Demand Management and Forecasting Chapter 15 Demand Management and Forecasting

Focus on two short-range forecasting techniques 15-2 OBJECTIVES Focus on two short-range forecasting techniques Moving Average Exponential Smoothing 2

Simple Moving Average Formula 15-3 Simple Moving Average Formula The simple moving average model assumes an average is a good estimator of future behavior The formula for the simple moving average is: Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods 15

Simple Moving Average Problem (1) 15-4 Simple Moving Average Problem (1) Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts 15

Calculating the moving averages gives us: 15-5 Calculating the moving averages gives us: F4=(650+678+720)/3 =682.67 F7=(650+678+720 +785+859+920)/6 =768.67 The McGraw-Hill Companies, Inc., 2004 16

15-6 Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother 17

Simple Moving Average Problem (2) Data 15-7 Simple Moving Average Problem (2) Data Question: What is the 3 week moving average forecast for this data? Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts 18

Simple Moving Average Problem (2) Solution 15-8 Simple Moving Average Problem (2) Solution F4=(820+775+680)/3 =758.33 F6=(820+775+680 +655+620)/5 =710.00 19

Exponential Smoothing Model 15-9 Exponential Smoothing Model Ft = Ft-1 + a(At-1 - Ft-1) Premise: The most recent observations might have the highest predictive value Therefore, we should give more weight to the more recent time periods when forecasting 24

Exponential Smoothing Problem (1) Data 15-10 Exponential Smoothing Problem (1) Data Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using a=0.10 and a=0.60? Assume F1=D1 25

15-11 Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future. 26

Exponential Smoothing Problem (1) Plotting 15-12 Exponential Smoothing Problem (1) Plotting Note how that the smaller alpha results in a smoother line in this example 27

Exponential Smoothing Problem (2) Data 15-13 Exponential Smoothing Problem (2) Data Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5? Assume F1=D1 28

Exponential Smoothing Problem (2) Solution 15-14 Exponential Smoothing Problem (2) Solution F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75 29

The MAD Statistic to Determine Forecasting Error 15-15 The MAD Statistic to Determine Forecasting Error The ideal MAD is zero which would mean there is no forecasting error The larger the MAD, the less the accurate the resulting model 30

15-16 MAD Problem Data Question: What is the MAD value given the forecast values in the table below? Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 31

15-17 MAD Problem Solution Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 4 300 320 20 325 315 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts 32

MAPE Mean Absolute Percentage Error (MAPE) is another measure often used to evaluate forecasting accuracy A MAPE of under 8% is acceptable for most applications

Computing MAD and MAPE: Problem (1)

Exponential smoothing Panel consensus All of the above 15-20 Question Bowl Which of the following is an example of a “Time Series Analysis” type of forecasting technique or model? Simulation Exponential smoothing Panel consensus All of the above None of the above Answer: b. Exponential smoothing (Also includes Simple Moving Average, Weighted Moving Average, Regression Analysis, Box Jenkins, Shiskin Time Series, and Trend Projections.) 7

Answer: d. All of the above 15-21 Question Bowl Which of the following are reasons why the Exponential Smoothing model has been a well accepted forecasting methodology? It is accurate It is easy to use Computer storage requirements are small All of the above None of the above Answer: d. All of the above 7

15-22 Question Bowl The value for alpha or α must be between which of the following when used in an Exponential Smoothing model? 1 to 10 1 to 2 0 to 1 -1 to 1 Any number at all Answer: c. 0 to 1 7

Which of the following are sources of error in forecasts? Bias Random 15-23 Question Bowl Which of the following are sources of error in forecasts? Bias Random Employing the wrong trend line All of the above None of the above Answer: d. All of the above 7

15-24 Question Bowl Which of the following would be the “best” MAD values in an analysis of the accuracy of a forecasting model? 1000 100 10 1 Answer: e. 0 7

15-25 End of Chapter 15