1. Copyright © 2014 by McGraw-Hill Education (Asia). All rights reserved. 3 Forecasting

2. 3-2 Learning Objectives  List the elements of a good forecast.  Outline the steps in the forecasting process.  Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each.  Compare and contrast qualitative and quantitative approaches to forecasting.

3. 3-3 Learning Objectives  Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems.  Describe two measures of forecast accuracy.  Describe two ways of evaluating and controlling forecasts.  Identify the major factors to consider when choosing a forecasting technique.

4. 3-4  A statement about the future value of a variable of interest such as demand.  Forecasting is used to make informed decisions.  Long-range  Short-range Forecasts

5. 3-5 Forecasts  Forecasts affect decisions and activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product/service design

6. 3-6 AccountingCost/profit estimates FinanceCash flow and funding Human resourcesHiring/recruiting/training MarketingPricing, promotion, strategy MISIT/IS systems, services OperationsSchedules, MRP, workloads Product/service designNew products and services Uses of Forecasts

7. I see that you will get an A this semester. 3-7  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 Features of Forecasts

8. 3-8 Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

9. 3-9 Steps in Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Obtain, clean and analyze data Step 5 Make the forecast Step 6 Monitor the forecast “The forecast”

10. 3-10 Types of Forecasts  Judgmental: uses subjective inputs  Time series: uses historical data, assuming the future will be like the past  Associative models: uses explanatory variables to predict the future

11. 3-11 Judgmental Forecasts  Executive opinions  Sales force opinions  Consumer surveys  Outside opinions/experts  Delphi method  Opinions of managers and staff  Achieves a consensus forecast

12. 3-12 Time Series Forecasts  Trend: long-term movement in data  Seasonality: short-term regular variations in data  Cycles: wavelike variations of more than one year’s duration  Irregular variations: caused by unusual circumstances  Random variations: caused by chance

13. 3-13 Forecast Variations Trend Irregular variation Seasonal variations 90 89 88 Figure 3.1 Cycles

14. 3-14 Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.

15. 3-15  Simple to use  Virtually no cost  Quick and easy to prepare  Data analysis is nonexistent  Easily understandable  Cannot provide high accuracy  Can be a standard for accuracy Naive Forecasts

16. 3-16  Stable time series data  F(t) = A(t-1)  Seasonal variations  F(t) = A(t-n)  Data with trends  F(t) = A(t-1) + (A(t-1) – A(t-2)) Uses of Naive Forecasts

17. 3-17 Techniques for Averaging  Moving average  Weighted moving average  Exponential smoothing

18. 3-18 Moving Averages  Moving average: A technique that averages a number of recent actual values, updated as new values become available.  Weighted moving average: More recent values in a series are given more weight in computing the forecast. F t = MA n = n A t-n + … A t-2 + A t-1 F t = WMA n = n w n A t-n + … w n-1 A t-2 + w 1 A t-1

19. 3-19 Simple Moving Average Actual MA3 MA5 F t = MA n = n A t-n + … A t-2 + A t-1

20. 3-20 Exponential Smoothing  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. F t = F t-1 +  ( A t-1 - F t-1 )

21. 3-21 Exponential Smoothing  Weighted averaging method based on previous forecast plus a percentage of the forecast error  A-F is the error term,  is the % feedback F t = F t-1 +  ( A t-1 - F t-1 )

22. 3-22 Example 3: Exponential Smoothing

23. 3-23 Picking a Smoothing Constant .1 .4 Actual

24. 3-24 Common Nonlinear Trends Parabolic Exponential Growth Figure 3.5

25. 3-25 Linear Trend Equation  F t = Forecast for period t  t = Specified number of time periods  a = Value of F t at t = 0  b = Slope of the line F t = a + bt 0 1 2 3 4 5 t FtFt

26. 3-26 Calculating a and b b = n(ty) - ty nt 2 - ( t) 2 a = y - bt n   

27. 3-27 Linear Trend Equation Example

28. 3-28 Linear Trend Calculation y = 143.5 + 6.3t a= 812- 6.3(15) 5 = b= 5 (2499)- 15(812) 5(55)- 225 = 12495-12180 275-225 = 6.3 143.5

29. 3-29 Techniques for Seasonality  Seasonal variations  Regularly repeating movements in series values that can be tied to recurring events  Seasonal relative  Percentage of average or trend  Centered moving average  A moving average positioned at the center of the data that were used to compute it

30. 3-30 Associative Forecasting  Predictor variables: used to predict values of variable interest  Regression: technique for fitting a line to a set of points  Least squares line: minimizes sum of squared deviations around the line

31. 3-31 Linear Model Seems Reasonable A straight line is fitted to a set of sample points. Computed relationship

32. 3-32 Linear Regression Assumptions  Variations around the line are random  Deviations around the line normally distributed  Predictions are being made only within the range of observed values  For best results:  Always plot the data to verify linearity  Check for data being time-dependent  Small correlation may imply that other variables are important

33. 3-33 Forecast Accuracy  Error: difference between actual value and predicted value  Mean Absolute Deviation (MAD)  Average absolute error  Mean Squared Error (MSE)  Average of squared error  Mean Absolute Percent Error (MAPE)  Average absolute percent error

34. 3-34 MAD, MSE, and MAPE MAD = Actualforecast   n MSE = Actualforecast ) - 1 2   n ( MAPE = Actualforecas t  n / Actual*100) 

35. 3-35 MAD, MSE, and MAPE  MAD  Easy to compute  Weights errors linearly  MSE  Squares error  More weight to large errors  MAPE  Puts errors in perspective

36. 3-36 Example 10

37. 3-37 Controlling the Forecast  Control chart  A visual tool for monitoring forecast errors  Used to detect non-randomness in errors  Forecasting errors are in control if  All errors are within the control limits  No patterns, such as trends or cycles, are present

38. 3-38 Sources of Forecast Errors  Model may be inadequate  Irregular variations  Incorrect use of forecasting technique

39. 3-39 Tracking Signal Tracking signal = ( Actual - forecast ) MAD   Tracking signal –Ratio of cumulative error to MAD Bias: Persistent tendency for forecasts to be greater or less than actual values.

40. 3-40 Choosing a Forecasting Technique  No single technique works in every situation  Two most important factors  Cost  Accuracy  Other factors include the availability of:  Historical data  Computers  Time needed to gather and analyze the data  Forecast horizon

41. 3-41 Operations Strategy  Forecasts are basis for many decisions  Work to improve short-term forecasts  Accurate short-term forecasts improve  Profits  Lower inventory levels  Reduce inventory shortages  Improve customer service levels  Enhance forecasting credibility

42. 3-42 Exponential Smoothing

43. 3-43 Linear Trend Equation

44. 3-44 Simple Linear Regression