Demand Forecasting

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Demand Forecasting

• Henry C. Co
• Technology and Operations Management,
• California Polytechnic and State University

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Types of Models

• Qualitative based on experience, judgment,
  knowledge
• Quantitative based on data, statistics.

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Qualitative Forecasts

• Executive opinions
• Combines views of key executives to obtain a
  sounder sales forecast than might be made by a
  single estimator.
• Sales force composite
• Obtains the combined views of members of the
  sales force as to the future sales outlook. In
  some companies, each salesperson estimates the
  future sales in his or her territory.
• To ensure realistic estimates, successive
  management levels are likely to do careful views.

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Qualitative Forecasts

• Consumer surveys
• Involves asking product users about the
  quantities they expect to buy in the forecast
  period. By combining user responses, the
  interviewing firm can estimate total demand for
  the product (or service), and then determine the
  portion of that demand that it expects to fill.
• Outside opinion
• Opinions of managers and staff
• Delphi technique

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Qualitative Forecasts

• Naive Methods eye-balling the numbers
• Formal Methods systematically reduce
  forecasting errors
• Time series models (e.g. exponential smoothing)
• Causal models (e.g. regression).

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Naive Forecasts
Uh, give me a minute.... We sold 250 wheels
last week.... Now, next week we should sell....
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Time Series Forecasts
Seasonal variation
Trend
Level
Assumptions There is information about the
past This information can be quantified in the
form of data The pattern of the past will
continue into the future.
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Using MS Excel
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Select the range A1B22. Click Insert, select
charts (scatter).
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Move the cursor to any point on the graph, and
right-click. Choose Add Trendline.
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Move the cursor to any point on the graph, and
right-click. Choose Add Trendline. For example,
select Exponential. Forecast Forward 34 periods
(through 1989). Display Equation and R-squared
value on chart
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Use the trend-line formula for post-diction y
7E-59e0.0753x. C2 7E-59EXP(0.0753A2) Copy
and Paste.
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Select the range A1B22. Click Insert, select
charts (scatter). Modify chart, legend, etc.
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Try other trend-linesPolynomial, etc.

• The easiest way to do this is to make a copy of
  the worksheet for the Exponential trend-line.
  Move the cursor to the trend-line, right-click,
  and choose a different trend-line.

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Polynomial Trend-Line
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Moving Average
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Forecast Errors

• MAD or MSE?

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• Mean Squared Error (MSE)
• Measures the accuracy of the forecasts
• sum of squares of the forecast errors, divided
  by n-1. Here, n is the number of squared errors
  summed.
• Mean Absolute Deviation (MAD)
• Also measures of forecast accuracy.
• average of the absolute value of forecast
  errors.

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• These two measures of forecast errors are usually
  consistent in the sense that if one forecasting
  model yields a higher MAD, it will also result in
  a higher MSE.
• But there are exceptions

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Past Performance of Two Models
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Which model would you use?A or B?