Finance

Sales and demand forecasters have a variety of techniques at their disposal to predict the future. For corning ware, where the levels of the distribution system are organized in a relatively straightforward way, we use statistical methods to forecast shipments and field information to forecast changes in shipment rates. The technique should identify seasonal variations and take these into account when forecasting; also, preferably, it will compute the statistical significance of the seasonals, deleting them if they are not significant.

Where data are unavailable or costly to obtain, the range of forecasting choices is limited. However, the box-jenkins has one very important feature not existing in the other statistical techniques: the ability to incorporate special information (for example, price changes and economic data) into the forecast. Although statistical tracking is a useful tool during the early introduction stages, there are rarely sufficient data for statistical forecasting.

To relate the future sales level to factors that are more easily predictable, or have a lead” Finance with sales, or both. In some instances where statistical methods do not provide acceptable accuracy for individual items, one can obtain the desired accuracy by grouping items together, where this reduces the relative amount of randomness in the data.

Generally, the manager and the forecaster must review a flow chart that shows the relative positions of the different elements of the distribution system, sales system, production system, or whatever is being studied. Our purpose here is to present an overview of this field by discussing the way a company ought to approach a forecasting problem, describing the methods available, and explaining how to match method to problem.

Since the distribution system was already in existence, the time required for the line to reach rapid growth depended primarily on our ability to manufacture it. Sometimes forecasting is merely a matter of calculating the company's capacity—but not ordinarily. As with time series analysis and projection techniques, the past is important to causal models.