Before the 2008 economic meltdown, demand was reasonably stable and companies could look at trends and seasonality to forecast future sales, says Chase, chief of industry consulting at SAS. Since the crisis, demand has become extremely volatile and trends have been disrupted, rendering old forecasting methods insufficient, he says.
Companies are compensating by using more robust analytics to mine big data, Chase says. Big data includes both structured data, such as that captured at point of sale, and unstructured data, such as that shared between consumers over the internet and in social media.
Additionally, companies are looking at both descriptive and predictive data. “Descriptive data is used for reporting purposes, to look backward to see what happened in the past and to make year-over-year or month-over-month comparisons,” he says. “Predictive data uses advanced math to predict what will happen, based on what has happened in the past. The power of analytics is in the predictive data,” he says.
SAP customer Nestle provides a good example, he says. Nestle uses the SAS demand planning and optimization solution along with an SAP enterprise system. “SAS is SAP certified and is a strategic alliance partner with SAP,” says Chase.
A division of Nestle that sells ice cream and frozen pizza does about 90 percent of its business around promotions, Chase explains. “Our system calculates, based on prior promotions, the incremental unit lift that an individual promotion will generate. We then take it a step further. Based on the pricing, we can tell whether or not the promotion will be profitable,” he says.
Most sales promotions are designed to gain market share rather than make a profit, but companies are learning that this approach often just rewards loyal customers, says Chase. “If we help a company find an offering that generates revenue and profit, they can shape demand by running that promotion in specific weeks. Our system predicts sales in the weeks when they run the promotion and tells them how much incremental demand and profit will occur.”
The idea behind demand sensing and shaping is to understand what products consumers are buying and then align supply and demand faster – and with less working capital, less waste and lower inventory costs, he says. “Companies increasingly are trying to figure out what influences consumers to buy their products. In addition to the traditional demand signals of trend and seasonality, they are looking at how things like price, advertising, in-store merchandising, promotions and even economic factors influence buying patterns,” says Chase.
Fortunately, he says, technology is available today that allows information to be gathered and analyzed on a grand scale. “The bottom line is that domain knowledge and experience and judgment are no longer enough,” he says. “Demand driven forecasting is all about the technology and analytic capability that allows you to look at demand signals, measure them mathematically and run ‘what if’ scenarios to see how to shape demand.”
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