Phantom Inventory May Be Retailers’ Biggest Holiday Season Headache

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Misplaced products could make or break retailers’ holiday season performance.

“Phantom inventory,” also known as goods that show up in management systems as available but are hidden from view because they are essentially lost, continue to remain a major dilemma for retailers during the November to January period, The Wall Street Journal reported.

When retailers don’t know their on-shelf availability, the results can be costly.

Sales could plummet if shoppers are constantly unable to find products, and operating costs could increase if store employees have to focus their efforts on missing item queries. Supply chains are also negatively impacted with faulty demand forecasting and inaccurate stock reads.

Considering the holiday season is one of the busiest shopping periods of the year, retailers could take preventative measures to minimize phantom inventory issues. From machine learning technology to improved retail operations, retailers can take precautions to minimize misplaced products and boost their profit margins.

According to a recent International Council of Shopping Centers (ICSC) survey, roughly 70 percent of total holiday spending occurred at retailers with brick-and-mortar locations and e-commerce websites. For the 2016 holiday season, consumers frequented stores to buy gifts and take advantage of omnichannel services, like in-store pickup. Along with this heightened shopper activity, some retailers also suffered from unregistered inventory records, scanning errors, employee miscommunication, rising product demands and missing items.

“One solution that has proved effective when implemented is to develop special analytics using machine learning technology,” according to the Journal. “The analytics methods re-create the demand patterns for individual products, and incorporate the demand inventory uncertainty for each stock-keeping unit into forecasts and plans.”

Machine learning technology establishes more accurate analytics. Unlike traditional data, machine learning enables analytics to re-create product demand patterns, transform demand inventory uncertainty into detailed forecasts and quantify lost sales. In the process, stock-outs are drastically minimized and retailers can establish better inventory plans for the following holiday season.

Improving retail operations could also ward off phantom inventory problems. When employing holiday season workers, retailers could inform new employees about backstock procedures, including proper inventory arrangement and promotion notifications. Further, retailers could provide seasonal workers with mobile POS devices, which virtually update store inventory and alert consumers if nearby locations carry desired items. If employees are aware of product information on the sales floor, retailers can operate more smoothly during the holiday season.


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