There's a pressing need for improvements in new-product forecasting, says Bower. Statistics show that between 70 and 80 percent of new products fail within 10 years. Forecasting error can be as high as 50 percent - "twice what you would normally expect with a baseline SKU," he says. "There's an awful lot of error right at the get-go."
The flood of new products into the supply chain isn't letting up. Bower says an average grocery store might see that addition of 10,000 new SKUs, as merchandisers labor to provide an ever-greater variety of product.
At the same time, retailers are becoming less patient than before about keeping new products on the shelves. Some are looking to the discipline of shelf optimization to determine what's selling, or which categories need to be refreshed. "Retailers are getting much smarter, using more mathematics," says Bower. "It puts every SKU at risk."
In theory, consumer packaged goods producers ought to do a better job of forecasting, because of the availability of point-of-sale data from major retailers. But not every supplier is making the best use of that intelligence. "The ones that do," says Bower, "are able to advance themselves."
A frustrated supplier or retailer might wonder whether it should even bother with forecasting new products, for which there is no consuming history on which to rely. But Bower believes there's room for improvement. "There are a lot of things you can do," he says, including foundational demand planning and the building of a "bottom-up" forecast to mitigate uncertainty.
Some companies have thrown in the towel on new-product forecasts. "I don't agree that that's a correct approach," Bower says.
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