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Understanding Forecast Characteristics

Hal Mather has written: "The forecasting problem is really two problems, quite different in concept and approach. The first is, 'How do you make better forecasts?" The second is, 'How do you use forecasts more effectively?' He goes on say that the real question must be: "How do I use forecast data, with its embedded accuracy problems, most effectively to run my business?" (1)
If we accept this as the key issue then we can improve the effectiveness of our forecast and its impact on the manufac­turing plan. The characteristics are:

1. Forecast is always wrong (or lucky)
2. Forecast should be two numbers
3. Forecast will be more accurate for families
4. Forecast is less accurate far out
5. Forecast is no substitute for calculated demand The implication for each of the above characteristic is:
1. Build in appropriate tolerance for error
2. Forecast a range, not a single number
3. Forecast significant families
4. Understand manufacturing ability to change rates
5. Forecast at highest practical level in product structure

The best results will come when the preparation of the forecast is a multi-discipline task. It must involve the users who must have, through education and training, an understanding of the techniques and the results. They must have valid expectations and a recognition why fore­casts fail.

Why Forecasts Fail

The eight most common causes why forecasts fail are:
1. Individual bias—No individual knows enough
2. Unrealistic expectations—There are many forces at work
3. Second guessing—Lack of communication/direction
4. Conflicting objectives—Departmental conflicts
5. Forecasting the wrong thing—Dependent demand
6. No timely monitoring—No measurement of forecast error
7. Sudden changes in the economy—Shortage or glut of resource
8. Bad source data—Used for quantitative forecast Source Data

Source data are fundamental to forecasting. Quality forecasts demand quality data which are appropriate to the situation. Source data will be a combination of statistical data and qualitative data, including surveys and manage­rial opinions. Irrespective of the source, data, to be valid, should meet the following criteria:

Data quality and accuracy

Determine if base data represent orders or shipments, both in terms of timing and quantity. Then be consistent. Avoid recording errors by using single entry and by installing demand filter to flag unreasonable data.

Level of Detail

The marketing forecast must reflect appropriate forecast periods (:i.e. annual, quarterly, monthly) without excess detail which is costly to handle. Planned promotions must be reflected. In the case of dating programs, recognize the difference between demand, shipment and cash flow.

Data External to Demand History

For best results, nontypical demand has been removed from the base demand history as used for quantitative forecasting. This requires the ability to make such separa­tions in the historical data while allowing for use of marketing and sales intelligence. There also should be a capability to identify portions of the forecast as being such "intelligence" and the source.

Dimensions of Data

Allow for conversion of data to the correct manufacturing unit-of-measure. Failure to use a constant unit-of-mea-sure can cause major data errors and bad decisions.

Data Modifications

It is a not uncommon practice to state forecasts in non-production dimensions such as dollars, kits, etc. This is acceptable as long as there is adequate definition of any planned pricing changes or any anticipated change of product contents

To be Continued


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