The techniques used for this model are simple series moving averages and weighted moving averages where data from past periods are summed and divided by the number of time periods. The only difference between simple and weighted moving averages fall on more weights being placed on more recent data for the latter technique.
Finally, the cause and effect model assumes that factors are related to demand and that relationships between cause and effect are used to estimate future demands. Techniques for this model are simple and multiple regressions where distinguishing factor depends on the number of variables. For simple, there is only one variable; while for multiple, the demand is dependent on more than one variable.
The appropriate forecasting technique or model considers varying factors, to wit: current situation, time and funds available, and accuracy of the technique. As initially proffered, the judgmental approach is most appropriate for the introduction of new product were no historical data is available. For example, a global organization seeking to expand in a new market, with no previous experience, could use judgmental forecasting. Should the organization opt to conduct a survey to establish the possible demand for a new product, the company must consider time, money and efforts for doing so.
Another forecasting issue is determining the accuracy of the forecast. For the analog technique, the company requires using a similar product or service to project the future demand of their own product. For example, a company planning to market bottled water uses the performance of a competitive product of the same qualities to establish demand. Some factors could be similar but the company must consider location of the target market, as well as other environmental factors that could influence demand.
The following product characteristics influence packaging and materials handling: physical characteristics,