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Demand Management


Forecasting Inaccuracy & Accuracy

Reasons for forecasting Inaccuracy
Lack of participation by functional managers in the development of & the execution of a forecast system.Forecast requires alignment with one another to ve effective.
Too difficult to understand. If a forecasting tool is too difficult to understand it will most likely fail.
The more complex is the forecasting tool, the more isolated users are from the process& the more work is required to maintain it. Such a tool is also likely to be more costly to run & maintain.

Quantitative & Qualitative Forecasting Techniques:

Quantitative Methods:
Based on historical information that is usually available within the company. Various techniques are:

Trend Analysis:
A method for forecasting sales data when a definite upward or downward pattern exists. Model includes double exponential smoothing, regression & triple smoothing.

Forecast Error & Tracking

Monitoring forecast feedback & measuring forecast performance are parts of the forecasting process. Monitoring forecast feedback alerts the forecaster to processes that are out of control & how far they are out of control. In the concept of tracking signals a demand filter identifies errors that exceed some predetermined range or trip value. Demand filtering checks actual demand against some limit & refers the data to a person to determine whether or not action should be taken.

Seasonality


Many products have a seasonal or periodic demand pattern: skis, lawnmowers, bathing suits, and Christmas tree lights are examples. Less obvious are products whose demand varies by the time of day, week, or month. Examples of these might be electric power usage during the day or grocery shopping during the week. Power usage peaks between 4 and 7 p.m., and supermarkets are most busy toward the end of the week or before certain holidays.

Exponential Smoothing


It is not necessary to keep months of history to get a moving average. Therefore, the forecast can be based on the old calculated forecast and the new data. This formula puts as much weight on the most recent month as on the old average. If this does not seem suitable, less weight could be put on the latest actual demand and more weight on the old forecast. One advantage to exponential smoothing is that the

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