Operations Management

Forecasting

Basic input in decision processTwo major aspect of forecastingExpected demandDegree of accuracy

What is Forecasting?

Short-range forecastUp to 1 year, generally less than 3 monthsPurchasing, job scheduling, workforce levels, job assignments, production levelsMedium-range forecast3 months to 3 yearsSales and production planning, budgetingLong-range forecast3+ yearsNew product planning, facility location, research and developmentForecasting Time Horizon

Common featuresAssumes same underlying causal system that existed in the past will continue in futureNot perfectGroup forecast will be more accurate compared to individual items.Accuracy decreases as time horizon increases

Economic forecastsAddress business cycle inflation rate, money supply, housing starts, etc.Technological forecastsPredict rate of technological progressImpacts development of new productsDemand forecastsPredict sales of existing products and servicesTypes of Forecast

Determine the use of the forecastSelect the items to be forecastedEstablish time horizon of the forecastSelect the forecasting model(s)Gather the dataMake the forecastMonitor forecastSeven Steps in Forecasting

Forecasting AccuracyImpossible to predict future values on regular basisForecasting error is bound to happenNeed to measure and control the forecasting errors.

Common Measures of Errors

Common Measures of Errors

Used when situation is vague and little data existNew productsNew technologyInvolves intuition, experienceQualitative MethodsForecasting Approaches

Jury of executive opinionSales force opinionConsumer SurveysOther ApproachesQualitative Methods

Naive approachMoving averagesExponential smoothingTrend projectionLinear regressionQuantitative Methods

Set of evenly spaced numerical dataObtained by observing response variable at regular time periodsForecast based only on past values, no other variables importantAssumes that factors influencing past and present will continue influence in futureTime Series Forecasting

Time Series Forecasting

Assumes demand in next period is the same as demand in most recent periode.g., If January sales were 68, then February sales will be 68Sometimes cost effective and efficientCan be good starting pointNave Approach

MA is a series of arithmetic means Used if little or no trendUsed often for smoothingProvides overall impression of data over timeMoving Average Approach

(12 + 13 + 16)/3 = 13 2/3(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 1/3Moving Average Example

Used when trend is present Older data usually less importantWeights based on experience and intuitionWeighted Moving Average

Form of weighted moving averageWeights decline exponentiallyMost recent data weighted mostRequires smoothing constant ()Ranges from 0 to 1Subjectively chosenInvolves little record keeping of past dataExponential Smoothing

New forecast =Last periods forecast+ a (Last periods actual demand Last periods forecast)Ft = Ft 1 + a(At 1 - Ft 1)whereFt=new forecastFt 1=previous forecasta=smoothing (or weighting) constant (0 a 1)Exponential Smoothing

The objective is to obtain the most accurate forecast no matter the techniqueWe generally do this by selecting the model that gives us the lowest forecast errorForecast error= Actual demand - Forecast value= At - FtChoosing

Fitting a trend line to historical data points to project into the medium to long-rangeLinear trends can be found using the least squares techniqueTrend Projections

Equations to calculate the regression variablesLeast Squares Method

OM - Forecasting PBS

The multiplicative seasonal model can adjust trend data for seasonal variations in demandSeasonal Variations In Data

Find average historical demand for each season Compute the average demand over all seasons Compute a seasonal index for each season Estimate next years total demandDivide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that seasonSteps in the process:Seasonal Variations In Data

Used when changes in one or more independent variables can be used to predict the changes in the dependent variableMost common technique is linear regression analysisWe apply this technique just as we did in the time series exampleAssociative Forecasting

Forecasting an outcome based on predictor variables using the least squares techniqueAssociative Forecasting

wherey=y-value of each data pointyc=computed value of the dependent variable, from the regression equationn=number of data pointsStandard Error of the Estimate

Computationally, this equation is considerably easier to useWe use the standard error to set up prediction intervals around the point estimateStandard Error of the Estimate

How strong is the linear relationship between the variables?Correlation does not necessarily imply causality!Coefficient of correlation, r, measures degree of associationValues range from -1 to +1Correlation

Correlation Coefficient

Coefficient of Determination, r2, measures the percent of change in y predicted by the change in xValues range from 0 to 1Easy to interpretFor the Nodel Construction example:r = .901r2 = .81Correlation

If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variablesComputationally, this is quite complex and generally done on the computerMultiple Regression Analysis

In the Nodel example, including interest rates in the model gives the new equation:An improved correlation coefficient of r = .96 means this model does a better job of predicting the change in construction salesSales = 1.80 + .30(6) - 5.0(.12) = 3.00Sales = $3,000,000Multiple Regression Analysis

Monitoring ForecastControl Chart

Tracking signal

Note:MADt = MAD t-1+(|Actual-Forecast|t-MADt-1)

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