*Operations ManagementTopic 6 Forecasting

*What is Forecasting?Process of predicting a future eventUnderlying basis of all business decisionsProductionInventoryPersonnelFacilities

*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 Horizons

*Seven Steps in ForecastingDetermine the use of the forecastSelect the items to be forecastedDetermine the time horizon of the forecastSelect the forecasting model(s)Gather the dataMake the forecastValidate and implement results

*Types of ForecastsEconomic 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 services

*Strategic Importance of ForecastingHuman Resources Hiring, training, laying off workersCapacity Capacity shortages can result in undependable delivery, loss of customers, loss of market shareSupply Chain Management Good supplier relations and price advantages

*The Realities!Forecasts are seldom perfectMost techniques assume an underlying stability in the systemProduct family and aggregated forecasts are more accurate than individual product forecasts

*Forecasting ApproachesUsed when situation is vague and little data existNew productsNew technologyInvolves intuition, experiencee.g., forecasting sales on InternetQualitative Methods

*Forecasting ApproachesUsed when situation is stable and historical data existExisting productsCurrent technologyInvolves mathematical techniquese.g., forecasting sales of color televisionsQuantitative Methods

*Overview of Quantitative ApproachesNaive approachMoving averagesExponential smoothingTrend projectionLinear regression

*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

*Components of DemandFigure 4.1

*Persistent, overall upward or downward patternChanges due to population, technology, age, culture, etc.Typically several years duration Trend Component

*Regular pattern of up and down fluctuationsDue to weather, customs, etc.Occurs within a single year Seasonal Component

*Repeating up and down movementsAffected by business cycle, political, and economic factorsMultiple years durationOften causal or associative relationshipsCyclical Component

*Erratic, unsystematic, residual fluctuationsDue to random variation or unforeseen eventsShort duration and nonrepeating Random Component

*Naive ApproachAssumes 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 point

*Moving averageWeighted moving averageExponential smoothingTechniques for Averaging

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

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

*Graph of Moving Average

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

*[(3 x 16) + (2 x 13) + (12)]/6 = 141/3[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 201/2Weighted Moving Average

*Moving Average And Weighted Moving AverageFigure 4.2

*Increasing n smooths the forecast but makes it less sensitive to changesDo not forecast trends wellRequire extensive historical dataPotential Problems With 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

*Exponential SmoothingNew 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)Remember This!!!!!!!!

*Choosing 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 - Ft

*Common Measures of Error

*Exponential Smoothing ExamplePredicted demand = 142 Ford MustangsActual demand = 153Smoothing constant a = .20

*Exponential Smoothing ExamplePredicted demand = 142 Ford MustangsActual demand = 153Smoothing constant a = .20

*Exponential Smoothing ExamplePredicted demand = 142 Ford MustangsActual demand = 153Smoothing constant a = .20New forecast= 142 + .2(153 142)= 142 + 2.2= 144.2 144 cars

*Exponential Smoothing Example 2Demand for the last four months was:Predict demand for July using each of these methods:(A)1) A 3-period moving average 2) exponential smoothing with alpha equal to .20 (use nave to begin).(B)3) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?

*Exponential Smoothing Example 21.(8+10+8)/3 = 8.33 (July Forecast)2.Use nave to beginB)

MonthDemandForecastMarch 6-April86May106 + 0.2(8 6) = 6.4June86.4 + 0.2(10 6.4) = 7.127.12 + 0.2(8 7.12) = 7.296

MonthMarchAprilMayJuneDemand68108Nave-6810Error-+2+2-2MAD6/3= 2.0

*Exponential Smoothing with Trend AdjustmentWhen a trend is present, exponential smoothing must be modified

*Exponential Smoothing with Trend AdjustmentFt = a(At - 1) + (1 - a)(Ft - 1 + Tt - 1)Tt = b(Ft - Ft - 1) + (1 - b)Tt - 1Step 1: Compute FtStep 2: Compute TtStep 3: Calculate the forecast FITt = Ft + Tt

*Moving Average

Weekly sales of ten-grain bread at the local organic food market are in the table below. Based on this data, forecast week 9 using a five-week moving average.Other Examples(382+410+432+405+421)/5 = 410.0

Week12345678Sales415389420382410432405421

*Exponential Smoothing & MADJim's department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0. Calculate MAD.Other Examples

PeriodDemand1242233264365266307328269251028

*Other Examples Exponential Smoothing

PeriodDemandForecastErrorAbsolute12428.0022326.40-3.403.4032625.040.960.9643625.4210.5810.5852629.65-3.653.6563028.191.811.8173228.923.083.0882630.15-4.154.1592528.49-3.493.49102827.090.910.91Total2.6432.03Average0.293.56BiasMAD

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