Presentation is loading. Please wait.

Presentation is loading. Please wait.

Exponential Smoothing with Trend Adjustment - continued

Published byLanny Hartanto Modified over 6 years ago

Similar presentations

Presentation on theme: "Exponential Smoothing with Trend Adjustment - continued"— Presentation transcript:

1 Exponential Smoothing with Trend Adjustment - continued
Ft = Last period’s forecast + (Last period’s actual – Last period’s forecast) Ft = Ft-1 +  (At-1 – Ft-1) or Tt = (Forecast this period - Forecast last period) + (1-)(Trend estimate last period Tt = (Ft - Ft-1) + (1- )Tt-1 PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

2 Exponential Smoothing with Trend Adjustment - continued
Ft = exponentially smoothed forecast of the data series in period t Tt = exponentially smoothed trend in period t At = actual demand in period t  = smoothing constant for the average  = smoothing constant for the trend PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

3 Comparing Actual and Forecasts
PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

4 Guidelines for Selecting Forecasting Model
You want to achieve: No pattern or direction in forecast error Error = (Yi - Yi) = (Actual - Forecast) Seen in plots of errors over time Smallest forecast error Mean square error (MSE) Mean absolute deviation (MAD) ^ PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

5 Pattern of Forecast Error
Trend Not Fully Accounted for Desired Pattern Time (Years) Error Time (Years) Error PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

6 Forecast Error Equations
Mean Square Error (MSE) Mean Absolute Deviation (MAD) Mean Absolute Percent Error (MAPE) PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

7 Selecting Forecasting Model Example
You’re a marketing analyst for Hasbro Toys. You’ve forecast sales with a linear model & exponential smoothing. Which model do you use? Actual Linear Model Exponential Smoothing Year Sales Forecast Forecast (.9) PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

8 Linear Model Evaluation
Y i 1 2 4 ^ 0.6 1.3 2.0 2.7 3.4 Year 1998 1999 2000 2001 2002 Total 0.4 -0.3 0.0 -0.7 Error 0.16 0.09 0.00 0.49 0.36 1.10 Error2 0.3 0.7 |Error| Actual 0.40 0.30 0.35 0.15 1.20 MSE = Σ Error2 / n = / 5 = MAD = Σ |Error| / n = / 5 = MAPE = 100 Σ|absolute percent errors|/n= 1.20/5 = 0.240 PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

9 Exponential Smoothing Model Evaluation
Year 1998 1999 2000 2001 2002 Total Y i 1 2 4 1.0 0.0 1.9 0.1 2.0 3.8 0.2 0.3 ^ Error 0.00 0.01 0.04 0.05 Error2 |Error| Actual 0.10 MSE = Σ Error2 / n = / 5 = MAD = Σ |Error| / n = / 5 = MAPE = 100 Σ |Absolute percent errors|/n = 0.10/5 = 0.02 PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

10 Exponential Smoothing Model Evaluation
Linear Model: MSE = Σ Error2 / n = / 5 = MAD = Σ |Error| / n = / 5 = MAPE = 100 Σ|absolute percent errors|/n= 1.20/5 = 0.240 Exponential Smoothing Model: MSE = Σ Error2 / n = / 5 = MAD = Σ |Error| / n = / 5 = MAPE = 100 Σ |Absolute percent errors|/n = 0.10/5 = 0.02 PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

11 Tracking Signal Measures how well the forecast is predicting actual values Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD) Good tracking signal has low values Should be within upper and lower control limits PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

12 Tracking Signal Equation
PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

13 Tracking Signal Computation
Mo Fcst Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

14 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 Error = Actual - Forecast = = -10 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

15 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 RSFE =  Errors = NA + (-10) = -10 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

16 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 Abs Error = |Error| = |-10| = 10 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

17 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 Cum |Error| =  |Errors| = NA + 10 = 10 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

18 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum |Error| MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 MAD =  |Errors|/n = 10/1 = 10 PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

19 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 TS = RSFE/MAD = -10/10 = -1 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

20 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 Error = Actual - Forecast = = -5 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

21 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 RSFE =  Errors = (-10) + (-5) = -15 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

22 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 Abs Error = |Error| = |-5| = 5 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

23 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 15 Cum Error =  |Errors| = = 15 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

24 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 15 7.5 MAD =  |Errors|/n = 15/2 = 7.5 |Error| PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

25 Tracking Signal Computation
Mo Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 15 7.5 -2 |Error| TS = RSFE/MAD = -15/7.5 = -2 PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

26 Plot of a Tracking Signal
Signal exceeded limit Tracking signal Upper control limit + MAD Acceptable range - Lower control limit Time PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

27 Tracking Signals Forecast Actual demand Tracking Signal
PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J

Download ppt "Exponential Smoothing with Trend Adjustment - continued"

Similar presentations

Forecasting OPS 370.

Forecasting OPS 370.
4/16/2015 1 Ardavan Asef-Vaziri Variable of interest Time Series Analysis.

4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis.
© 1997 Prentice-Hall, Inc. S2 - 1 Principles of Operations Management Forecasting Chapter S2.

© 1997 Prentice-Hall, Inc. S2 - 1 Principles of Operations Management Forecasting Chapter S2.
PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc.,

PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc.,
Bina Nusantara Model Ramalan Pertemuan 14: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008.

Bina Nusantara Model Ramalan Pertemuan 14: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008.
Prepared by Lee Revere and John Large

Prepared by Lee Revere and John Large
4-1 Operations Management Forecasting Chapter 4 - Part 2.

4-1 Operations Management Forecasting Chapter 4 - Part 2.
© 2006 Prentice Hall, Inc.4 – 1  Short-range forecast  Up to 1 year, generally less than 3 months  Purchasing, job scheduling, workforce levels, job.

© 2006 Prentice Hall, Inc.4 – 1  Short-range forecast  Up to 1 year, generally less than 3 months  Purchasing, job scheduling, workforce levels, job.
Forecasting 5 June 2001. Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice.

Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice.
Forecasting.

Forecasting.
PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc.,

PowerPoint presentation to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc.,
T18-04 - 1 T18-04 Linear Trend Forecast Purpose Allows the analyst to create and analyze the Linear Trend forecast. The MAD and MSE for the forecast.

T T18-04 Linear Trend Forecast Purpose Allows the analyst to create and analyze the "Linear Trend" forecast. The MAD and MSE for the forecast.
Chapter 3 Forecasting McGraw-Hill/Irwin

Chapter 3 Forecasting McGraw-Hill/Irwin
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ 07458 PERTEMUAN 14.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ PERTEMUAN 14.
T18-05 - 1 T18-05 Trend Adjusted Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the Trend Adjusted Exponential Smoothing

T T18-05 Trend Adjusted Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Trend Adjusted Exponential Smoothing"
Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 4-1 Operations Management Forecasting Chapter 4.

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J Operations Management Forecasting Chapter 4.
4 Forecasting PowerPoint presentation to accompany Heizer and Render

4 Forecasting PowerPoint presentation to accompany Heizer and Render
Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Similar presentations

Ads by Google