© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted Moving Average Forecasts u Exponential Smoothing u Simple Linear Regression

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 5 Types of Forecasts u Qualitative (Judgmental) u Quantitative – Time Series Analysis – Causal Relationships – Simulation

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 11 Delphi Method 4. Summarize again, refining forecasts and conditions, and again develop new questions. 5. Repeat Step 4 if necessary. Distribute the final results to all participants.

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 6 Components of Demand 1234 x x x x x x xx x x x xxx x x x x x xx x x x xxx x x x x x x x x x x x x x x x x x x x x Year Sales What’s going on here?

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 7 A Trend is Worth Noting u Start by identifying the trend u What is the trend in the sales of personal computers? u Are there any seasonal effects, cyclical factors or other predicted events that might affect the sales of personal computers?

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 10 Delphi Method l. Choose the experts to participate. There should be a variety of knowledgeable people in different areas. 2. Through a questionnaire (or ), obtain forecasts (and any premises or qualifications for the forecasts) from all participants. 3. Summarize the results and redistribute them to the participants along with appropriate new questions.

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 12 Judgmental Forecasting Applications Small and Large Firms Technique Low Sales < $100M High Sales > $500M Manager’s opinion40.7%39.6% Jury of executive opinion40.7%41.6% Sales force composite29.6%35.4% Number of Firms2748 Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 13 Quantitative Forecasting Applications Small and Large Firms Technique Low Sales < $100M High Sales > $500M Moving average29.6%29.2% Straight line projection14.8%14.6% Naive18.5%14.6% Exponential smoothing14.8%20.8% Regression22.2%27.1% Simulation3.7%10.4% Classical decomposition3.7%8.3% Box-Jenkins3.7%6.3% Number of Firms2748 Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 14 Time Series Analysis Pick a model based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 30 Forecast Errors u Study the formula for a moment u Now, what does MAD tell you?

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 31 Example--MAD MonthSalesForecast 1220n/a Determine the MAD for the four forecast periods

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 32 Solution MonthSalesForecastAbs Error 1220n/a

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 15 Simple Moving Average u Let’s develop 3-week and 6-week moving average forecasts for demand. u Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

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© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 18 In-Class Exercise u Develop 3-week and 5-week moving average forecasts for demand. u Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 19 In-Class Exercise (Solution)

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 20 Weighted Moving Average Determine the 3-period weighted moving average forecast for period 4. Weights: t-1.5 t-2.3 t-3.2

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 21 Solution

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 22 In-Class Exercise Determine the 3-period weighted moving average forecast for period 5. Weights: t-1.7 t-2.2 t-3.1

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 23 Solution

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 24 Exponential Smoothing u Premise--The most recent observations might have the highest predictive value. u Therefore, we should give more weight to the more recent time periods when forecasting F t = F t-1 +  (A t-1 - F t-1 )

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 25 Exponential Smoothing Example  Determine exponential smoothing forecasts for periods 2-10 using  =.10 and  =.60. u Let F 1 =D 1

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© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 27 Effect of  on Forecast

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 28 In-Class Exercise Determine exponential smoothing forecasts for periods 2-5 using  =.50 Let F 1 =D 1

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 29 In-Class Exercise (Solution)

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 35 Simple Linear Regression Model u b is similar to the slope. However, since it is calculated with the variability of the data in mind, its formulation is not as straight- forward as our usual notion of slope Y t = a + bx x (weeks) Y

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 36 Calculating a and b

© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 37 Regression Equation Example Develop a regression equation to predict sales based on these five points.

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y = t Period Sales Forecast 39 © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill