How U.S. Retailers can Prepare for Stock Surges
In today’s turbulent retail market, the ability to predict and forecast staff and stock effectively has never been more valuable. This is something we’ve recently seen Sears find out the hard way. After years of losses and struggles, the 132-year-old retailer has been forced to file for bankruptcy after being unable to meet a hefty debt payment. In court papers, Sears revealed that it faces catastrophic consequences if it can’t repair its supply chain and keep merchandise flowing to the company’s stores and warehouses. For many, this news won’t come as a surprise: once the go-to store of middle-class Americans, in recent years, Sears had become visibly both understaffed and understocked.
Unfortunately, this is a familiar story among other retailers. Traditional forecasting methods have been found to present an error rate of 20-30 percent and as a result, many retailers are being left with too few staff on the shop floor and in the warehouse, and insufficient stock in place to meet sudden upticks in demand. Typically, this results in an unfortunate mix of abandoned shopping baskets, unfulfilled sales opportunities and increased costs.
However, as retailers begin to recognize this disparity in their planning efforts, typically using inaccurate forecast data to run their business, they’re now looking to improve their ability to accurately forecast demand and stock surges. Artificial intelligence (AI) and machine learning could be the solution to all their problems. As computing and data processing power ramp up, retailers are also likely to see a significant impact on their sales and profitability through automated technologies.