OPERATIONS MANAGEMENT INTEGRATING MANUFACTURING AND SERVICES FIFTH EDITION Mark M. Davis Janelle Heineke Copyright ©2005, The McGraw-Hill Companies, Inc. PowerPoint Presentation by Charlie Cook, The University of West Alabama

SUPPLEMENT PowerPoint Presentation by Charlie Cook The University of West Alabama Copyright © 2005 The McGraw-Hill Companies. All rights reserved. Forecasting 12

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–3 SUPPLEMENT OBJECTIVES Introduce the basic concepts of forecasting and its importance within an organization. Identify several of the more common forecasting methods and how they can improve the performance of both manufacturing and service operations. Provide a framework for understanding how forecasts are developed. Demonstrate that errors exist in all forecasts and show how to measure and assess these errors. Discuss some of the software programs that are available for developing forecasting models.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–4 Managerial Issues Recognizing the increased importance of forecasting in both manufacturing and services. How to go about implementing forecasting at all levels in the organization. Understanding how managers can use the various forecasting methods to decide when to add manufacturing capacity and where to locate retail service outlets for maximum sales.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–5 Comparing the Costs and Benefits of Forecasting Exhibit S12.1

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–6 Types of Forecasting Qualitative Techniques –Nonquantitative forecasting techniques based on expert opinions and intuition. Typically used when there are no data available. Time Series Analysis –Analyzing data by time periods to determine if trends or patterns occur. Causal Relationship Forecasting –Relating demand to an underlying factor other than time.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–7 Forecasting Techniques Used in Business Exhibit S12.2 Source: Adapted from Chaman L. Jain, “Benchmarking Forecasting Models,” The Journal of Business Forecasting, Fall 2002, pp. 18–20, 30. Reprinted by permission of the Institute of Business Forecasting.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–8 Forecasting Techniques and Common Models: Qualitative Methods Exhibit S12.3

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–9 Forecasting Techniques and Common Models: Time Series Analysis Exhibit S12.3

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–10 Forecasting Techniques and Common Models: Causal Relationships Exhibit S12.3

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–11 Comparison of Forecasting Techniques Exhibit S12.4

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–12 Components of Demand Average Demand for the Period Trends Seasonal Influence Cyclical Elements Random Variation

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–13 Historical Monthly Product Demand Consisting of a Growth Trend, Cyclical Factor, and Seasonal Demand Exhibit S12.5

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–14 Common Types of Trends Exhibit S12.6

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–15 Time Series Analysis Simple Moving Average –Average over a given number of time periods that is updated by replacing the data in the oldest period with that in the most recent period. F t =Forecasted sales for the period A t-1 =Actual sales in period t-1 n=Number of periods in the moving average

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–16 Forecast Demand Based on a Three- and a Nine- Week Simple Moving Average Exhibit S12.7

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–17 Moving Average Forecast of Three- and Nine- Week Periods versus Actual Demand Exhibit S12.8

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–18 Time Series Analysis (cont’d) Weighted Moving Average –Simple moving average where weights are assigned to each time period in the average. The sum of all of the weights must equal one. F t =Forecasted sales for the period A t-1 =Actual sales in period t-1 w t-1 =Weight assigned to period t-1 n=Number of periods in the moving average

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–19 Time Series Analysis (cont’d) Exponential Smoothing –Times series forecasting technique that does not require large amounts of historical data. Benefits of Using Exponential Models –Models are surprisingly accurate. –Model formulation is fairly easy. –Readily understood by users. –Little computation is required. –Limited volume of historical data.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–20 Time Series Analysis (cont’d) Exponential Smoothing Constant Alpha (  ) –A value between 0 and 1 that is used to minimize the error between historical demand and respective forecasts. –Use small values for  if demand is stable, larger values for  if demand is fluctuating. –Adaptive forecasting Two or more predetermined values of alpha Computed values of alpha

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–21 Time Series Analysis (cont’d) Exponential Smoothing Formula F t =Exponentially smoothed forecast for period t F t-1 =Exponentially smoothed forecast for prior period A t-1 =Actual demand in the prior period  =Desired response rate, or smoothing constant

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–22 Time Series Analysis (cont’d) Exponential Smoothing With a Trend Constant Delta (  ) to correct for lagging behind a trend: FIT t =Forecast including trend  =Trend constant

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–23

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–24

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–25 Exponential Forecasts versus Actual Demands for Units of a Product over Time Showing the Forecast Lag Exhibit S12.9

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–26 Forecasting Errors in Time Series Analysis Sources of Error –Projection of past trends into the future –Bias errors Consistent mistakes causing a forecast to be too high or too low: wrong relationships, wrong trend line, errors in shifting seasonal demand, undetected trends. –Random errors Unexplainable variations (noise) in a forecast that cannot be explained by the forecast model.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–27 Forecasting Errors in Time Series Analysis (cont’d) Measurement of Error –MAD (mean absolute deviation)—Average forecasting error based on the absolute difference between actual and forecast demands. t=Period number A t =Actual demand for period t F t =Forecast for period t n=Total number of periods | |=Absolute value

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–28 A Normal Distribution with a Mean = 0 and a MAD = 1 Exhibit S12.10

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–29 Forecasting Errors in Time Series Analysis (cont’d) Measurement of Error (cont’d) –Tracking signal—a measurement of error that indicates if the forecast is staying within specified limits of the actual demand. RSFE=Running sum of forecast errors MAD=Mean absolute deviation

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–30 Computing the Mean Absolute Deviation (MAD), the Running Sum of Forecast Errors (RSFE), and the Tracking Signal from Forecast and Actual Data Exhibit S12.11

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–31 A Plot of the Tracking Signals Calculated in Exhibit S12.11 Exhibit S12.12

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–32 The Percentages of Points Included within the Control Limits for a Range of 0 to 4 MADs Exhibit S12.13

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–33 Forecasting Errors in Time Series Analysis (cont’d) Mean Absolute Percentage Error (MAPE) –Used to determine the forecasting errors as a percentage of the actual demand. A t =Actual demand F t =Forecast demand n=number of periods in forecast

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–34 Linear Regression Analysis –A forecasting technique that assumes that the relationship between the dependent and independent variables is a straight line. Y=Dependent variable to be solved for a=Y intercept b=Slope of the XY relationship X=Independent variable (e.g., units of time)

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–35 Least Squares Regression Line Exhibit S12.14

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–36 Least Squares Regression Analysis Exhibit S12.15A

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–37 Linear Regression Analysis (cont’d) Standard Error of the Estimate –A measure of the dispersion of data about a regression line. –How well (or closely) the regression line fits the data.  ˆ      n n i ii YX yy S

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–38 Using the Regression Function on a Spreadsheet: Standard Error of the Estimate Exhibit S12.15B

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–39 Causal Relationship Forecasting Leading Indicator –An event whose occurrence causes, presages or influences the occurrence of another subsequent event. Warning strips on the highway Prerequisites to a college course An engagement ring

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–40 Causal Relationship: Sales to Housing Starts Exhibit S12.16

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–41 Causal Relationship Forecasting Reliability of Data –Coefficient of determination The proportion of variability in demand that can be attributed to an independent variable.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–42 Exhibit S12.17

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–43 Causal Relationship Forecasting (cont’d) Reliability of Data –Mean squared error—A measure of the variability in the data about a regression line.  2 2 ˆ     n yy ii MSE

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–44 Causal Relationship Forecasting (cont’d) Multiple Regression Analysis –Forecasting using more than one independent variable; measuring the combined effects of several independent variables on the dependent variable.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–45 The Application of Forecasting to Service Operations Real-time data acquisition makes information immediately available to decision makers. –Point-of-Sale (POS) equipment –Yield management—attempts to maximize the revenues of a firm.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–46 Causal Relationship Forecasting (cont’d) Neural Networks –A forecasting technique simulating human learning that develops complex relationships between the model inputs and outputs.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–47 Types of Forecasting Software Being Used Exhibit S12.18 Source: Nada R. Sanders and Karl B. Manrodt, “Forecasting Software in Practice: Use, Satisfaction, and Performance,” Interfaces 33, no. 5 (September–October 2003), pp. 90–93.

Copyright © 2005 The McGraw-Hill Companies. All rights reserved. McGraw-Hill/Irwin S12–48 Average MAPE* for Different Types of Forecasting Software Exhibit S12.19 * Mean Absolute Percentage of Error