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Forecasting Problems
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And Those New Items New items can be handled much like new accounts, with one big difference. Sales of new items are based on perception of demand for the item, or speculation. The most reliable method I have found is to find the item most like the new item that you already have in the line and assess and adjust for the differences. The thought process is much the same as understanding the forecast assumption premises discussed early in this paper. For instance, you would start with the base (old) item and adjust for where the new one is in the replacement life cycle, if it fits a wider range of applications, has a higher or lower price, or if features have been added that make it more desirable. And don't forget to account for seasonality or advertisement when the new item is to be released. Several forecasting systems have the ability to profile or copy a pattern from an existing item and impose it to the new, allowing for adjustments based upon the differences. If you have these features, use them—they are very helpful. Who Can You Trust? Time and again in this paper, I have cited the need to get input from the sales and marketing folks who are closest to the customer. And time and again in my presentations across the country, I hear of problems with the reliability of information from these sources, if you can get any at all. In the absences of a formal sales and operations planning process, all is not lost. But, you must remember that these folks were not hired for their forecasting ability, but their ability to move product. If the environment is not conducive to getting information (and even if it is), I've found that your chances for success increase as the time requirement from these folks decreases. There is no crime in starting small, just with those who are most amenable to giving you input. The crime is in not starting at all! Keep all documents easy to read, easy to understand, and do as much homework as you can before you ask for advice. But the same cries ring out all across the country—I can't believe what this person tells me! If there is no formal process, you can still try explaining that the better the forecast, the better the chances to have the correct product there for the customers, and the better the opportunity for a big commission check. You could even point out how a couple of instances of low forecasts caused back orders. Failing that, use history! Would you ignore a trend on an item that had 20 percent growth every year? Then why would you ignore the trend from an individual if they were consistently over- or underoptimistic? Bias is bias, no matter how or why it occurs. If someone consistently gives biased information, too high or too low, that is a trend. Adjust it! Saving Time Yes, I saved the best for last! And the first pointer will save you a lot of time! If you are spending tons of time developing group forecasts to force on items, going through all the sound steps we've covered, but not reviewing the item forecasts, STOP DOING IT. All you are doing is imposing wonderful product group forecasts on item forecasts that stink, and the results will be terrible. Remember two things—your customers buy items, not aggregates, and garbage in, garbage out. And you will not only be saving your time, but the time in manufacturing, purchasing, etc., that will be wasted when you force a higher level increase on a item forecast that is already too high, causing more to be built or bought that will just sit on a shelf. Review all item forecasts and make sure they are reasonable before you do anything else. If you ever think you don't have the time to do item forecasts, just remind yourself what the customers buy.... Many statistical forecasting packages supply item exception reports. Work them! There are ways to cut down the time involved and deliver better results. First, always keep track of the number of exceptions by type, and how many of them actually spur you to revise the forecast. If you have an exception message that rarely prompts a change, TURN IT OFF! Many of these systems are initially implemented with standard settings, and one size does not fit all! Once you get your report down to a manageable size, you may even choose to tighten parameters on exceptions that cause intervention to prevent even more problems before they reach the MPS. Also, if your system gives you the option, sort it by items within a product group, rather than strictly in numerical order. Quite often, seasonal surges affect items within a particular group, or customer pipeline fills may spike demand in a given month. If the items are grouped together, it is much easier to spot these situations and recognize them for what they are. If the items are sorted numerically, you may miss the pattern and adjust forecasts that don't need it. But the best time saver I can recommend is that you review all item forecasts at least twice yearly, preferably before budget activities and at the beginning of the fiscal year. Although on the surface this sounds like it takes more time than it saves, I find the reverse to be true. Remember that no system can know the changing market conditions, catalog changes, promotional activities, and other factors that were discussed early on. Reviewing the items and adding intelligence prevents exception messages, overstocks that are built because the model doesn't respond to a trend soon enough, and back orders that aren't built because the model didn't spot seasonably. Consider the amount of time you waste investigating problems after the fact, the waste by everyone in execution, the returns, and finally, what it is that the customers buy! For balance of this article, click on the below link: Lean Manufacturing Articles and click on Series 10
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