Chapter 8 - ForecastingLearning ObjectivesLearning Objectives con’tPrinciples of ForecastingTypes of Forecasting MethodsSlide 6Types of Forecasting ModelsQualitative MethodsQuantitative MethodsTime Series ModelsTime Series PatternsSlide 12Time Series Models con’tSlide 14Time Series ProblemTime Series Problem SolutionForecasting trend problem: a company uses exponential smoothing with trend to forecast usage of its lawn care products. At the end of July the company wishes to forecast sales for August. July demand was 62. The trend through June has been 15 additional gallons of product sold per month. Average sales have been 57 gallons per month. The company uses alpha+0.2 and beta +0.10. Forecast for August.Linear Trend LineForecasting TrendForecasting SeasonalitySeasonality con’tSeasonality problem: a university must develop forecasts for the next year’s quarterly enrollments. It has collected quarterly enrollments for the past two years. It has also forecast total enrollment for next year to be 90,000 students. What is the forecast for each quarter of next year?Causal ModelsLinear RegressionLinear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year.Correlation Coefficient How Good is the Fit?Multiple RegressionMeasuring Forecast ErrorMeasuring Forecasting AccuracyAccuracy & Tracking Signal Problem: A company is comparing the accuracy of two forecasting methods. Forecasts using both methods are shown below along with the actual values for January through May. The company also uses a tracking signal with ±4 limits to decide when a forecast should be reviewed. Which forecasting method is best?Selecting the Right Forecasting ModelForecasting SoftwareGuidelines for Selecting SoftwareOther Forecasting MethodsCollaborative Planning Fore-casting & Replenishment (CPFR)Forecasting within OM: How it all fits togetherForecasting within OM con’tSlide 38Forecasting Across the OrganizationChapter 8 HighlightsChapter 8 Highlights con’tHighlights con’tHomework Help© Wiley 2010 1Chapter 8 - ForecastingOperations ManagementbyR. Dan Reid & Nada R. Sanders4th Edition © Wiley 2010© Wiley 2010 2Learning ObjectivesIdentify Principles of ForecastingExplain the steps in the forecasting processIdentify types of forecasting methods and their characteristicsDescribe time series and causal models© Wiley 2010 3Learning Objectives con’tGenerate forecasts for data with different patterns: level, trend, seasonality, and cyclicalDescribe causal modeling using linear regressionCompute forecast accuracyExplain how forecasting models should be selected© Wiley 2010 4Principles of ForecastingMany types of forecasting models that differ in complexity and amount of data & way they generate forecasts:1. Forecasts are rarely perfect2. Forecasts are more accurate for grouped data than for individual items3. Forecast are more accurate for shorter than longer time periods© Wiley 2010 5Types of Forecasting MethodsDecide what needs to be forecastLevel of detail, units of analysis & time horizon requiredEvaluate and analyze appropriate dataIdentify needed data & whether it’s availableSelect and test the forecasting modelCost, ease of use & accuracyGenerate the forecastMonitor forecast accuracy over time© Wiley 2010 6Types of Forecasting MethodsForecasting methods are classified into two groups:© Wiley 2010 7Types of Forecasting ModelsQualitative methods – judgmental methodsForecasts generated subjectively by the forecasterEducated guesses Quantitative methods – based on mathematical modeling:Forecasts generated through mathematical modeling© Wiley 2010 8Qualitative Methods© Wiley 2010 9Quantitative MethodsTime Series Models:Assumes information needed to generate a forecast is contained in a time series of dataAssumes the future will follow same patterns as the pastCausal Models or Associative ModelsExplores cause-and-effect relationshipsUses leading indicators to predict the futureHousing starts and appliance sales© Wiley 2010 10Time Series ModelsForecaster looks for data patterns as Data = historic pattern + random variationHistoric pattern to be forecasted: Level (long-term average) – data fluctuates around a constant meanTrend – data exhibits an increasing or decreasing pattern Seasonality – any pattern that regularly repeats itself and is of a constant lengthCycle – patterns created by economic fluctuations Random Variation cannot be predicted© Wiley 2010 11Time Series Patterns© Wiley 2010 12Time Series ModelsNaive:The forecast is equal to the actual value observed during the last period – good for level patternsSimple Mean:The average of all available data - good for level patternsMoving Average:The average value over a set time period (e.g.: the last four weeks)Each new forecast drops the oldest data point & adds a new observationMore responsive to a trend but still lags behind actual datatA1tFn/AFt1tn/AFt1t© Wiley 2010 13Time Series Models con’tWeighted Moving Average:All weights must add to 100% or 1.00 e.g. Ct .5, Ct-1 .3, Ct-2 .2 (weights add to 1.0)Allows emphasizing one period over others; above indicates more weight on recent data (Ct=.5)Differs from the simple moving average that weighs all periods equally - more responsive to trends tt1tACF© Wiley 2010 14Time Series Models con’tExponential Smoothing:Most frequently used time series method because of ease of use and minimal amount of data neededNeed just three pieces of data to start:Last period’s forecast (Ft)Last periods actual value (At)Select value of smoothing coefficient, ,between 0 and 1.0If no last period forecast is available, average the last few periods or use naive methodHigher values (e.g. .7 or .8) may place too much weight on last period’s random variation tt1tFα1αAF © Wiley 2010 15Time Series ProblemDetermine forecast for periods 7 & 82-period moving average4-period moving average2-period weighted moving average with t-1 weighted 0.6 and t-2 weighted 0.4Exponential smoothing with alpha=0.2 and the period 6 forecast being 375Period Actual1 3002 3153 2904 3455 3206 3607