Logistics Management LSM 730 Lecture 23 Dr. Khurrum S. Mughal.

Slides:



Advertisements
Similar presentations
Agenda of Week V. Forecasting
Advertisements

© 1997 Prentice-Hall, Inc. S2 - 1 Principles of Operations Management Forecasting Chapter S2.
Time Series and Forecasting
Forecasting Demand ISQA 511 Dr. Mellie Pullman.
Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Lecture 3 Forecasting CT – Chapter 3.
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ PERTEMUAN 14.
Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.
Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Forecasting Operations Chapter 12 Roberta Russell & Bernard.
15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Time Series and Forecasting Chapter 16.
Time Series and Forecasting
Slides 13b: Time-Series Models; Measuring Forecast Error
Forecasting Chapter 15.
1 1 Slide © 2009 South-Western, a part of Cengage Learning Chapter 6 Forecasting n Quantitative Approaches to Forecasting n Components of a Time Series.
Slides by John Loucks St. Edward’s University.
LSS Black Belt Training Forecasting. Forecasting Models Forecasting Techniques Qualitative Models Delphi Method Jury of Executive Opinion Sales Force.
Operations and Supply Chain Management
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall7-1 Chapter 7: Forecasting.
Sales Management Sales Forecasting Topic 13. Sales Forecasting What is it? Why do it? Qualitative vs Quantitative Goal = Accuracy Commonly Done by Marketing.
Introduction to Forecasting COB 291 Spring Forecasting 4 A forecast is an estimate of future demand 4 Forecasts contain error 4 Forecasts can be.
Forecasting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
8-1 Forecasting Supply Chain Requirements CR (2004) Prentice Hall, Inc. Chapter 8 I hope you'll keep in mind that economic forecasting is far from a perfect.
© The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.
1 What Is Forecasting? Sales will be $200 Million!
Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
DSc 3120 Generalized Modeling Techniques with Applications Part II. Forecasting.
Forecasting Models Decomposition and Exponential Smoothing.
1 DSCI 3023 Forecasting Plays an important role in many industries –marketing –financial planning –production control Forecasts are not to be thought of.
MBA.782.ForecastingCAJ Demand Management Qualitative Methods of Forecasting Quantitative Methods of Forecasting Causal Relationship Forecasting Focus.
Introduction to Forecasting IDS 605 Spring Forecasting 4 A forecast is an estimate of future demand.
Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.
Lesson 4 -Part A Forecasting Quantitative Approaches to Forecasting Components of a Time Series Measures of Forecast Accuracy Smoothing Methods Trend Projection.
Forecasting. 預測 (Forecasting) A Basis of Forecasting In business, forecasts are the basis for budgeting and planning for capacity, sales, production and.
Maintenance Workload Forecasting
15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.
PowerPoint Slides by Robert F. BrookerCopyright (c) 2001 by Harcourt, Inc. All rights reserved. Managerial Economics in a Global Economy Chapter 5 Demand.
Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.
15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.
Economics 173 Business Statistics Lecture 26 © Fall 2001, Professor J. Petry
Time Series and Forecasting
MGS3100_03.ppt/Feb 11, 2016/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Time Series Forecasting Feb 11, 2016.
Quantitative Forecasting Methods (Non-Naive)
1 1 Chapter 6 Forecasting n Quantitative Approaches to Forecasting n The Components of a Time Series n Measures of Forecast Accuracy n Using Smoothing.
CHAPTER 12 FORECASTING. THE CONCEPTS A prediction of future events used for planning purpose Supply chain success, resources planning, scheduling, capacity.
Chapter 12 Forecasting. Lecture Outline Strategic Role of Forecasting in SCM Components of Forecasting Demand Time Series Methods Forecast Accuracy Regression.
Time Series and Forecasting Chapter 16 McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
1-1 Logistics Management LSM 730 Dr. Khurrum S. Mughal Lecture 22.
13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.
Assignable variation Deviations with a specific cause or source. forecast bias or assignable variation or MSE? Click here for Hint.
Managerial Decision Modeling 6 th edition Cliff T. Ragsdale.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 3 Forecasting.
Chapter 15 Forecasting. Forecasting Methods n Forecasting methods can be classified as qualitative or quantitative. n Such methods are appropriate when.
13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.
Chapter 3 Lecture 4 Forecasting. Time Series is a sequence of measurements over time, usually obtained at equally spaced intervals – Daily – Monthly –
T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.
Welcome to MM305 Unit 5 Seminar Dr. Bob Forecasting.
Welcome to MM305 Unit 5 Seminar Forecasting. What is forecasting? An attempt to predict the future using data. Generally an 8-step process 1.Why are you.
Forecasting Chapter 9.
Forecasting Methods Dr. T. T. Kachwala.
Forecasting techniques
Time Series Forecasts Trend - long-term upward or downward movement in data. Seasonality - short-term fairly regular variations in data related to factors.
Exponential Smoothing with Trend Adjustment - continued
Forecasting Elements of good forecast Accurate Timely Reliable
Forecasting Chapter 15.
Forecasting - Introduction
OUTLINE Questions? Quiz Go over homework Next homework Forecasting.
Exponential Smoothing
TIME SERIES MODELS – MOVING AVERAGES.
Presentation transcript:

Logistics Management LSM 730 Lecture 23 Dr. Khurrum S. Mughal

Moving Average Naive forecast Simple moving average demand in current period is used as next period’s forecast Simple moving average uses average demand for a fixed sequence of periods stable demand with no pronounced behavioral patterns Weighted moving average weights are assigned to most recent data

Exponential Smoothing Ft +1 = Dt + (1 - )Ft where: Ft +1 = forecast for next period Dt = actual demand for present period Ft = previously determined forecast for present period = weighting factor, smoothing constant

Effect of Smoothing Constant 0.0  1.0 If = 0.20, then Ft +1 = 0.20Dt + 0.80 Ft If = 0, then Ft +1 = 0Dt + 1 Ft = Ft Forecast does not reflect recent data If = 1, then Ft +1 = 1Dt + 0 Ft =Dt Forecast based only on most recent data

Classic Time Series Decomposition Model Basic formulation F = T  S  C  R where F = forecast T = trend S = seasonal index C = cyclical index (usually 1) R = residual index (usually 1) CR (2004) Prentice Hall, Inc.

Regression Forecasting Using Bobbie Brooks Sales Data (1) (2) (3) (4) (5) (6)= (2)/(5) Sales period Time Sales (Dt ) Trend value Seasonal Forecast period, t ($000s) Dt  t t2 (Tt ) index ($000s) Summer 1 $9,458 9,458 1 $12,053 0.78 Trans-season 2 11,542 23,084 4 12,539 0.92 Fall 3 14,489 43,467 9 13,025 1.11 Holiday 4 15,754 63,016 16 13,512 1.17 Spring 5 17,269 86,345 25 13,998 1.23 Summer 6 11,514 69,084 36 14,484 0.79 Trans-season 7 12,623 88,361 49 14,970 0.84 Fall 8 16,086 128,688 64 15,456 1.04 Holiday 9 18,098 162,882 81 15,942 1.14 Spring 10 21,030 210,300 100 16,428 1.28 Summer 11 12,788 140,668 121 16,915 0.76 Trans-season 12 16,072 192,864 144 17,401 0.92 Fall 13 ? 17,887 * $18,602 Holiday 14 ? 18,373 * 20,945 Totals 78 176,723 1,218,217 650 N = 12 å Dt å ´ t = 1,218,217 t2 = 650 = ( 176 , 723 / 12 ) = 14 , 726 . 92 = 78 / 12 = 6 . 5 Regression equation is: Tt = 11,567.08 + 486.13t *Forecasted values CR (2004) Prentice Hall, Inc. 8-35

Regression Analysis Basic formulation F = o  1X1  2X2  …  nXn Example Bobbie Brooks, a manufacturer of teenage women’s clothes, was able to forecast seasonal sales from the following relationship F = constant  1(Time)  2(consumer debt ratio) + 3(no. nonvendor accounts) CR (2004) Prentice Hall, Inc.

Combined Model Forecasting Combines the results of several models to improve overall accuracy. Consider the seasonal forecasting problem of Bobbie Brooks. Four models were used. Three of them were two forms of exponential smoothing and a regression model. The fourth was managerial judgement used by a vice president of marketing using experience. Each forecast is then weighted according to its respective error as shown below. Calculation of forecast weights Model type (1) Forecast error (2) Percent of total (3)= 1.0/(2) Inverse of proportion (4)= (3)/48.09 weights MJ 9.0 0.466 2.15 0.04 R 0.7 0.036 27.77 0.58 ES 1 1.2 0.063 15.87 0.33 2 8.4 0.435 2.30 0.05 Total 19.3 1.000 48.09 1.00 CR (2004) Prentice Hall, Inc.

Combined Model Forecasting (Cont’d) type (1) Model forecast (2) Weighting factor (3)= ´ Weighted proportion Regression model (R) $20,367,000 0.58 $11,813,000 Exponential Smoothing ES 1 20,400,000 0.33 6,732,000 Combined exponential smoothing-- regression model (ES 2 ) 17,660,000 0.05 883,000 Managerial judgment (MJ) 19,500,000 0.04 780,000 Weighted average forecast $20,208,000 Weighted Average Fall Season Forecast Using Multiple Forecasting Techniques CR (2004) Prentice Hall, Inc.

Multiple Model Errors CR (2004) Prentice Hall, Inc. 8-38