By Michael Gilliland
Functional, nontechnical ideas to the issues of industrial forecasting. Written in a nontechnical type, this ebook offers useful options to universal enterprise forecasting difficulties, displaying you the way to consider enterprise forecasting within the context of uncertainty, randomness and strategy functionality; addresses the philosophical foundations of forecasting; increases expertise of primary matters usually neglected in pursuit of the right forecast; introduces a brand new strategy to take into consideration enterprise forecasting, concentrating on procedure potency and the removal of worst practices; offers sensible advice. �Read more...
summary: sensible, nontechnical options to the issues of commercial forecasting. Written in a nontechnical sort, this ebook presents useful suggestions to universal company forecasting difficulties, displaying you ways to contemplate enterprise forecasting within the context of uncertainty, randomness and technique functionality; addresses the philosophical foundations of forecasting; increases expertise of basic concerns often ignored in pursuit of the right forecast; introduces a brand new technique to take into consideration enterprise forecasting, concentrating on strategy potency and the removing of worst practices; presents functional recommendation
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Additional resources for The business forecasting deal : exposing myths, eliminating bad practices, providing practical solutions
4 Shipments versus Consumption this with shipment volatility, which is measured by the coefficient of variation of shipments from the manufacturer to the distributor or retail store. 4. As seen by the thick line, weekly consumption (consumer purchases at retail) is very stable—a little higher in summer, a little lower in winter. In fact, the mean weekly sales would provide a very good forecast. The thin line, however, shows shipments from the manufacturer to the retailer, and this kind of pattern is very typical.
In a period of chronic supply shortages (due to either supply problems or much higher than anticipated demand), customers may simply go elsewhere. Customers may truly want your product (so there is real demand), but it won’t be reflected in your historical data because no orders were placed. While orders are often perceived as equal to or greater than true demand, this third example shows that what is ordered may also be less than true demand. As with orders, the use of shipments to represent demand has a number of potential problems.
Sales volatility, as expressed by the CV of actual sales for each SKU, is along the horizontal axis. The inverse relationship between volatility and forecast accuracy is apparent. ) There are several lessons to draw from this kind of volatility analysis. First, we must be clear that just because one product is less volatile than another, it doesn’t guarantee we will be able to forecast it more accurately, but that is the general tendency. Second, our expectations for forecast accuracy must take into consideration the volatility of the behavior we are trying to forecast.