Lecture Introduction to Management Science with Spreadsheets: Chapter 2 - Stevenson, Ozgur

Chapter 2 Forecasting, after completing this chapter, you should be able to: Explain the importance of forecasting in organizations, describe the three major approaches to forecasting, use a variety of techniques to make forecasts, measure the accuracy of a forecast over time using various methods,...

Introduction to Management Science with Spreadsheets Stevenson and Ozgur First Edition Part Introduction to Management Science and Forecasting Chapter 2 Forecasting McGraw­Hill/Irwin Copyright © 2007 by The McGraw­Hill Companies, Inc. All rights reserved Learning Objectives After completing this chapter, you should be able to: Explain the importance of forecasting in organizations Describe the three major approaches to forecasting Use a variety of techniques to make forecasts Measure the accuracy of a forecast over time using various methods Determine when a forecast can be improved Discuss the main considerations in selecting a forecasting technique Utilize Excel to solve various forecasting problems Copyright © 2007 The McGraw­Hill  McGraw­ Companies. All rights reserved.   Hill/Irwin  2–2 The The Importance Importance of of Forecasting Forecasting • Forecasting – is important because it helps reduce uncertainty – provides decision makers with an improved picture of probable future events and, thereby, enable decision makers to plan accordingly – is used for planning the system itself – is used for planning the use of the system – as a process has an inherent tendency for inaccuracy Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–3 The The Importance Importance of of Forecasting Forecasting • The Forecasting Process Determine the purpose of the forecast Determine the time horizon Select an appropriate technique Identify the necessary data, and gather it if necessary Make the forecast Monitor forecast errors in order to determine if the forecast is performing adequately If it is not, take appropriate corrective action Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–4 Approaches Approaches to to Forecasting Forecasting • Qualitative Forecasts – are based on judgment and/or opinion rather than on the analysis of “hard” data • Forecasts That Use Time Series Data – involve the assumption that past experience reflects probable future experience (i.e., the past movements or patterns in the data will persist into the future) • Explanatory Models – incorporate one or more variables that are related to the variable of interest and, therefore, they can be used to predict future values of that variable Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–5 Selecting Selecting the the Forecasting Forecasting Technique Technique • Factors affecting the choice of the forecasting technique to be used: – the importance (purpose) of the forecast – the desired accuracy of the forecast – the cost of developing the forecast – resources available to support and conduct the forecasting process – the planning horizon (long- or short-term) – the sophistication of the users of the forecast – A good rule is to choose the simplest technique that gives acceptable results Copyright © 2007 The McGraw­Hill  McGraw­ Companies. All rights reserved.   Hill/Irwin  2–6 Table Table2–7 2–7 Forecasting ForecastingApproaches Approaches Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–7 Table Table2–7 2–7 Forecasting ForecastingApproaches Approaches(cont’d) (cont’d) Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–8 Figure Figure22–1 –1 Examples ExamplesofofSimple SimplePatterns PatternsSometimes SometimesFound FoundininTime Time Series SeriesData Data Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–9 Figure Figure22–2 –2 Data Datawith withTrend Trendand andSeasonal SeasonalVariations Variations Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–10 Source: E Turban, Jay Aronson, and Ting-Peng Liang, Decision Support Systems and Intelligence Systems, 7th ed (Upper Saddle River, NJ: Prentice Hall, 2005), p 109 Exhibit Exhibit22–14 –14 Linear LinearRegression-Explanatory Regression-ExplanatoryModel ModelOutput Output Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–50 Table Table22–5 –5 Expansion Expansionof ofData DataUsed UsedininSimple SimpleRegression RegressionSection Section Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–51 Exhibit Exhibit22–15 –15 Input InputBox Boxfor forMultiple MultipleRegression Regression Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–52 Exhibit Exhibit22–16 –16 Multiple MultipleRegression RegressionOutput Outputwith withExcel Excel Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–53 Summarizing Summarizing Forecast Forecast Accuracy Accuracy • The mean absolute deviation (MAD) – measures the average forecast error over a number of periods, without regard to the sign of the error: • The mean squared error (MSE) – is the average squared error experienced over a number of periods Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–54 Example Example2-7 2-7 Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–55 Figure Figure22–15 –15 Monitoring MonitoringForecast ForecastErrors Errors Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–56 Relative Relative Measures Measures of of Forecast Forecast Accuracy Accuracy • Percentage error (PE) – for a given time series data measures the percentage point deviation of the forecasted value from the actual value • Mean percentage error (MPE) – measures the forecast bias • Mean absolute percentage error (MAPE) – measures overall forecast accuracy Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–57 Example Example2-8 2-8 Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–58 Example Example2-8 2-8cont’d cont’d Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–59 Example Example2-8 2-8cont’d cont’d Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–60 Tracking Tracking Signal Signal • The tracking signal – Is the ratio of cumulative forecast error at any point in time to the corresponding MAD at that point in time – A value of a tracking signal that is beyond the action limits suggests the need for corrective action Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–61 Example Example2-9 2-9 Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–62 Exhibit Exhibit2–17 2–17 Measuring MeasuringForecast ForecastAccuracy AccuracyUsing UsingMAD, MAD,MSE, MSE,MPE, MPE,and and MAPE MAPE Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–63 Table Table2–6 2–6 Comparison ComparisonofofTypes Typesof ofForecasts Forecasts Copyright © 2007 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin  2–64 ... Companies. All rights reserved.   McGraw­ Hill/Irwin ? ?2? ??31 Example Example 2-4 2- 4 Copyright ©? ?20 07 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin ? ?2? ?? 32 Example Example 2-4 2- 4cont’d cont’d A plot of... Wizard Copyright ©? ?20 07 The McGraw­Hill  Companies. All rights reserved.   McGraw­ Hill/Irwin ? ?2? ? ?21 Table Table 22? ??1 –1 Values ValuesofofΣΣt,t,t2, t2,and andΣΣt2t2 Copyright ©? ?20 07 The McGraw­Hill ... McGraw­ Hill/Irwin ? ?2? ? ?22 Example Example 2-3 2- 3 Monthly demand for Dan’s Doughnuts over the past nine months for trays (six dozen per tray) of sugar doughnuts was Mar 1 12 Apr 125 May 120 Jun 133 Jul