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Chapter 3 forecasting.ppt
- 1. 3-1 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Chapter 3 Forecasting
- 2. 3-2 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting FORECAST: • A statement about the future • Used to help managers – Plan the system – Plan the use of the system
- 3. 3-3 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Forecast Uses • Plan the system – Generally involves long-range plans related to: • Types of products and services to offer • Facility and equipment levels • Facility location • Plan the use of the system – Generally involves short- and medium-range plans related to: • Inventory management • Workforce levels • Purchasing • Budgeting
- 4. 3-4 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting • 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 I see that you will get an A this quarter. Common Features
- 5. 3-5 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Elements of a Good Forecast Timely Accurate Reliable Written
- 6. 3-6 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting 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 Gather and analyze data Step 5 Make the forecast Step 6 Monitor the forecast “The forecast”
- 7. 3-7 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Types of Forecasts • Judgmental - uses subjective inputs (qualitative) • Time series - uses historical data assuming the future will be like the past (quantitative) • Associative models - uses explanatory variables to predict the future
- 8. 3-8 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Judgmental Forecasts (Qualitative) •Consumer surveys •Delphi method •Executive opinions – Opinions of managers and staff •Sales force.
- 9. 3-9 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting
- 10. 3-10 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Time Series Forecasts (Quantitative) • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Irregular variations - caused by unusual circumstances • Random variations - caused by chance • CYCLE- wave like variations lasting more than one year
- 11. 3-11 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Forecast Variations Trend Irregular variation Cycles Seasonal variations 90 89 88 Figure 3-1 cycle
- 12. 3-12 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting The Forecast of Forecasts • Naïve • Simple Moving Average • Weighted Moving Average • Exponential Smoothing • ES with Trend and Seasonality
- 13. 3-13 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting • Simple to use • Virtually no cost • Data analysis is nonexistent • Easily understandable • Cannot provide high accuracy Naïve Forecast
- 14. 3-14 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting NAÏVE METHOD • No smoothing of data Period 1 2 3 4 5 6 7 8 Average Demand 74 86 88 Forecast 98 90 change 12 2
- 15. 3-15 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Techniques for Averaging • Moving average • Weighted moving average • Exponential smoothing
- 16. 3-16 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Simple Moving Average • Smoothes out randomness by averaging positive and negative random elements over several periods • n - number of periods (this example uses 4) Period 1 2 3 4 5 6 7 Demand 74 90 100 60 80 90 Forecast 81 82.5 82.5
- 17. 3-17 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Points to Know on Moving Averages • Pro: Easy to compute and understand • Con: All data points were created equal…. …. Weighted Moving Average
- 18. 3-18 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Weighted Moving Average • Similar to a moving average methods except that it assigns more weight to the most recent values in a time series. • n -- number of periods ai – weight applied to period t-i+1 1 2 3 Alpha Period 1 2 3 4 5 6 7 8 Average Demand 46 48 47 23 40 Forecast 32.70 35.60 a t 1 n t i i 1 i t 1 t A F 0.6 0.3 0.1
- 19. 3-19 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Exponential Smoothing • Simpler equation, equivalent to WMA a – exponential smoothing parameter (0< a<1) • ) ( 1 1 1 t t t t F A F F a a 0.1 Period 1 2 3 4 5 6 7 8 Average Demand 74 90 100 60 Forecast 72 72.2 73.98
- 20. 3-20 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting F2 = 37 + (0.30)(37-37) = 37 F3 =37+ (0.30)(40-37) = 37.9 Exponential Smoothing (α=0.30) PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 ) ( 1 1 1 t t t t F A F F a
- 21. 3-21 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting FORECAST, Ft + 1 PERIOD MONTH DEMAND (a = 0.3) (a = 0.5) 1 Jan 37 – – 2 Feb 40 37.00 37.00 3 Mar 41 37.90 38.50 4 Apr 37 38.83 39.75 5 May 45 38.28 38.37 6 Jun 50 40.29 41.68 7 Jul 43 43.20 45.84 8 Aug 47 43.14 44.42 9 Sep 56 44.30 45.71 10 Oct 52 47.81 50.85 11 Nov 55 49.06 51.42 12 Dec 54 50.84 53.21 13 Jan – 51.79 53.61 Exponential Smoothing (cont.)
- 22. 3-22 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Linear Trend Equation • b is the line slope. Yt = a + bt 0 1 2 3 4 5 t Y a
- 23. 3-23 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Calculating a and b b = n (ty) - t y n t2 - ( t)2 a = y - b t n Yes… Linear Regression!!
- 24. 3-24 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Linear Trend Equation 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
- 25. 3-25 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting 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 Look on page 85
- 26. 3-26 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Disadvantage of simple linear regression 1-apply only to linear relationship with an independent variable. 2-one needs a considerable amount of data to establish the relationship ( at least 20). 3-all observations are weighted equally
- 27. 3-27 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Forecast Accuracy • Forecast error – difference between forecast and actual demand – MAD • mean absolute deviation – MAPD • mean absolute percent deviation – Cumulative error – Average error or bias
- 28. 3-28 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Mean Absolute Deviation (MAD) where t = period number At = demand in period t Ft = forecast for period t n = total number of periods = absolute value At - Ft n MAD =
- 29. 3-29 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting MAD Example 1 37 37.00 – – 2 40 37.00 3.00 3.00 3 41 37.90 3.10 3.10 4 37 38.83 -1.83 1.83 5 45 38.28 6.72 6.72 6 50 40.29 9.69 9.69 7 43 43.20 -0.20 0.20 8 47 43.14 3.86 3.86 9 56 44.30 11.70 11.70 10 52 47.81 4.19 4.19 11 55 49.06 5.94 5.94 12 54 50.84 3.15 3.15 557 49.31 53.39 PERIOD DEMAND, At Ft (a =0.3) (At - Ft) |At - Ft| At - Ft n MAD = = = 4.85 53.39 11
- 30. 3-30 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Other Accuracy Measures Mean absolute percent deviation (MAPD) MAPD = |At - Ft| At Cumulative error E = et Average error (E )= et n
- 31. 3-31 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Sources of forecast errors • The model may be inadequate. • Irregular variation may be occur. • The forecasting technique may be used incorrectly or the results misinterpreted. • There are always random variation in the data.
- 32. 3-32 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting End Notes • The two most important factors in choosing a forecasting technique: – Cost – Accuracy • Keep it SIMPLE!