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Improving Slow Mover Management with Aggregate Forecasting

May 24, 2021 4 min

Generally, a “slow mover” is defined as a product that sells at the store level less than 30% of the weeks per year. For DIY retailers, slow movers are typically larger items, like lawn mowers or washing machines. Because slow movers generate so little sales data, and because what data does exist is spread out over a long time period, it can be difficult (or impossible) to accurately forecast when an item will sell at the individual store level.

While slow movers may have some seasonality that can guide inventory predictions, that isn’t always the case, and a season’s parameters can change depending on location. For example, a lawn mower may see peak sales in the spring, but the length of what would be considered spring can vary by weeks or months, depending on the climate in a particular region.

Still, these challenges don’t usually have a significant effect on local planning. Demand at the store level can be met mainly through presentation stock or, when feasible from a cost and space standpoint, safety stock. An individual store can keep one item on hand and then place an order to the DC to replace it when it sells. The true problems arise when the DC does not have the inventory to fulfill that replacement order in a timely manner.

Basing DC stock levels on manual work (or even guesswork) at the local store level can be a recipe for disaster.

Basing DC stock levels on manual work (or even guesswork) at the local store level can be a recipe for disaster. If the predicted demand is higher than reality, the overstock in the DC will take up valuable floor space and tie up funds in inventory that may not sell for months or longer. If, on the other hand, you’ve underestimated demand, the DC will not have the inventory available to replenish a store within a reasonable amount of time, leading to unhappy customers and lost sales.

Further, orders of large slow movers typically have long lead times, particularly if manufacturing takes place overseas. This means that any orders need to be placed well in advance of the actual demand and that they must have a high degree of accuracy.

So how can a retailer forecast slow movers for DCs with the understanding that local store and channel-specific demand is likely fractional?

Aggregate Forecasting of Slow Movers Optimizes DC and Store Replenishment

Retailers need to use aggregate forecasting for the management of slow movers. When data is particularly sparse, as is often the case with slow movers, multiple aggregate levels can help retailers estimate demand influencing factors and increase forecasting accuracy.

Retailers should be able to disaggregate these forecasts down to the product-store level. This allows insights into demand by, for example, region or season. They can then place accurate orders that take into account long manufacturing lead times.

For example, a retailer of lawn equipment can disaggregate the forecast of lawn mowers to product-store level to develop a new promotion on a specific lawn mower brand by viewing how recent promotions of other lawn mowers behaved across all stores. The retailer can then place the order for lawn mowers well in advance of the promotion to meet the demand.

Similarly, the retailer can determine the seasonality of lawn movers by disaggregating the forecasts for all lawn mowers within one geographic area. This automated process will identify the length of the peak buying season in that region, allowing the retailer to make more accurate orders to meet increased demand during that timeframe.

For omnichannel retailers, slow mover management needs to cover DCs that serve both stores and ecommerce channels. In this case, the system should generate a separate forecast for each channel (or, when applicable, banner or brand) for planning purposes and also have the ability to combine these forecasts to ensure that each DC holds enough inventory to meet offline, online, and brand- or banner-specific demand patterns.

Automation of Slow Mover Forecasting Saves Time, Improves Outcomes

An advanced forecasting and replenishment solution can automate the creation of these aggregate forecasts and determine the best levels for disaggregation while saving time and increasing the accuracy of slow mover management.

Make sure your vendor uses best practices and a data-based approach to help you determine the level or levels at which to forecast your slow movers.

Make sure your vendor uses best practices and a data-based approach to help you determine the level or levels at which to forecast your slow movers. You should be able to easily configure the selection of aggregate levels to best meet your business needs, manage promotions of slow movers, and always ensure accurate inventory is available to meet the increased demand during peak periods.

By taking advantage of automation and aggregation, retailers can place more accurate orders for slow movers with adequate lead time, ensuring that their DCs are adequately stocked, that their stores are replenished appropriately, and that their customers can find the large items they need, when they need them.

Written by

Niilo Mustala

Senior Solution Consultant