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Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter Demand Forecasting 3 Slides prepared by Laurel Donaldson Douglas College.

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1 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter Demand Forecasting 3 Slides prepared by Laurel Donaldson Douglas College

2 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Learning Objectives 2 LO 1 LO 3 LO 2 LO 4 LO 5 LO 6 LO 7 LO 8 Identify uses of demand forecasts, distinguish between forecasting time frames, describe common features of forecasts, list the elements of a good forecast and steps of forecasting process, and contrast different forecasting approaches. Describe at least three judgmental forecasting methods. Describe the components of a time series model, and explain averaging techniques and solve typical problems. Describe trend forecasting and solve typical problems. Describe seasonality forecasting and solve typical problems. Describe associative models and solve typical problems. Describe three measures of forecast accuracy, and two ways of controlling forecasts, and solve typical problems. Identify the major factors to consider when choosing a forecasting technique. Identify uses of demand forecasts, distinguish between forecasting time frames, describe common features of forecasts, list the elements of a good forecast and steps of forecasting process, and contrast different forecasting approaches. Describe at least three judgmental forecasting methods. Describe the components of a time series model, and explain averaging techniques and solve typical problems. Describe trend forecasting and solve typical problems. Describe seasonality forecasting and solve typical problems. Describe associative models and solve typical problems. Describe three measures of forecast accuracy, and two ways of controlling forecasts, and solve typical problems. Identify the major factors to consider when choosing a forecasting technique.

3 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter Outline  What is forecasting?  Features common to all forecasts  Elements of a good forecast  Steps in the forecasting process  Approaches to forecasting  Judgmental methods  Time series models  Associative models  Accuracy and control of forecasts  Choosing a forecasting technique  Excel Templates 3

4 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1 What is Forecasting? 4 I see that you will get a 100 in OM this semester. A demand forecast is an estimate of demand expected over a future time period

5 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1  How big a facility do I need to manufacture a new videophone?  How much money do I need to run operations of my accounting office?  How many pairs of white shoes should I order for the summer season in my store?  How many operators should I schedule next month for my call centre?  How much lettuce should I buy for next week in my restaurant? 5 Need to FORECAST demand!

6 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1 3 Uses for Forecasts: Design the System long term (annual) (types of products & services to offer, capacities, equipment, location) Use of the System medium term (monthly) (inventory, workforce levels, planning production) Schedule the System short term (daily, weekly) (production, purchasing, staff scheduling) 6

7 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1 Features of Forecasts  Assumes causal system past ==> future  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs. individuals  Forecast accuracy decreases as time horizon increases 7

8 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1 Elements of a Good Forecast Accurate Reliable MeaningfulCompatible Useful time horizon Easy to understand & use 8

9 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1 Steps in the Forecasting Process 1 Determine purpose of forecast 2 Establish a time horizon 3 Select a forecasting technique 4 Obtain, clean and analyze data 5 Make the forecast 6 Monitor the forecast 9

10 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 1 Approaches to Forecasting  Judgmental  non-quantitative analysis of subjective inputs  considers “soft” information such as  human factors, experience, gut instinct  Judgmental  non-quantitative analysis of subjective inputs  considers “soft” information such as  human factors, experience, gut instinct 10  Quantitative  Time series models  extends historical patterns of numerical data  Associative models  create equations with explanatory variables to predict the future  Quantitative  Time series models  extends historical patterns of numerical data  Associative models  create equations with explanatory variables to predict the future

11 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 2 Judgmental Methods  Executive opinions  pool opinions of high-level executives  long term strategic or new product development 11  Expert opinions  Delphi method: iterative questionnaires circulated until consensus is reached.  technological forecasting

12 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 2 Judgmental Methods  Sales force opinions  based on direct customer contact 12  Consumer surveys  questionnaires or focus groups  Historical analogies  use demand for a similar product

13 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 What is a Time Series? Time series: a time ordered sequence of observations taken at regular intervals of time 13  Level: (average) horizontal pattern  Trend: steady upward or downward movement  Seasonality: regular variations related to time of year or day  Cycles: wavelike variations lasting more than one year  Irregular variations: caused by unusual circumstances, not reflective of typical behaviour  Random variations: residual variations after all other behaviours are accounted for (called noise) The following 6 patterns could be identified in a time series:

14 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Patterns of a Time Series 14 Year 1 Year 2 Year 3 Year 4 Seasonal peaks (winters) Trend component Actual demand line Demand for snowboards Random variation

15 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Time series models  Naive methods  Averaging methods  Moving average  Weighted moving average  Exponential smoothing  Trend models  Linear and non-linear trend  Trend adjusted exponential smoothing  Techniques for seasonality  Techniques for cycles  Naive methods  Averaging methods  Moving average  Weighted moving average  Exponential smoothing  Trend models  Linear and non-linear trend  Trend adjusted exponential smoothing  Techniques for seasonality  Techniques for cycles 15

16 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Naive Methods  Next period = last period  Simple to use and understand  Very low cost  Low accuracy 16 F = forecast A = actual

17 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Naive Method - Example 17 Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell....

18 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Naive Method with Trend: Example 18 2 years ago we sold 50 memberships. Last year we sold 75 memberships. This year we expect to sell …100

19 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Averaging Methods 19 F = forecast A = actual  = smoothing constant

20 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Moving Average  average of last few actual data values, updated each period  easy to calculate and understand  smoothes bumps, lags behind changes  choose number of periods to include  fewer data points = more sensitive to changes  more data points = smoother, less responsive 20

21 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Moving Average - Example  Compute a three-period moving average forecast for period 6, given the demand below 21

22 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Weighted Moving Average - Example  Compute a 4-period weighted moving average forecast for period 6 using a weight of 0.4 for the most recent period, 0.3 for the next, 0.2 for the next, and 0.1 for the next. 22  The choice of weights may involve the use of trial and error to find a suitable weighting scheme  Weights must add up to 100%

23 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Moving Average Example PeriodDemandForecast ( )/3 = 16 1/3 ( )/3 = 19 1/ ( )/3 = 11 2 / 3 ( )/3 = 14

24 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Graph of Moving Average 24 |||||||||||| Quantity 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Moving Average Forecast

25 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Moving Average Example PeriodDemandForecast Apply weights of.5 for most recent period, then.3, then.2 [(.5 x 16) + (.3 x 14) + (.2 x 12)] = 14.6 [(.5 x 19) + (.3 x 16) + (.2 x 14)] = 17.1 [(.5 x 23) + (.3 x 19) + (.2 x16)] = [(.5 x 14) + (.3 x 12) + (.2 x 9)] = 12.4

26 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Moving Average And Weighted Moving Average – 25 – 20 – 15 – 10 – 5 – Quantity |||||||||||| Actual sales Moving average Weighted moving average

27 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing  sophisticated weighted moving average  weights decline exponentially  most recent data weighted most  subjectively choose smoothing constant    ranges from 0 to 1 (commonly.05 to.5)  widely used  easy to use  easy to alter weighting 27

28 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing Formula  Forecast = previous forecast plus a percentage of the forecast error  Actual - Forecast is the error term   is the % feedback 28 F t = F t-1 +  ( A t-1 - F t-1 ) F = forecast A = actual

29 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing: Alternate Formula  Forecast = previous forecast plus a percentage of the forecast error   is the weight on actual demand   is the weight on previous forecast 29 F t = (1 -  F t-1 +  ( A t-1 ) F = forecast A = actual

30 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing: Example  Forecasted demand = 142 video games  Actual demand = 153  Smoothing constant  = New forecast=.2 (153) + (1 -.2)(142) = = ≈ 144 games

31 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing: Example  Forecasted demand = 142 video games  Actual demand = 153  Smoothing constant  = New forecast= ( ) = = ≈ 144 games

32 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing: Example 32  Prepare a forecast using smoothing constant  =  What is the starting point?  average of several periods of actual data  subjective estimate (for this example, use 60)  first actual value (naïve approach)  Prepare a forecast using smoothing constant  =  What is the starting point?  average of several periods of actual data  subjective estimate (for this example, use 60)  first actual value (naïve approach)

33 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Exponential Smoothing: Your Turn!  What are the exponential smoothing forecasts for periods 2-5 using  =0.5?  Use naïve approach for 1 st week  What are the exponential smoothing forecasts for periods 2-5 using  =0.5?  Use naïve approach for 1 st week 33

34 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 F 2 =(.5)(820)+(1 -.5)(820) =820F 3 =(.5)(775)+( )(820)=797.5 Exponential Smoothing: Your Turn! 34

35 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Selecting a Smoothing Constant  – 200 – 175 – 150 – ||||||||| ||||||||| Period Demand a =.1 Actual demand a =.5

36 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Choosing   When demand is fairly stable, use a lower value for   smoothes out random fluctuations  When demand increasing or decreasing, use a higher value for   more responsive to real changes  Try to find balance  trial and error  can change over time. 36

37 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 True or False? A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average. False As compared to a simple moving average, the weighted moving average is more reflective of the recent changes. True A smoothing constant of.1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a value of.3 will. False 37

38 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 3 Excel: Exponential Smoothing 38 Solved Problem 1: Excel Template

39 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Techniques for Trend 39 –Develop an equation that describes the trend –Look at historical data –Develop an equation that describes the trend –Look at historical data

40 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Nonlinear Trends 40

41 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Linear Trend Equation –Fit a trend line to a series of historical data –Use regression to find the equation of the line (called the Least Squares Line) 41

42 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Linear Trend 42 Deviation Time Demand Actual observation Points on the line

43 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Linear Trend: Example 43

44 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Excel - Linear Trend 44 Insert Chart Scatter Highlight data range Right Click on a data point Add Trendline Type: Linear Options: Show equation on chart Or Insert Functions: =SLOPE(Range of y's,Range of x's) =INTERCEPT(Range of y's,Range of x's)

45 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Excel - Linear Trend 45

46 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Trend-Adjusted Exponential Smoothing  select values (usually through trial and error) for   = smoothing constant for average   = smoothing constant for trend  estimate starting smoothed average and smoothed trend  use most recent data 46

47 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Trend-Adjusted Exponential Smoothing 47 TAF t+1 = S t + T t (3–6) where S t = smoothed average at the end of period t T t = smoothed trend at the end of period t S t = TAF t +α(A t  TAF t )(3–7) T t = T t-1 +  ( S t  S t-1  T t-1 )

48 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 4 Trend-Adjusted Forecast: Example 48

49 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 5 Techniques for Seasonality  Additive or Multiplicative Model  quantity added to average or trend  or proportion x average or trend 49 time Demand Additive Model Demand = Trend + Seasonality Multiplicative Model Demand = Trend x Seasonality Multiplicative Model Demand = Trend x Seasonality

50 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 5 Using Seasonal Relatives  Seasonal Relative (or index)  = proportion of average or trend for a season in the multiplicative model  seasonal relative of 1.2 = 20% above average  Deseasonalize  remove seasonal component to more clearly see other components  divide by seasonal relative  Reseasonalize  adjust the forecast for seasonal component  multiply by seasonal relative  Seasonal Relative (or index)  = proportion of average or trend for a season in the multiplicative model  seasonal relative of 1.2 = 20% above average  Deseasonalize  remove seasonal component to more clearly see other components  divide by seasonal relative  Reseasonalize  adjust the forecast for seasonal component  multiply by seasonal relative 50

51 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 5 Times Series Decomposition 1. Compute the seasonal relatives.2. De-seasonalize the demand data. 3. Fit a model to de-seasonalized demand data, e.g., moving average or trend. 4. Forecast using this model and the de-seasonalized demand data. 5. Re-seasonalize the deseasonalized forecasts. 51

52 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 5 Techniques for Seasonality - Example  Predict quarterly demand for a certain loveseat  The series has both trend and seasonality.  Quarterly relatives : Q 1 = 1.20, Q 2 = 1.10, Q 3 = 0.75, Q 4 =  Trend equation y t = t (t = 1 in first quarter of 2003)  Predict demand for quarter 3 of

53 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 6 Associative Forecasting 53 If I want to predict ridership originating from a new train station, what data might I look at? 1. Find (predictor) variables that are associated with ridership at other stations. 2. Associated = correlated = as one moves the other moves 3. Create a model that shows the relationship between the predictor variables and the predicted variable (e.g. ridership) 4. Technique is regression analysis Simple linear regression with one variable Multiple regression (can be non-linear) 5. Test the model to see which variables most useful in predicting ridership (look at r 2 ) 6. Use the model to predict ridership, given values of the predictor variables.

54 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 6 Associative Models  Predictor variables (x): used to predict values of the variable of interest (y)  (also called independent variables)  Predictor variables (x): used to predict values of the variable of interest (y)  (also called independent variables) 54  Linear regression: process of finding a straight line that best fits a set of points on a graph  (use the Least Squares Equation)  Linear regression: process of finding a straight line that best fits a set of points on a graph  (use the Least Squares Equation)  Multiple regression: models with more than one predictor variable  (computations complex, created with computer)  Multiple regression: models with more than one predictor variable  (computations complex, created with computer)

55 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 6 Simple Linear Regression  would a linear model be reasonable? 55 Computed relationship

56 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 6 Excel: Simple Linear Regression 56

57 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 6 Correlation and Excel  Correlation coefficient (r): measure of the strength of relationship between two variables  ranges from -1 to +1  -1 = two variables move together in same direction  +1 = two variables move together in opposite direction  =CORREL(Range of y values, Range of x values)  r 2 measures proportion of variation in the values of y that is “explained” by the predictor variables in the regression model  ranges from 0 to 1  higher values = more useful predictors  =RSQ(Range of y values, Range of x values) 57

58 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 6 Linear Regression Assumptions  Predictions are being made only within the range of observed values  relationship may be non-linear outside that range  y-intercept often not meaningful  Variations around the line are random and normally distributed  For best results:  Always plot the data to verify linearity  Small correlation may imply that other variables are important 58

59 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Accuracy and Control of Forecasts  Error = Actual value - Forecast value  +ve = forecast too low, -ve = too high  Three measures of forecasts are used:  Mean absolute deviation (MAD)  Mean squared error (MSE)  Mean absolute percent error (MAPE)  Control charts  plot errors to see if within pre-set control limits  Tracking signal  Ratio of cumulative error and MAD  Error = Actual value - Forecast value  +ve = forecast too low, -ve = too high  Three measures of forecasts are used:  Mean absolute deviation (MAD)  Mean squared error (MSE)  Mean absolute percent error (MAPE)  Control charts  plot errors to see if within pre-set control limits  Tracking signal  Ratio of cumulative error and MAD 59

60 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Error, MAD, MSE and MAPE 60

61 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 MAD, MSE and MAPE MAD Easy to compute Weights errors linearly MSE Squares error More weight to large errors MAPE Puts errors in perspective above 70% satisfactory 61

62 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Error, MAD, MSE and MAPE: Example Compute MAD, MSE, and MAPE for the following data. 62

63 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Forecast Errors  bias = the sum of the forecast errors  +ve bias = frequent underestimation  -ve bias = frequent overestimation  bias = the sum of the forecast errors  +ve bias = frequent underestimation  -ve bias = frequent overestimation 63  possible sources of error include:  Model may be inadequate (things have changed)  Incorrect use of forecasting technique  Irregular variations

64 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Controlling the Forecasting Process  Control chart  A visual tool for monitoring forecast errors  Used to detect non-randomness in errors  Set limits that are multiples of the √MSE  Control chart  A visual tool for monitoring forecast errors  Used to detect non-randomness in errors  Set limits that are multiples of the √MSE 64  Forecasting errors are “in control” when only random errors, no errors from identifiable causes  “in control” if  All errors are within control limits  No patterns (e.g. trends or cycles) are present  errors outside limit = need corrective action

65 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Control Chart 65 0 Upper limit Lower limit Range of acceptable variation Time Error Need for corrective action

66 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Controlling Forecasts: Control Limits 66 Standard deviation of error = Control Limits = 0 ± 2 (or 3) s 95% of all errors should be within 2s 97.7% of all errors should be within 3s

67 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Control Chart Example 67 A F A - F Month (Sales) (Forecast) ErrorMSE Errors should be within ± 2(16.2). Lower limit = Upper limit = 32.4

68 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Control Chart Example 68 All the errors are within the control limits

69 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Below is a pharmacy’s actual sales and forecasted demand for a certain prescription drug for 5 months. How accurate is their forecast? Calculate MAD and MSE and create a control chart. MonthSalesForecast 1220n/a Pharmacy Forecast Control: Your Turn! 69

70 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 MonthSalesForecastAbs Error 1220n/a Sq. Error Pharmacy Forecast Control: Your Turn! 70

71 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Pharmacy Forecast Control: Your Turn! 71 All the errors are within the control limits Errors should be within ± 2(11.7). Lower limit = Upper limit = 23.4

72 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 Tracking Signal  Tracking signal  ratio of cumulative error to MAD  can be plotted on a control chart  investigate if TS > 4 72 Tracking signal = ( Actual - forecast ) MAD 

73 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 7 True or False? When error values fall outside the limits of a control chart, this signals a need for corrective action Ans: True When all errors plotted on a control chart are either all positive, or all negative, this shows that the forecasting technique is performing adequately. Ans: False A random pattern of errors within the limits of a control chart signals a need for corrective action. Ans: False 73

74 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 8 Choosing a Forecasting Technique  No single technique works in every situation  Two most important factors  Cost  Accuracy  Other factors include availability of:  Historical data  Computers  Time needed to gather and analyze the data  Forecast horizon 74

75 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 8 Choosing a Forecast Technique 75 Source: J. Holton Wilson and D. Allison-Koerber, “Combining Subjective and Objective Forecasts Improves Results,” Journal of Business Forecasting Methods & Systems, 11(3) Fall 1992, p. 4.

76 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 8 Choosing a Forecast Technique Source: C. L. Jain, “Benchmarking Forecasting Models,” Journal of Business Forecasting Methods & Systems, Fall 2002, pp. 18–20,

77 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. LO 8 Which technique? Sales for a product have been fairly consistent over several years, although showing a steady upward trend. The company wants to understand what drives sales. The best forecasting technique would be: A)trend models B)judgmental methods C)moving averages D)regression models E)exponential smoothing techniques Ans: D 77

78 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Learning Checklist  Describe at least three judgmental forecasting methods.  Describe the components of a time series model, and explain averaging techniques and solve typical problems.  Describe trend forecasting and solve typical problems.  Describe seasonality forecasting and solve typical problems.  Describe associative models and solve typical problems. 78

79 Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Learning Checklist  Identify uses of demand forecasts  Distinguish between forecasting time frames  Describe common features of forecasts  List the elements of a good forecast and steps of forecasting process,  Contrast different forecasting approaches.  Describe three measures of forecast accuracy, and two ways of controlling forecasts, and solve typical problems.  Identify the major factors to consider when choosing a forecasting technique. 79


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