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Operations management forecasting
The document summarizes key concepts about forecasting from the 8th edition of the textbook "Operations Management" by William J. Stevenson. It discusses definitions of forecasting, the importance and uses of forecasts in various business functions. Methods of forecasting include qualitative judgmental forecasts, quantitative time series analysis, and associative models using explanatory variables. Specific forecasting techniques covered include naive forecasts, moving averages, exponential smoothing, trend analysis, and regression. The document also addresses evaluating forecast accuracy and controlling forecasts.
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Operations management forecasting
- 1. 3-1 Forecasting William J.Stevenson Operations Management 8th edition Chapter 3: Forecasting Presented by: Analyn Arienda Jessica Lhay Asaña Twinkle Constantino
- 2. 3-2 Forecasting FORECAST: Astatement about the future value of a variable of interest such as demand. Predictions about the future. Two important aspects of forecasts. One is the expected level of demand; the other is the degree of accuracy that can be assigned to a forecast (i.e., the potential size of forecast error). Forecasts may be Short range (e.g., an hour, day, week, or month), or Long range (e.g., the next six months, the next year, the next five years, or the life of a product or service)
- 3. 3-3 Forecasting Forecasts affectdecisions and activities throughout an organization Accounting, finance Human resources Marketing Management Information System Operations Product / service design
- 4. 3-4 Forecasting Accounting Cost/profitestimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services Uses of Forecasts
- 5. 3-5 Forecasting Assumescausal system past ==> future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.
- 6. 3-6 Forecasting Elements ofa Good Forecast Timely AccurateReliable Written
- 7. 3-7 Forecasting Steps inthe Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”
- 8. 3-8 Forecasting Approach inForecasting Qualitative methods consist mainly of subjective inputs, which often defy precise numerical description. It involve either the projection of historical data or the development of associative models that attempt to utilize causal (explanatory) variables to make a forecast. Quantitative consist mainly of analyzing objective, or hard, data. They usually avoid personal biases that sometimes contaminate qualitative methods. In practice, either approach or a combination of both approaches might be used to develop a forecast.
- 9. 3-9 Forecasting Types ofForecasts 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
- 10. 3-10 Forecasting 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 Forecasting Time SeriesForecasts 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 Random variations - caused by chance
- 12. 3-12 Forecasting Forecast Variations Trend Irregular variatio n Seasonalvariations 90 89 88 Figure 3.1 Cycles
- 13. 3-13 Forecasting 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.
- 14. 3-14 Forecasting Simpleto 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
- 15. 3-15 Forecasting Example: Naïve Forecasts PERIODACTUAL CHANGE FROM PREVIOUS VALUE FORECAST 1 50 2 53 3 3 53 + 3 = 56
- 16. 3-16 Forecasting Techniques forAveraging Moving average Weighted moving average Exponential smoothing
- 17. 3-17 Forecasting 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. MAn = n Aii = 1 n
- 18. 3-18 Forecasting Simple MovingAverage MAn = n Aii = 1 n 35 37 39 41 43 45 47 1 2 3 4 5 6 7 8 9 10 11 12 Actual MA3 MA5
- 19. 3-19 Forecasting 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. Ft = Ft-1 + (At-1 - Ft-1)
- 20. 3-20 Forecasting 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 Ft = Ft-1 + (At-1 - Ft-1)
- 21. 3-21 Forecasting Period ActualAlpha = 0.1 Error Alpha = 0.4 Error 1 42 2 40 42 -2.00 42 -2 3 43 41.8 1.20 41.2 1.8 4 40 41.92 -1.92 41.92 -1.92 5 41 41.73 -0.73 41.15 -0.15 6 39 41.66 -2.66 41.09 -2.09 7 46 41.39 4.61 40.25 5.75 8 44 41.85 2.15 42.55 1.45 9 45 42.07 2.93 43.13 1.87 10 38 42.36 -4.36 43.88 -5.88 11 40 41.92 -1.92 41.53 -1.53 12 41.73 40.92 Example 3 - Exponential Smoothing
- 22. 3-22 Forecasting Picking aSmoothing Constant 35 40 45 50 1 2 3 4 5 6 7 8 9 10 11 12 Period Demand .1 .4 Actual
- 23. 3-23 Forecasting Common NonlinearTrends Parabolic Exponential Growth Figure 3.5
- 24. 3-24 Forecasting Linear TrendEquation Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line Ft = a + bt 0 1 2 3 4 5 t Ft
- 25. 3-25 Forecasting Calculating aand b b = n (ty) - t y n t2 - ( t)2 a = y - b t n
- 26. 3-26 Forecasting Linear TrendEquation Example t y Week t2 Sales ty 1 1 150 150 2 4 157 314 3 9 162 486 4 16 166 664 5 25 177 885 t = 15 t2 = 55 y = 812 ty = 2499 (t) 2 = 225
- 27. 3-27 Forecasting Linear TrendCalculation 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
- 28. 3-28 Forecasting 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
- 29. 3-29 Forecasting Linear ModelSeems Reasonable A straight line is fitted to a set of sample points. 0 10 20 30 40 50 0 5 10 15 20 25 X Y 7 15 2 10 6 13 4 15 14 25 15 27 16 24 12 20 14 27 20 44 15 34 7 17 Computed relationship
- 30. 3-30 Forecasting 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
- 31. 3-31 Forecasting MAD, MSE,and MAPE MAD = Actual forecast n MSE = Actual forecast) -1 2 n ( MAPE = Actual forecas t n / Actual*100)(
- 32. 3-32 Forecasting Example 10 PeriodActual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100 1 217 215 2 2 4 0.92 2 213 216 -3 3 9 1.41 3 216 215 1 1 1 0.46 4 210 214 -4 4 16 1.90 5 213 211 2 2 4 0.94 6 219 214 5 5 25 2.28 7 216 217 -1 1 1 0.46 8 212 216 -4 4 16 1.89 -2 22 76 10.26 MAD= 2.75 MSE= 10.86 MAPE= 1.28
- 33. 3-33 Forecasting Controlling theForecast 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
- 34. 3-34 Forecasting Sources ofForecast errors Model may be inadequate Irregular variations Incorrect use of forecasting technique
- 35. 3-35 Forecasting Tracking Signal Trackingsignal = (Actual-forecast) MAD •Tracking signal –Ratio of cumulative error to MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values.
- 36. 3-36 Forecasting Choosing aForecasting 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
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- 38. 3-38 Forecasting Linear TrendEquation
- 39. 3-39 Forecasting Simple LinearRegression
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