Thanks to global competition, faster product development, and increasingly flexible manufacturing systems, an unprecedented number and variety of products are competing in markets ranging from apparel and toys to power tools and computers. Despite the benefits to consumers, this phenomenon is making it more difficult for manufacturers and retailers to predict which of their goods will sell and to plan production and orders accordingly.
As a result, inaccurate forecasts are increasing, and along with them the costs of those errors. Manufacturers and retailers alike are ending up with more unwanted goods that must be marked down—perhaps even sold at a loss—even as they lose potential sales because other articles are no longer in stock. In industries with highly volatile demand, like fashion apparel, the costs of such “stockouts” and markdowns can actually exceed the total cost of manufacturing.1
To address the problem of inaccurate forecasts, many managers have turned to one or another popular production-scheduling system. But quick-response programs, just-in-time (JIT) inventory systems, manufacturing resource planning, and the like are simply not up to the task. With a tool like manufacturing resource planning, for example, a manufacturer can rapidly change the production schedule stored in its computer when its original forecast and plan prove incorrect. Creating a new schedule doesn’t help, though, if the supply chain has already been filled based on the old one.
Similarly, quick response and JIT address only part of the overall picture. A manufacturer might hope to be fast enough to produce in direct response to demand, virtually eliminating the need for a forecast. But in many industries, sales of volatile products tend to occur in a concentrated season, which means that a manufacturer would need an unjustifiably large capacity to be able to make goods in response to actual demand. Using quick response or JIT also may not be feasible if a company is dependent on an unresponsive supplier for key components. For example, Dell Computer Corporation developed the capability to assemble personal computers quickly in response to customers’ orders but found that ability constrained by component suppliers’ long lead times.
We think that manufacturers and retailers alike can greatly reduce the cost of forecasting errors by embracing accurate response, a new approach to the entire forecasting, planning, and production process. We believe that companies can improve their forecasts and simultaneously redesign their planning processes to minimize the impact of inaccurate forecasts. Accurate response provides a way to do both. It entails figuring out what forecasters can and cannot predict well, and then making the supply chain fast and flexible so that managers can postpone decisions about their most unpredictable items until they have some market signals, such as early-season sales results, to help correctly match supply with demand.
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