Operations management 12th stevenson ch03 forecasting

Chapter Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc All rights reserved Chapter 3: Learning Objectives  You should be able to: Instructor Slides List the elements of a good forecast Outline the steps in the forecasting process Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each Compare and contrast qualitative and quantitative approaches to forecasting Describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems Explain three measures of forecast accuracy Compare two ways of evaluating and controlling forecasts Assess the major factors and trade-offs to consider when choosing a forecasting technique 3-2 Forecast Forecast – a statement about the future value of a variable of interest  We make forecasts about such things as weather, demand, and resource availability  Forecasts are an important element in making informed decisions Instructor Slides 3-3 Forecasts affect decisions and activities throughout an organization Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services Two Important Aspects of Forecasts Expected level of demand  The level of demand may be a function of some structural variation such as trend or seasonal variation Accuracy  Related to the potential size of forecast error Instructor Slides 3-5 Features Common to All Forecasts Techniques assume some underlying causal system that existed in the past will persist into the future Forecasts are not perfect Forecasts for groups of items are more accurate than those for individual items Forecast accuracy decreases as the forecasting horizon increases Instructor Slides 3-6 Elements of a Good Forecast The forecast  should be timely  should be accurate  should be reliable  should be expressed in meaningful units  should be in writing  technique should be simple to understand and use  should be cost effective Instructor Slides 3-7 Steps in the Forecasting Process Determine the purpose of the forecast Establish a time horizon Obtain, clean, and analyze appropriate data Select a forecasting technique Make the forecast Monitor the forecast Instructor Slides 3-8 Forecasting Approaches  Qualitative Forecasting  Qualitative techniques permit the inclusion of soft information such as:  Human factors  Personal opinions  Hunches  These factors are difficult, or impossible, to quantify  Quantitative Forecasting  Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast  These techniques rely on hard data Judgmental Forecasts Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts  Executive opinions  Salesforce opinions  Consumer surveys  Delphi method Techniques for Cycles  Cycles are similar to seasonal variations but are of longer duration  Explanatory approach  Search for another variable that relates to, and leads, the variable of interest  Housing starts precede demand for products and services directly related to construction of new homes  If a high correlation can be established with a leading variable, an equation can be developed that describes the relationship, enabling forecasts to be made Instructor Slides 3-49 Associative Forecasting Techniques Associative techniques are based on the development of an equation that summarizes the effects of predictor variables Predictor variables - variables that can be used to predict values of the variable of interest Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms Instructor Slides 3-50 Simple Linear Regression Regression - a technique for fitting a line to a set of data points  Simple linear regression - the simplest form of regression that involves a linear relationship between two variables The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion) Instructor Slides 3-51 Least Squares Line yc = a + bx where yc = Predicted (dependent) variable x = Predictor (independent) variable b = Slope of the line a = Value of yc when x = (i.e., the height of the line at the y intercept) and b= n( ∑ xy ) − ( ∑ x )( ∑ y ) n( ∑ x ) − ( ∑ x ) y − b∑ x ∑ a= or y − b x n where n = Number of paired observations Instructor Slides 3-52 Standard Error Standard error of estimate  A measure of the scatter of points around a regression line  If the standard error is relatively small, the predictions using the linear equation will tend to be more accurate than if the standard error is larger Se = ∑( y − y ) c n−2 where S e = standard error of estimate y = y value of each data point n = number of data points Instructor Slides 3-53 Correlation Coefficient  Correlation, r  A measure of the strength and direction of relationship between two variables  Ranges between -1.00 and +1.00 r= n( ∑ xy ) − ( ∑ x )( ∑ y ) n( ∑ x ) − ( ∑ x ) n( ∑ y ) − ( ∑ y )  r2, square of the correlation coefficient  A measure of the percentage of variability in the values of y that is “explained” by the independent variable  Ranges between and 1.00 Instructor Slides 3-54 Simple Linear Regression Assumptions Variations around the line are random Deviations around the average value (the line) should be normally distributed Predictions are made only within the range of observed values Instructor Slides 3-55 Forecast Accuracy and Control Forecasters want to minimize forecast errors  It is nearly impossible to correctly forecast real-world variable values on a regular basis  So, it is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs Forecast accuracy should be an important forecasting technique selection criterion  Error = Actual – Forecast  If errors fall beyond acceptable bounds, corrective action may be necessary Instructor Slides 3-56 Forecast Accuracy Metrics Actual ∑ MAD = ( Actual ∑ MSE = t − Forecastt MAD weights all errors evenly n t − Forecastt ) MSE weights errors according to their squared values n −1 MAPE = Instructor Slides ∑ Actual t − Forecastt ×100 Actual t n MAPE weights errors according to relative error 3-57 Forecast Error Calculation Actual Forecast (A) (F) 107 110 125 Period (A-F) Error |Error| Error [|Error|/Actual]x100 -3 2.80% 121 4 16 3.20% 115 112 3 2.61% 118 120 -2 1.69% 108 109 1 0.93% Sum 13 39 11.23% n=5 n-1 = n=5 MAD MSE MAPE = 2.6 = 9.75 = 2.25% Instructor Slides 3-58 Issues to consider: Always plot the line to verify that a linear relationship is appropriate The data may be time-dependent  If they are use analysis of time series use time as an independent variable in a multiple regression analysis A small correlation may indicate that other variables are important Instructor Slides 3-59 Monitoring the Forecast  Tracking forecast errors and analyzing them can provide useful insight into whether forecasts are performing satisfactorily  Sources of forecast errors  The model may be inadequate  Irregular variations may have occurred  The forecasting technique has been incorrectly applied  Random variation  Control charts are useful for identifying the presence of non-random error in forecasts  Tracking signals can be used to detect forecast bias Instructor Slides 3-60 Choosing a Forecasting Technique Factors to consider  Cost  Accuracy  Availability of historical data  Availability of forecasting software  Time needed to gather and analyze data and prepare a forecast  Forecast horizon Instructor Slides 3-61 Using Forecast Information  Reactive approach  View forecasts as probable future demand  React to meet that demand  Proactive approach  Seeks to actively influence demand  Advertising  Pricing  Product/service modifications  Generally requires either an explanatory model or a subjective assessment of the influence on demand Instructor Slides 3-62 Operations Strategy  The better forecasts are, the more able organizations will be to take advantage of future opportunities and reduce potential risks  A worthwhile strategy is to work to improve short-term forecasts  Accurate up-to-date information can have a significant effect on forecast accuracy:  Prices  Demand  Other important variables  Reduce the time horizon forecasts have to cover  Sharing forecasts or demand data through the supply chain can improve forecast quality Instructor Slides 3-63 ... the forecasting process Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each Compare and contrast qualitative and quantitative approaches to forecasting. .. Instructor Slides 3-7 Steps in the Forecasting Process Determine the purpose of the forecast Establish a time horizon Obtain, clean, and analyze appropriate data Select a forecasting technique Make the... forecasting technique Make the forecast Monitor the forecast Instructor Slides 3-8 Forecasting Approaches  Qualitative Forecasting  Qualitative techniques permit the inclusion of soft information