I came across this article which talks about biggest challenge for retailers. It is interesting to know how the retailers adjust to the uncertain demand in an uncertain economy..
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.
This approach incorporates two basic elements that other forecasting and scheduling systems either totally or partially lack. First, it takes into account missed sales opportunities. Forecasting errors result in too little or too much inventory. Accurate response measures the costs per unit of stockouts and markdowns, and factors them into the planning process. Most companies do not even measure how many sales they have lost, let alone consider those costs when they commit to production.
Second, accurate response distinguishes those products for which demand is relatively predictable from those for which demand is relatively unpredictable. It does this by using a blend of historical data and expert judgment.
Those two elements help companies rethink and overhaul not only every important aspect of their supply chains—including the configuration of their supplier networks, schedules for producing and delivering unfinished materials, transportation, and the number and location of warehouses—but also the designs of their products. Armed with the knowledge of which products have predictable demand and which do not, they can then take different approaches to manufacturing each class of product. Those in the relatively predictable category should be made the furthest in advance in order to reserve greater manufacturing capacity for making unpredictable items closer to the selling season. Such a strategy enables companies to make smaller quantities of the unpredictable products in advance, see how well the different goods fare early in the selling period, and then use that information to determine which products to make more of.
I hope it has added some value .....
