Lecture 17 - Forecasting. When you complete this chapter you should be able to : Understand the three time horizons and which models apply for each use; explain when to use each of the four qualitative models; apply the naive, moving average, exponential smoothing, and trend methods.
Lecture 17 Forecasting Books • Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, P.E., CFPIM, CIRM, Fleming College, Emeritus, Stephen N. Chapman, Ph.D., CFPIM, North Carolina State University, Lloyd M. Clive, P.E., CFPIM, Fleming College • Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005, N.Y.: McGrawHill/Irwin • Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of Business, Rollins College, Prentice Hall Learning Objectives When you complete this chapter you should be able to : Understand the three time horizons and which models apply for each use ỵ Explain when to use each of the four qualitative models ỵ Apply the naive, moving average, exponential smoothing, and trend methods ỵ ForecastingatDisneyWorld ỵ ỵ ỵ GlobalportfolioincludesparksinHongKong, Paris,Tokyo,Orlando,andAnaheim Revenuesarederivedfrompeoplehowmany visitorsandhowtheyspendtheirmoney Dailymanagementreportcontainsonlythe forecastandactualattendanceateachpark ForecastingatDisneyWorld ỵ ỵ ỵ Disneygeneratesdaily,weekly,monthly,annual, and5ưyearforecasts Forecastusedbylabormanagement,maintenance, operations,finance,andparkscheduling Forecastusedtoadjustopeningtimes,rides, shows,staffinglevels,andguestsadmitted ForecastingatDisneyWorld ỵ ỵ ỵ 20%ofcustomerscomefromoutsidetheUSA Economicmodelincludesgrossdomestic product,crossưexchangerates,arrivalsintothe USA Astaffof35analystsand70fieldpeoplesurvey 1millionparkguests,employees,andtravel professionalseachyear ForecastingatDisneyWorld ỵ ỵ ỵ Inputstotheforecastingmodelincludeairline specials,FederalReservepolicies,WallStreet trends,vacation/holidayschedulesfor3,000 schooldistrictsaroundtheworld Averageforecasterrorforthe5ưyearforecastis 5% Averageforecasterrorforannualforecastsis between0%and3% WhatisForecasting? ỵ ỵ Process of predicting a future event Underlying basis of all business decisions ỵ ỵ þ þ Production Inventory Personnel Facilities ?? Forecasting Time Horizons þ Shortrange forecast þ þ þ Mediumrange forecast þ þ þ Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, job assignments, production levels 3monthsto3years Salesandproductionplanning,budgeting Longưrangeforecast ỵ ỵ 3+years Newproductplanning,facilitylocation,researchand development DistinguishingDifferences ỵ þ þ Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes Shortterm forecasting usually employs different methodologies than longerterm forecasting Shortterm forecasts tend to be more accurate than longerterm forecasts InfluenceofProductLifeCycle Introduction Growth Maturity Decline ỵ ỵ Introductionandgrowthrequirelongerforecasts thanmaturityanddecline Asproductpassesthroughlifecycle,forecasts areusefulinprojecting ỵ ỵ ỵ Staffinglevels Inventorylevels Factorycapacity WeightedMovingAverage ỵ ỵ Usedwhentrendispresent ỵ Olderdatausuallylessimportant Weightsbasedonexperienceandintuition Weighted moving average = ∑ (weight for period n) x (demand in period n) ∑ weights Weights Applied Period Last month Weighted Moving Average Two months ago Three months ago Sum of weights Month January February March April May 121/6 June 141/3 July 201/2 Actual Shed Sales 10 12 13 16 19 23 26 3-Month Weighted Moving Average [(3 x 13) + (2 x 12) + (10)]/6 = [(3 x 16) + (2 x 13) + (12)]/6 = [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = Potential Problems With Moving Average þ þ þ Increasing n smooths the forecast but makes it less sensitive to changes Do not forecast trends well Require extensive historical data Moving Average And Weighted Moving Average Weighted moving average Sales demand 30 – 25 – 20 Actual – sales 15 – Moving average 10 – – | J | F | M | A | M | J | J | A | S | O | N | D ExponentialSmoothing ỵ ỵ ỵ Formofweightedmovingaverage ỵ Weightsdeclineexponentially ỵ Mostrecentdataweightedmost Requiressmoothingconstant( ) ỵ Rangesfrom0to1 ỵ Subjectivelychosen Involveslittlerecordkeepingofpastdata Exponential Smoothing Last period’s forecast + α (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + α(At – 1 Ft – 1) where Ft = new forecast Ft – = previous forecast α = smoothing (or weighting) constant (0 ≤ α ≤ 1) Exponential Smoothing Exponential smoothing averages the current smoothed estimate with the most recent data point, thus giving least weight to the oldest data. Choosing a “good” value for is critical New forecast = ( )(latest demand) + (1 )(previous forecast) Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 New forecast = 142 + 2(153 – 142) Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 New forecast = 142 + 2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars Effect of Smoothing Constants Weight Assigned to Smoothing Constant Most Recent Period (α) 2nd Most 3rd Most Recent Recent Period Period α(1 - α) α(1 - α)2 4th Most Recent Period α(1 - α)3 5th Most Recent Period α(1 - α)4 α = 1 09 081 073 066 α = 5 25 125 063 031 Impact of Different 225 – Actual demand Demand 200 – α = 175 – 150 – α = | | | | | Quarter | | | | Impact of Different 225 – Actual demand values of 200 – Chose high when underlying average 175 is likely to change ỵ Choose 150 low values of when underlying average is stable Demand ỵ = | | | | | Quarter α = | | | | Choosing The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error = Actual demand - Forecast value = At - Ft End of Lecture 17 ... Global portfolio includes parks in Hong Kong, Paris, Tokyo, Orlando,? ?and? ?Anaheim Revenues are derived from people – how many visitors? ?and? ?how they spend their money Daily? ?management? ?report contains only the forecast? ?and? ?actual attendance at each park... Impactsdevelopmentofnewproducts Demandforecasts ỵ Predict sales? ?of? ?existing products? ?and? ?services Strategic Importance? ?of? ?Forecasting Human Resources – Hiring, training, laying off workers þ Capacity –... delivery, loss of customers, loss of market share ỵ Supply Chain Management Good supplier relations and price advantages ỵ SevenStepsinForecasting þ þ þ þ þ þ þ Determine the use? ?of? ?the forecast