1. 3-1 Forecasting I see that you will get an A this semester. 10 th ed.

2. 3-2 FORECAST:  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

3. 3-3  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

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

6. 3-6 Examples of Forecasting Uses AccountingCost/profit estimates FinanceCash flow and funding Human ResourcesHiring/recruiting/training MarketingPricing, promotion, strategy MISIT/IS systems, services OperationsSchedules, MRP, workloads Product/service designTiming of new products and services

7. 3-7 Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

8. 3-8 Steps in the 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”

9. 3-9 Types of Forecasts  Judgmental - uses subjective inputs (e.g., sales force estimates).  Time series - uses historical data assuming the future will be like the past. Time could be in weeks, months, years, etc., and is based on t=1,2,3,…  Associative models - uses explanatory variables to predict the future. It suggests a causal relationship, such as personal consumption being based on per capita income of households.

10. 3-10 Judgmental Forecasts  Executive opinions  Sales force opinions  Consumer surveys  Outside opinion  Delphi method  Opinions of managers and staff  Achieves a consensus forecast

11. 3-11 Time Series Forecasts  Trend - long-term movement in data  Seasonality - short-term regular variations in data  Cycle – wavelike variations of more than one year’s duration  Irregular variations - caused by unusual circumstances that are not random  Random variations - caused by chance

12. 3-12 Forecast Variations Trend Irregular variatio n Seasonal variations 90 89 88 Cycles

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15. 3-15 Some Common Time Series Techniques or Time Series Forecasting Models  Naïve forecasts  Moving average  Weighted moving average  Exponential smoothing

16. 3-16 Naive Forecasts The forecast for any period equals the previous period’s actual value.

17. 3-17  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 Naïve Forecasts

18. 3-18  F t = A t-1 Formula for Naïve Forecasts This is the forecasting that is the most responsive to changes in the past actual demand.

19. 3-19 Moving Average Formula  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 =w n A t-n + … w n-1 A t-2 + w 1 A t-1

20. 3-20 Simple Moving Average Actual Demand MA 3 MA 5 F t = MA n = n A t-1 + A t-2 + … A t-n

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 Exponential Smoothing Formula 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.  The symbol “α” is the Greek letter “alpha.” Alpha is called the smoothing constant. Note that alpha varies from zero to one. F t = F t-1 +  ( A t-1 - F t-1 )

23. 3-23 Exponential Smoothing

24. 3-24 Picking a Smoothing Constant .1 .4 Actual

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26. 3-26 Homework Problem Referring to page 118 in the text, do problems 2a and 2b, but skip problem 2b(1) which asks for a linear trend equation. Complete and partial solutions of homework problems are found on the slides at the end of this session. For this problem and for all homework problems, do not go to the solutions until you have made a strong effort to solve the problems.

27. 3-27 Linear Trend Equation  F t = Forecast for period t  t = The time period being forecasted  a = Value of F t at t = 0  b = Slope of the line F t = Y t = a + bt 0 1 2 3 4 5 t FtFt

28. 3-28 Calculating a and b b = n(ty) - ty nt 2 - ( t) 2 a = y - bt n    Y t = a + bt where b and a follow from the following formulae:

29. 3-29 Linear Trend Equation Example Calculations of b and a are from the sums given in the table on the left. n = 10 F t = Y t = a + bt F t = Y t = a + bt = 67.78 + 5.79t

30. 3-30 Plot of Previous Slide Y t

31. 3-31 Homework Problem Referring to page 118 in the text, do 2b(1), which asks for a linear trend equation. Also, do problem 2c. However, change problem 2c to read as follows: “Which method seems MOST appropriate? Why?”

32. 3-32 Associative Forecasting  Associative models - uses explanatory variables to predict the future. It suggests a causal relationship, such as personal consumption being based on per capita income of households

33. 3-33 Example of an Associative Forecast: Using x to Predict y A straight line is fitted to a set of sample points. Computed relationship

34. 3-34 Example of an Associative Forecast Equation for Automobiles (with several variables, and nonlinear)

35. 3-35 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

36. 3-36 Some Measures of Forecasting Accuracy MAD = Actualforecast   n MSE = Actualforecast ) - 1 2   n ( Note that the errors are taken for each of the past n periods where the actual demand is known.

37. 3-37 Some Characteristics of MAD and MSE  MAD  Easy to compute  Weights errors linearly  MSE  Squares error  More weight to large errors

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

39. 3-39 The next two slides ask some basic questions about forecasting, and give some examples of measuring forecasting error. These slides make up an in-class assignment. You can try to answer the questions on the slides. However, if you have difficulty with all or some of the questions, we will do them in class. At least become familiar with the questions before the next class. If you find the next two slides difficult to read, simply magnify the size of the slides. If you can, please try to print a hardcopy of the next two slides and bring them to class. If you can set the resolution of your printer, it is suggested that you set it to a high resolution for the best printed copy. In-class assignment

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42. 3-42 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

43. 3-43 Good Operations Strategy  Understand that forecasts are the basis for many decisions  Work to improve short-term forecasts  Understand that accurate short-term forecasts have benefits for the following:  Profits  Lower inventory levels  Reduce inventory shortages  Improve customer service levels  Enhance forecasting credibility

44. 3-44 Supply Chain Forecasts  Sharing forecasts with suppliers can  Improve forecast quality in the supply chain  Lower costs  Lead to shorter lead times

45. 3-45 Common Nonlinear Trends Parabolic Exponential Growth

46. 3-46 Exponential Smoothing

47. 3-47 Linear Trend Equation

48. 3-48 Simple Linear Regression

49. 3-49 Homework Problem Solutions Month Sales F M A M J J A S 20 0 2a

50. 3-50 2b(1) Hence, n = 7,  t = 28,  t 2 = 140 tYtYt2t2 1 19 1 2 18 364 3 15 459 4 20 8016 5 18 9025 6 22 13236 7 20 14049 28 132 542140 For the September forecast, t = 8, and Y t = 16.86 +.50(8) = 20.86 Therefore, Y t = 16.86 +.50t To solve this problem, we need to plug the appropriate values into the equation F t = Y t = a + bt

51. 3-51 2b(2)

52. 3-52 F t = F t-1 +  ( A t-1 - F t-1 ) 2b(3) MonthForecast =F(old)+.20[Actual – F(old)] April18.8=19+.20[18 – 19] May18.04=18.8+.20[15 – 18.8] June18.43=18.04+.20[20 – 18.04] July18.34=18.43+.20[18 – 18.43] August19.07=18.34+.20[22 – 18.34] September19.26=19.07+.20[20 – 19.07] Answer is 19.26 (or actually, 19,260 units).

53. 3-53 F t = A t-1 This formula is telling us that the forecast in period t is simply the actual demand for period t-1, or simply, the actual demand of the previous period. For the September forecast, the answer would be the actual demand in August. Hence, the answer is 20 (or 20,000 units) 2b(4)

54. 3-54 2b(5) F t = WMA n =w n A t-n + … w n-1 A t-2 + w 1 A t-1 =.6 (20) +.3(22) +.1(18) = 20.4, (or 20,400 units) Note that the more recent actual demand values are usually given more weight in computing the forecast.

55. 3-55 2c Change this problem to read “which method seems the most appropriate?” To logically find the answer, go back to the plot in 2a, and make your best guess as to where the September actual demand might be on the plot or graph. Hence, you need to see a pattern on the plot. Once you have September on the plot, find the method from part b of the problem which comes the closest to your guess on the plot. The answer should be the linear trend equation.

56. 3-56 See you next class