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Slide 0 of 56 Chapter 3 Forecasting in POM: The Starting Point for All Planning.

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Presentation on theme: "Slide 0 of 56 Chapter 3 Forecasting in POM: The Starting Point for All Planning."— Presentation transcript:

1 Slide 0 of 56 Chapter 3 Forecasting in POM: The Starting Point for All Planning

2 Slide 1 of 56 OverviewOverview l Introduction l Qualitative Forecasting Methods l Quantitative Forecasting Models l How to Have a Successful Forecasting System l Computer Software for Forecasting l Forecasting in Small Businesses and Start-Up Ventures l Wrap-Up: What World-Class Producers Do

3 Slide 2 of 56 Demand Management l Independent demand items are the only items demand for which needs to be forecast l These items include: l Finished goods and l Spare parts

4 Slide 3 of 56 Demand Management A Independent Demand (finished goods and spare parts) B(4) C(2) D(2)E(1) D(3)F(2) Dependent Demand (components)

5 Slide 4 of 56 IntroductionIntroduction l Demand estimates for independent demand products and services are the starting point for all the other forecasts in POM. l Management teams develop sales forecasts based in part on demand estimates. l Sales forecasts become inputs to both business strategy and production resource forecasts.

6 Slide 5 of 56 Forecasting is an Integral Part of Business Planning ForecastMethod(s) DemandEstimates SalesForecastManagementTeam Inputs:Market,Economic,Other BusinessStrategy Production Resource Forecasts

7 Slide 6 of 56 Examples of Production Resource Forecasts Forecast Horizon Time Span Item Being Forecast Units of Measure Long-RangeYears l Product lines l Factory capacities l Planning for new products l Capital expenditures l Facility location or expansion l R&D Dollars, tons, etc. Medium- Range Months l Product groups l Department capacities l Sales planning l Production planning and budgeting Dollars, tons, etc. Short-RangeWeeks l Specific product quantities l Machine capacities l Planning l Purchasing l Scheduling l Workforce levels l Production levels l Job assignments Physical units of products

8 Slide 7 of 56 Forecasting Methods l Qualitative Approaches l Quantitative Approaches

9 Slide 8 of 56 Qualitative Forecasting Applications Small and Large Firms Technique Low Sales (less than $100M) High Sales (more than $500M) Managers Opinion 40.7%39.6% Executives Opinion 40.7%41.6% Sales Force Composite 29.6%35.4% Number of Firms 2748 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 Note: More than one response was permitted.

10 Slide 9 of 56 Qualitative Approaches l Usually based on judgments about causal factors that underlie the demand of particular products or services l Do not require a demand history for the product or service, therefore are useful for new products/services l Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

11 Slide 10 of 56 Qualitative Methods l Executive committee consensus l Delphi method l Survey of sales force l Survey of customers l Historical analogy l Market research

12 Slide 11 of 56 Quantitative Forecasting Approaches l Based on the assumption that the forces that generated the past demand will generate the future demand, i.e., history will tend to repeat itself l Analysis of the past demand pattern provides a good basis for forecasting future demand l Majority of quantitative approaches fall in the category of time series analysis

13 Slide 12 of 56 Quantitative Forecasting Applications Small and Large Firms Technique Low Sales (less than $100M) High Sales (more than $500M) Moving Average 29.6%29.2 Simple Linear Regression 14.8%14.6 Naive18.5%14.6 Single Exponential Smoothing 14.8%20.8 Multiple Regression 22.2%27.1 Simulation3.7%10.4 Classical Decomposition 3.7%8.3 Box-Jenkins3.7%6.3 Number of Firms 2748 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 Note: More than one response was permitted.

14 Slide 13 of 56 l A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand l Analysis of the time series identifies patterns l Once the patterns are identified, they can be used to develop a forecast Time Series Analysis

15 Slide 14 of 56 Components of Time Series 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 Whats going on here?

16 Slide 15 of 56 Components of Time Series l Trends are noted by an upward or downward sloping line l Seasonality is a data pattern that repeats itself over the period of one year or less l Cycle is a data pattern that repeats itself... may take years l Irregular variations are jumps in the level of the series due to extraordinary events l Random fluctuation from random variation or unexplained causes

17 Slide 16 of 56 Actual Data & the Regression Line

18 Slide 17 of 56 SeasonalitySeasonality Length of Time Number of Length of Time Number of Before Pattern Length of Seasons Before Pattern Length of Seasons Is Repeated Season in Pattern Is Repeated Season in Pattern YearQuarter 4 YearQuarter 4 Year Month12 Year Month12 Year Week52 Year Week52 Month Week 4 Month Week 4 Month Day Month Day Week Day 7 Week Day 7

19 Slide 18 of 56 Eight Steps to Forecasting l Determining the use of the forecast--what are the objectives? l Select the items to be forecast l Determine the time horizon of the forecast l Select the forecasting model(s) l Collect the data l Validate the forecasting model l Make the forecast l Implement the results

20 Slide 19 of 56 Quantitative Forecasting Approaches l Linear Regression l Simple Moving Average l Weighted Moving Average l Exponential Smoothing (exponentially weighted moving average) l Exponential Smoothing with Trend (double smoothing)

21 Slide 20 of 56 Simple Linear Regression l Relationship between one independent variable, X, and a dependent variable, Y. l Assumed to be linear (a straight line) l Form: Y = a + bX l Y = dependent variable l X = independent variable l a = y-axis intercept l b = slope of regression line

22 Slide 21 of 56 Simple Linear Regression Model l 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

23 Slide 22 of 56 Calculating a and b

24 Slide 23 of 56 Regression Equation Example Develop a regression equation to predict sales based on these five points.

25 Regression Equation Example Slide 24 of 55

26 y = t Period Sales Forecast Regression Equation Example Slide 25 of 55

27 Slide 26 of 56 Forecast Accuracy l Accuracy is the typical criterion for judging the performance of a forecasting approach l Accuracy is how well the forecasted values match the actual values

28 Slide 27 of 56 Monitoring Accuracy l Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach l Accuracy can be measured in several ways l Mean absolute deviation (MAD) l Mean squared error (MSE)

29 Slide 28 of 56 Mean Absolute Deviation (MAD)

30 Slide 29 of 56 Mean Squared Error (MSE) MSE = (S yx ) 2 Small value for S yx means data points tightly grouped around the line and error range is small. The smaller the standard error the more accurate the forecast. MSE = 1.25(MAD) MSE = 1.25(MAD) When the forecast errors are normally distributed

31 Slide 30 of 56 Example--MADExample--MAD MonthSalesForecast 1220n/a Determine the MAD for the four forecast periods

32 Slide 31 of 56 SolutionSolution MonthSalesForecastAbs Error 1220n/a

33 Slide 32 of 56 Simple Moving Average l An averaging period (AP) is given or selected l The forecast for the next period is the arithmetic average of the AP most recent actual demands l It is called a simple average because each period used to compute the average is equally weighted l... more

34 Slide 33 of 56 Simple Moving Average l It is called moving because as new demand data becomes available, the oldest data is not used l By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response) l By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)

35 Slide 34 of 56 Simple Moving Average l Lets develop 3-week and 6- week moving average forecasts for demand. l Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

36 Simple Moving Average Slide 35 of 55

37 Simple Moving Average Slide 36 of 55

38 Slide 37 of 56 Weighted Moving Average l This is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equal l This allows more recent demand data to have a greater effect on the moving average, therefore the forecast l... more

39 Slide 38 of 56 Weighted Moving Average l The weights must add to 1.0 and generally decrease in value with the age of the data l The distribution of the weights determine impulse response of the forecast

40 Slide 39 of 56 Weighted Moving Average Determine the 3-period weighted moving average forecast for period 4 Weights (adding up to 1.0): t-1:.5 t-2:.3 t-3:.2

41 Slide 40 of 56 SolutionSolution

42 Slide 41 of 56 Exponential Smoothing l The weights used to compute the forecast (moving average) are exponentially distributed l The forecast is the sum of the old forecast and a portion of the forecast error F t = F t-1 + (A t-1 - F t-1 ) F t = F t-1 + (A t-1 - F t-1 ) l... more

43 Slide 42 of 56 Exponential Smoothing The smoothing constant,, must be between 0.0 and 1.0 (excluding 0.0 and 1.0) The smoothing constant,, must be between 0.0 and 1.0 (excluding 0.0 and 1.0) A large provides a high impulse response forecast A large provides a high impulse response forecast A small provides a low impulse response forecast A small provides a low impulse response forecast

44 Slide 43 of 56 Exponential Smoothing Example Determine exponential smoothing forecasts for periods 2 through 10 using =.10 and =.60. Determine exponential smoothing forecasts for periods 2 through 10 using =.10 and =.60. l Let F 1 =D 1

45 Exponential Smoothing Example Slide 44 of 55

46 Slide 45 of 56 Effect of on Forecast

47 Slide 46 of 56 Criteria for Selecting a Forecasting Method l Cost l Accuracy l Data available l Time span l Nature of products and services l Impulse response and noise dampening

48 Slide 47 of 56 Reasons for Ineffective Forecasting l Not involving a broad cross section of people l Not recognizing that forecasting is integral to business planning l Not recognizing that forecasts will always be wrong (think in terms of interval rather than point forecasts) l Not forecasting the right things (forecast independent demand only) l Not selecting an appropriate forecasting method (use MAD to evaluate goodness of fit) l Not tracking the accuracy of the forecasting models

49 Slide 48 of 56 How to Monitor and Control a Forecasting Model l Tracking Signal Tracking signal = =

50 Slide 49 of 56 Tracking Signal: What do you notice?

51 Slide 50 of 56 Sources of Forecasting Data l Consumer Confidence Index l Consumer Price Index l Housing Starts l Index of Leading Economic Indicators l Personal Income and Consumption l Producer Price Index l Purchasing Managers Index l Retail Sales

52 Slide 51 of 56 Wrap-Up: World-Class Practice l Predisposed to have effective methods of forecasting because they have exceptional long-range business planning l Formal forecasting effort l Develop methods to monitor the performance of their forecasting models l Use forecasting software with automated model fitting features, which is readily available today l Do not overlook the short run.... excellent short range forecasts as well


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