1. Forecasting Production and Operations Management 3-1

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. 3-2/42

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. 3-3/42

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 Introduction 3-4/42

5. Introduction Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product/service design 3-5/42

6. Introduction -- Uses of Forecasts 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 3-6/42

7. I see that you will get an A this semester. 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 3-7/42

8. Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use 3-8/42

9. 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” 3-9/42

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 3-10/42

11. Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast 3-11/42

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 3-12/42

13. Time Series -- Forecast Variations Trend Irregular variation Seasonal variations 90 89 88 Cycles 3-13/42

14. Time Series -- 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. 3-14/42

15. Time Series -- Naive Forecasts 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 3-15/42

16. Time Series -- Uses of Naive Forecasts 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)) 3-16/42

17. Time Series -- Techniques for Averaging Moving average Weighted moving average Exponential smoothing 3-17/42

18. Time Series -- 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 3-18/42

19. Time Series -- Simple Moving Average Actual MA3 MA5 F t = MA n = n A t-n + … A t-2 + A t-1 3-19/42

20. Time Series -- 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 ) 3-20/42

21. Time Series -- 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 ) 3-21/42

22. Example 3: Exponential Smoothing Time Series -- Example 3: Exponential Smoothing 3-22/42

23. Time Series -- Picking a Smoothing Constant .1 .4 Actual 3-23/42

24. Time Series -- Common Nonlinear Trends Parabolic Exponential Growth 3-24/42

25. Time Series – 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 3-25/42

26. Time Series – Calculating a and b b = n(ty) - ty nt 2 - ( t) 2 a = y - bt n    3-26/42

27. Time Series -- Linear Trend Equation Example 3-27/42

28. Time Series -- 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 3-28/42

29. Time Series -- 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 3-29/42

30. Time Series -- 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 3-30/42

31. Time Series -- Linear Model Seems Reasonable A straight line is fitted to a set of sample points. Computed relationship 3-31/42

32. Time Series -- 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 3-32/42

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 3-33/42

34. Forecast Accuracy -- MAD, MSE, and MAPE MAD = Actualforecast   n MSE = Actualforecast ) - 1 2   n ( MAPE = Actualforecast  n / Actual*100)  3-34/42

35. Forecast Accuracy -- MAD, MSE, and MAPE MAD Easy to compute Weights errors linearly MSE Squares error More weight to large errors MAPE Puts errors in perspective 3-35/42

36. Forecast Accuracy -- Example 3-36/42

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 3-37/42

38. Controlling the Forecast -- Sources of Forecast Errors Model may be inadequate Irregular variations Incorrect use of forecasting technique 3-38/42

39. Controlling the Forecast -- 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. 3-39/42

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 3-40/42

41. Forecasting -- Operations Strategy Forecasts are the 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 3-41/42

42. Supply Chain Forecasts Sharing forecasts with supply can Improve forecast quality in the supply chain Lower costs Shorter lead times 3-42/42