (UDF) has proven to be a game-changer. UDF leverages powerful algorithms and machine learning to predict future sales – i.e., how much of each product will sell in a given timeframe.
SAP’s UDF goes beyond historical sales data to produce predictions. It takes into account a wide range of influencing factors, like weekdays, price effects, trends, seasons, payday effects, and weather. It can even make educated guesses about the sales of new products based on similar items.
All this means that UDF can produce incredibly accurate + granular forecasts, which are then used for everything from stock replenishment to ad planning. The solution can run what-if scenarios, testing how different factors might impact sales.
As a unified solution, these forecasts can be used across multiple use cases, from auto-replenishment to promotion quantity planning, assortment planning, price calculation, and warehouse requirements.
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Better algorithms = better forecasts
Kaufland was the first retailer to use SAP’s new UDF forecasting system, which replaces forecasts for all assortments from the old SAP F&R system and also the rival’s fresh products forecasts. The system forecasts the expected sales quantities for all assortments at all 1,450 Kaufland stores in Germany and Eastern Europe.
The improved algorithms provide “better forecast quality,” says Michael Hahn from Schwarz Group. Hahn is responsible for supply chain management systems within Schwarz IT, operating both the Lidl and Kaufland brands.
Based on SAP HANA database technology, the UDF system calculates up to 35 million store-product combinations a day. In the first step in this process — modeling –UDF evaluates the sales history from the past 800 days.
In the second step — forecasting — the system calculates expected sales for the next 101 days at a rolling daily level. The long forecast horizon is intended to support advertising planning. Purchase orders to suppliers and supply to stores are then controlled by forecasts for the near future or even the next day.
According to Hahn, the results were so good that they became the basis for production control in Kaufland-owned meat plants and for the scheduling of baking machines in stores.
Kaufland’s replenishment software, which provides optimized order quantities based on factors like rebates and truck utilization as an auto replenishment, now also leverages forecasts from the UDF system.
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Optimizing forecasting and replenishment
For Kaufland, the huge number of influencing factors that SAP’s UDF system can include in its calculations are a huge plus. But to streamline the initial deployment, Kaufland limited the factors to those that are relevant for the given assortment.
Also, Kaufland aims to use intelligent modelling for products recalculated daily in order to reduce the current computing time of eight to nine hours by focusing only on products with significant changes.
Hahn says that in the case of promotional products, Kaufland managed to push the error rate to less than 38%. Here, the error rate refers to the WMAPE (weighted mean absolute percentage error). The most important KPIs for Kaufland in assessing the forecast software are:
- Out-of-stocks
- Inventories
- Inventory costs
- Revenue
Kaufland uses SAP’s Unified Demand Forecast solution for promotion quantity planning, assortment planning, price calculation, and warehouse requirements.
The retailer also uses the software to run what-if scenarios for a number of use cases, including advertising planning and pricing.
