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Seven Recent Trends in Retail Demand Forecasting and Replenishment

Posted by Jeff Bodenstab on May 24, 2017 3:30:00 PM


Most retailers are facing a shrinking operating “margin for error”. So it’s not surprising that many are looking for more accurate demand forecasting and intelligent stock replenishment. In a report entitled Market Guide for Retail Forecasting and Replenishment Solutions, Gartner analyst Mike Griswold spotlights seven recent trends in this area.

1/ Multichannel retailing is requiring inventory positioning in more locations than ever before and causing retailers to focus on “bottom up” forecasting, says Griswold. Until now, he says, many retailers have planned less than half of their assortment at the item/location level, but now they’re looking for platforms that can scan disaggregated demand streams down to the channel and stock-keeping unit (SKU) level. These retailers want their supply chains to be able to fulfill both e-commerce and brick-and-mortar purchases for a wide item assortment of items in a way that is “inventory agnostic”. They look to match the dynamic evolution of demand and give customers multiple order collection and delivery options. If needed, they look to balance inventory between stores and DCs via high-frequency inter-depot transfers.

2/ Less mature retailers are also focused on the demand signal. Griswold reports that retailers with less mature planning capabilities are seeking more consistent ownership of the demand signal, which is often fragmented and often owned by merchandising (especially in apparel). They want a single, unified model that allows stakeholder collaboration via “what-if” simulations of trade-offs. More retailers are now measuring forecast quality, forecast value-add (FVA) and bias, Griswold adds. They’re also looking for more external collaboration, to get better forecasts and share them with sales channels and suppliers.

3/ More sophisticated retailers understand that servicing “lumpy,” long-tail demand is driven by inventory and not forecast accuracy. For the long tail—slow-moving items with unpredictable demand—the key to meeting demand is to ensure service levels. For these items that can’t be reliably planned, retailers want supply chain planning (SCP) software that can accurately and automatically model stock-to-service levels to offer a clear picture of demand variability: how much stock they need, the mix, and where and when they need it.

4/ Retailers of all maturities are looking to automate forecasting and replenishment to improve planner productivity. With increasing pressure on margins, retailers don’t want to add more planners, and automation is no longer a dirty word, he says. They want the automated application to do the “heavy lifting”, allowing their planners to add value and business acumen.

5/ Product returns are increasingly costly. More e-commerce means more returns and some retailers see that more diligence on the forecasting and replenishment side—as well as analytics—can help them better predict and minimize returns at the outset, and then better manage and reposition the returned goods across their inventory.

6/ Cloud-based applications are becoming more acceptable as deployment platforms. Smaller retailers have been quicker to adapt to the cloud than larger ones, Griswold says, but retailers in general are becoming more comfortable with cloud platform viability and scalability. They see that cloud offers the flexibility to scale to demand, help companies get up and running quickly without extensive IT resources, and hedge against IT infrastructure obsolescence.

7/ Retailers are beginning to understand how machine learning differs from statistical techniques. Griswold says that retailers are beginning to comprehend the benefits of machine learning in the retail supply chain. Machine learning can analyze demand variables and their complex interactions and patterns in automated fashion, and “self-learn” demand profiles. Machine learning’s ability to collate and analyze clusters of big data can also help predict demand beforehand for launches, promotions, and markdowns involving products that share similar characteristics.

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Topics: Forecasting Demand and Analytics, Multichannel