The requirements for retail success don’t get much more basic than the ability to accurately forecast customer demand. Even a mom-and-pop bodega should be able to gauge how many people will order a breakfast sandwich and coffee each day.
For larger-scale retailers, the ability to develop an accurate forecast only gets complicated, and re-complicated, as they strive to correctly place and price inventory in multiple locations so they can maximize sell-through with minimum discounting.
Which is why many retailers are now exploring how artificial intelligence (AI) can help improve demand forecasting and optimize inventory levels and placement across their business networks — from stores to DCs.
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For example, Floor & Décor faces several forecasting and inventory planning challenges because one of its core products, flooring, is usually only purchased twice in an average consumer’s lifetime.
Floor & Décor also prides itself on having a large in-stock selection at its more than 225 stores. This provides a clear benefit to the customer and creates a better overall experience, but it also has another advantage — because flooring is both heavy and bulky, having inventory spread around also helps minimize shipping costs, which ultimately improves business efficiency and profitability.
“We sell [flooring] jobs, not individual pieces, so our sales are very ‘lumpy’ and intermittent,” said Darryl Aldridge, VP of Inventory at Floor & Décor in an interview with Retail TouchPoints. “That makes it a challenge to forecast and have the right inventory in the right location, because we’re not necessarily selling our best-known products every day.”
To address these challenges and opportunities, Floor & Décor turned to AI-powered solutions from Manhattan to improve “what-if” scenario planning and the overall accuracy of store-level inventory assortments, among other benefits.
Manhattan’s AI-powered solutions, Aldridge and his team have already seen “much better long-range projections and store-level forecasts that are much more accurate,” he said. Additionally, the larger number of data inputs used by these solutions allows Aldridge to play out a range of “what if?” scenarios based on the potential impact of various events.
For example, “if an area has a flood, we would expect a bump in sales afterwards,” said Aldridge. “Flooring is not the first thing you would think of [after a natural disaster], but [using AI] we can dial up scenarios” that can forecast increased demand.
On the supply chain side, AI allows Aldridge and his team to more easily factor in the effects of weather-related disruptions, geopolitical events (the war in Ukraine, for example) or accidents such as the Francis Scott Key bridge collapse in March 2024. “We can get answers about these within a day versus a week or more, which allows us to do a lot more ‘what if’ scenarios,” said Aldridge. “[For example], if we see an uptick in fuel prices, that will affect our trucking costs, we can see if it makes sense to buy products from somewhere different.”
Download the complete report to learn how AI can not only help perfect demand planning and inventory placement, but also empower teams to use this data to support more effective marketing campaign creation and customer experiences.
