Chapter 12 Forecasting Operations Operations Management Management 66thth Edition Edition Roberta Russell & Bernard W Taylor, III Copyright 2009 John Wiley & Sons, Inc Beni Asllani University of Tennessee at Chattanooga Lecture Outline  Strategic Role of Forecasting in Supply Chain Management  Components of Forecasting Demand  Time Series Methods  Forecast Accuracy  Time Series Forecasting Using Excel  Regression Methods Copyright 2009 John Wiley & Sons, Inc 12-2 Forecasting  Predicting the future  Qualitative forecast methods  subjective  Quantitative forecast methods  based on mathematical formulas Copyright 2009 John Wiley & Sons, Inc 12-3 Forecasting and Supply Chain Management  Accurate forecasting determines how much inventory a company must keep at various points along its supply chain  Continuous replenishment     supplier and customer share continuously updated data typically managed by the supplier reduces inventory for the company speeds customer delivery  Variations of continuous replenishment     quick response JIT (just-in-time) VMI (vendor-managed inventory) stockless inventory Copyright 2009 John Wiley & Sons, Inc 12-4 Forecasting  Quality Management  Accurately forecasting customer demand is a key to providing good quality service  Strategic Planning  Successful strategic planning requires accurate forecasts of future products and markets Copyright 2009 John Wiley & Sons, Inc 12-5 Types of Forecasting Methods  Depend on    time frame demand behavior causes of behavior Copyright 2009 John Wiley & Sons, Inc 12-6 Time Frame  Indicates how far into the future is forecast  Short- to mid-range forecast  typically encompasses the immediate future  daily up to two years  Long-range forecast  usually encompasses a period of time longer than two years Copyright 2009 John Wiley & Sons, Inc 12-7 Demand Behavior  Trend  a gradual, long-term up or down movement of demand  Random variations  movements in demand that not follow a pattern  Cycle  an up-and-down repetitive movement in demand  Seasonal pattern  an up-and-down repetitive movement in demand occurring periodically Copyright 2009 John Wiley & Sons, Inc 12-8 Demand Demand Forms of Forecast Movement Random movement Time (b) Cycle Demand Demand Time (a) Trend Time (c) Seasonal pattern Copyright 2009 John Wiley & Sons, Inc Time (d) Trend with seasonal pattern 12-9 Forecasting Methods  Time series  statistical techniques that use historical demand data to predict future demand  Regression methods  attempt to develop a mathematical relationship between demand and factors that cause its behavior  Qualitative  use management judgment, expertise, and opinion to predict future demand Copyright 2009 John Wiley & Sons, Inc 12-10 Computing a Forecast with Seasonal Adjustment Copyright 2009 John Wiley & Sons, Inc 12-53 OM Tools Copyright 2009 John Wiley & Sons, Inc 12-54 Regression Methods  Linear regression  a mathematical technique that relates a dependent variable to an independent variable in the form of a linear equation  Correlation  a measure of the strength of the relationship between independent and dependent variables Copyright 2009 John Wiley & Sons, Inc 12-55 Linear Regression y = a + bx a = y-bx Σ xy - nxy b = Σ x2 - nx2 where a = intercept b = slope of the line Σ x x = = mean of the x data n Σ y y = n = mean of the y data Copyright 2009 John Wiley & Sons, Inc 12-56 Linear Regression Example x (WINS) y (ATTENDANCE) xy x2 6 7 36.3 40.1 41.2 53.0 44.0 45.6 39.0 47.5 145.2 240.6 247.2 424.0 264.0 319.2 195.0 332.5 16 36 36 64 36 49 25 49 49 346.7 2167.7 311 Copyright 2009 John Wiley & Sons, Inc 12-57 Linear Regression Example (cont.) x y b 49 = 346.9 = = 6.125 = 43.36 ∑xy - nxy2 = ∑x2 - nx2 (2,167.7) - (8)(6.125)(43.36) = (311) - (8)(6.125)2 = 4.06 a = y - bx = 43.36 - (4.06)(6.125) = 18.46 Copyright 2009 John Wiley & Sons, Inc 12-58 Linear Regression Example (cont.) Regression equation Attendance forecast for wins = 18.46 + y 4.06x y = 18.46 + 4.06(7) = 46.88, or 46,880 60,000 – 50,000 – Attendance, y 40,000 – 30,000 – 20,000 – Linear regression line, y = 18.46 + 4.06x 10,000 – | | | | | | | Wins, x Copyright 2009 John Wiley & Sons, Inc | | | | 10 12-59 Correlation and Coefficient of Determination  Correlation, r  Measure of strength of relationship  Varies between -1.00 and +1.00  Coefficient of determination, r2  Percentage of variation in dependent variable resulting from changes in the independent variable Copyright 2009 John Wiley & Sons, Inc 12-60 Computing Correlation r= r= n∑ xy - ∑ x∑ y [n∑ x2 - (∑ x)2] [n∑ y2 - (∑ y)2] (8)(2,167.7) - (49)(346.9) [(8)(311) - (49)2] [(8)(15,224.7) - (346.9)2] r = 0.947 Coefficient of determination r2 = (0.947)2 = 0.897 Copyright 2009 John Wiley & Sons, Inc 12-61 Regression Analysis with Excel Copyright 2009 John Wiley & Sons, Inc 12-62 Regression Analysis with Excel (cont.) Copyright 2009 John Wiley & Sons, Inc 12-63 Regression Analysis with Excel (cont.) Copyright 2009 John Wiley & Sons, Inc 12-64 Multiple Regression Study the relationship of demand to two or more independent variables y = β0 + β1x1 + β2x2 … + βkxk where β0 = the intercept β1, … , βk = parameters for the independent variables x1, … , xk = independent variables Copyright 2009 John Wiley & Sons, Inc 12-65 Multiple Regression with Excel Copyright 2009 John Wiley & Sons, Inc 12-66 Copyright 2009 J ohn Wiley & Sons, Inc All rights reserved Reproduction or translation of this work beyond that permitted in section 117 of the 1976 United States Copyright Act without express permission of the copyright owner is unlawful Request for further information should be addressed to the Permission Department, J ohn Wiley & Sons, Inc The purchaser may make back-up copies for his/her own use only and not for distribution or resale The Publisher assumes no responsibility for errors, omissions, or damages caused by the use of these programs or from the use of the information Copyright 2009herein John Wiley & Sons, Inc 12-67 ... in demand  Seasonal pattern  an up -and- down repetitive movement in demand occurring periodically Copyright 2009 John Wiley & Sons, Inc 12-8 Demand Demand Forms of Forecast Movement Random movement... Sons, Inc 12-7 Demand Behavior  Trend  a gradual, long-term up or down movement of demand  Random variations  movements in demand that not follow a pattern  Cycle  an up -and- down repetitive... historical demand data to predict future demand  Regression methods  attempt to develop a mathematical relationship between demand and factors that cause its behavior  Qualitative  use management