Slide 1

Forecasting for Operations Decisions

W S WilliamForecasting is the art and science of predicting future events. Institute of Business Forecasting3-3Elements of a Good ForecastTimelyAccurateReliableMeaningfulWrittenEasy to useKey Issues in ForecastingChoice of forecasting horizon (a week, a month etc.)

A forecasting method with desired accuracy.

The unit of forecasting ( gross sales, individual product demand etc.)Forecast HorizonForecast horizon is the period for which forecast is prepared

Long-Range (years)( e.g. Process selection, Capacity addition)Medium-Range (months)(e.g. Manpower planning, procurement of long lead time items)Short-Range (weeks)(e.g. Production schedules, overtimes etc.)Examples of Production Resource ForecastsForecast HorizonTime SpanItem Being ForecastUnits of MeasureLong-RangeYears Product lines Factory capacities Planning for new products Capital expenditures Facility location or expansion R&DDollars, tons, etc.Medium-RangeMonths Product groups Department capacities Sales planning Production planning and budgetingDollars, tons, etc.Short-RangeWeeks Specific product quantities Machine capacities Planning Purchasing Scheduling Workforce levels Production levels Job assignmentsPhysical units of products12Principles of ForecastingForecasting is almost always wrong

Every forecast should include an estimate of the forecast error

The greater the degree of aggregation, the more accurate the forecast

Long-term forecasts are usually less accurate than short-term forecastsForecasting MethodsBroadly, forecasting methods fall under two categories:

Qualitative Methods : These are subjective in nature (Executive Opinion, Market Research , Delphi Method)Quantitative Methods: They use mathematical or simulation methods base d on historical demand or relationships between variables.Extrapolated or Time Series (Use past data to forecast future)

Explanatory or Causal Method (Establishes a relationship between dependent and independent variables); y= f(x)

Components of DemandHorizontal Component

Trend Component

Seasonal ComponentSimple Moving AverageAn averaging period (AP) is given or selectedThe forecast for the next period is the arithmetic average of the AP most recent actual demandsIt is called a simple average because each period used to compute the average is equally weighted. . . moreSimple Moving AverageIt is called moving because as new demand data becomes available, the oldest data is not usedBy increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response)By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)Simple Moving Average

Lets develop 3-week and 6-week moving average forecasts for demand. Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts 15

Simple Moving AverageSlide 13 of 5516

Simple Moving AverageSlide 14 of 5517Weighted Moving AverageThis is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equalThis allows more recent demand data to have a greater effect on the moving average, therefore the forecast. . . moreWeighted Moving AverageThe weights must add to 1.0 and generally decrease in value with the age of the dataThe distribution of the weights determine impulse response of the forecastWeighted Moving Average

Determine the 3-period weighted moving average forecast for period 4Weights (adding up to 1.0): t-1:.5t-2:.3t-3:.2

20Moving Average MethodStep1: Select the number of periods for which moving average will be computed, thus number N is called an order of moving average

Step 2: Take the average demand for the most recent N periods. This average demand then becomes the forecast for the next period.3-19Exponential SmoothingPremise--The most recent observations might have the highest predictive value.Therefore, we should give more weight to the more recent time periods when forecasting.Ft = Ft-1 + (At-1 - Ft-1)3-20Associative ForecastingPredictor variables - used to predict values of variable interest

Regression - technique for fitting a line to a set of points

Least squares line - minimizes sum of squared deviations around the lineSimple Linear RegressionRelationship between one independent variable, X, and a dependent variable, Y.Assumed to be linear (a straight line)Form: Y = a + bXY = dependent variableX = independent variable a = y-axis intercept b = slope of regression lineSimple Linear Regression Modelb is similar to the slope. However, since it is calculated with the variability of the data in mind, its formulation is not as straight-forward as our usual notion of slope Yt = a + bx0 1 2 3 4 5 x (weeks)Y35Calculating a and b

36Regression Equation Example

Develop a regression equation to predict sales based on these five points.37

Regression Equation ExampleSlide 25 of 5538y = 143.5 + 6.3t 13514014515015516016517017518012345PeriodSalesSalesForecastRegression Equation ExampleSlide 26 of 5539Forecast AccuracyAccuracy is the typical criterion for judging the performance of a forecasting approachAccuracy is how well the forecasted values match the actual valuesMonitoring Accuracy Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach

Accuracy can be measured in several waysMean absolute deviation (MAD)Mean squared error (MSE)Mean Squared Error (MSE)MSE = (Syx)2Small value for Syx means data points tightly grouped around the line and error range is small. The smaller the standard error the more accurate the forecast. MSE = 1.25(MAD)When the forecast errors are normally distributedExample--MADMonthSalesForecast1220n/a2250255321020543003205325315Determine the MAD for the four forecast periods31Solution

MonthSalesForecastAbs Error1220n/a225025553210205543003202053253151040323-32Tracking SignalTracking signal= (Actual-forecast)MADTracking signalRatio of cumulative error to MADBias Persistent tendency for forecasts to beGreater or less than actual values.Criteria for Selecting a Forecasting MethodCost

Accuracy

Data available

Time span

Nature of products and services

Impulse response and noise dampeningReasons for Ineffective ForecastingNot involving a broad cross section of peopleNot recognizing that forecasting is integral to business planningNot recognizing that forecasts will always be wrong (think in terms of interval rather than point forecasts)Not forecasting the right things (forecast independent demand only)Not selecting an appropriate forecasting method (use MAD to evaluate goodness of fit)Not tracking the accuracy of the forecasting modelsThank youSheet: Sheet1WeekDemandSheet: Sheet1WeekDemand3-Week6-Week682.6666666666666727.6666666666666788.0854.6666666666666768.6666666666666876.3333333333334802.0842.6666666666666815.3333333333334833.3333333333334844.0856.6666666666666866.5867.0854.8333333333334Sheet1WeekDemand3-Week6-Week1650267837204785682.675859727.676920788.007850854.67768.678758876.33802.009892842.67815.3310920833.33844.0011789856.67866.5012844867.00854.83

&APage &P

Sheet1

&APage &PDemand3-Week6-WeekWeekDemand

Sheet: Sheet1WeekDemand4.0Sheet: Sheet1Sheet: Sheet2Sheet: Sheet3Sheet: Sheet4Sheet: Sheet5Sheet: Sheet6Sheet: Sheet7Sheet: Sheet8Sheet: Sheet9Sheet: Sheet10Sheet: Sheet11Sheet: Sheet12Sheet: Sheet13Sheet: Sheet14Sheet: Sheet15Sheet: Sheet16WeekSalesSheet: Sheet1Sheet: Sheet2Sheet: Sheet3Sheet: Sheet4Sheet: Sheet5Sheet: Sheet6Sheet: Sheet7Sheet: Sheet8Sheet: Sheet9Sheet: Sheet10Sheet: Sheet11Sheet: Sheet12Sheet: Sheet13Sheet: Sheet14Sheet: Sheet15Sheet: Sheet16WeekWeek*WeekSalesWeek*Sales1.01.0150.0150.02.04.0157.0314.03.09.0162.0486.04.016.0166.0664.05.025.0177.0885.03.055.0162.42499.0AverageSumAverageSum