Forecast Management




The real opportunity in this area, consequently, is not the manip­ulation of data in an attempt to reduce forecast error but is the resulting discussion and activity necessary to maintain some level of accuracy. Frequent communication between marketing and production departments will be required to include as many known changes as are practical. The reluctance by marketing personnel to commit to an initial forecast and then to change it at a later date will be more than offset by the benefit of promotions without surprises and by the overall improvements to customer service.

Improved relations between those who are concerned with the demand for goods and those who produce them are needed. A medium for this relationship can be frequent attention to the forecast and the history of demand. One national manufacturer has actually taken a materials manager and placed him in the marketing department to manage its product forecasting. Manag­ing the forecasting system not only implies monitoring the specific details and reacting where necessary but also eliminating adjust­ments when the forecast is tracking correctly. Some simple measure of the forecast error will allow management to focus on the most critical products first. Forecast error will occur in two fundamental ways: random variation and bias.

Random variation is indicated by forecast errors that cancel each other over extended periods of time. The pattern of forecast to actual sales will alternate between high and low, etc., and cannot be avoided. However, attention should be given to safety stocks for these products and to the forecasting parameters to avoid overreaction by the forecasting system. Random variation is measured by the mean absolute deviation of the forecast to demand and is smoothed exponentially using the following formula:

Bias is a similar type of variation where the errors do not cancel each other but are generally in error in the same direction. Over time, this bias will lead to an increase in the cumulative forecast error, which results in either over or under total production. The bias is measured for each sales period, which updates an "average" bias for the product by also using exponential smoothing. The formula for this update is:

The two measures can be compared and reported simultaneously by calculating the tracking signal, which gives a measure of the forecast bias in relation to the MAD.

The tracking signal allows products of varying sales volumes to be compared using the same measure. The tracking signal varies from -1 to +1, which are the bias extremes in either direction. Again, this calculation allows forecast managers to focus their efforts on the critical problems first, leaving the system to deal with the routine calculations. The meetings between production and marketing managers can now be planned on a practical basis. Working only on the most critical items and on the ones with the highest tracking signal will provide the most effective use of the forecast information.

In the area of process control, SPC (Statistical Process Control) has proven to be a powerful tool in ensuring higher quality product and improved employee relations. This is not just the result of plotting a few numbers but, rather, is the result of constant

attention to the process and of concentration on the most serious problems first. The resulting cross-departmental discussions encourage the most effective improvements. The same holds true for forecasting product demand, or more specifically, managing the forecast. Successful forecast management requires: establishing an initial forecast that is used throughout the company, a system for continual updating of the forecast as new information is made available, signals to show where the most attention is needed and, most important, involvement of the people involved in getting the goods to the customer.


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