Improving the Accuracy of Your ForecastsBy Jon Schreibfeder

Effective Inventory Management, Inc.EIM

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This report is the third in a series of white papers designed to help forward-thinking distributors increase efficiency, customer service, and profitability with smart inventory management strategies based on tried and proven methods and best practices.

It’s tough being a distributor in today’s market:

• Increased competition.Competitioncontinuestoincreaseasnewdistributionchannelsevolveandexistingdistributionchannelsexpand.Twentyyearsagomostdistributorsexistedon“marketislands.”Theymayhavehadafewcompetitorsbuttheyknewhowtheseotherfirmsconductedbusiness.AnumberofdevelopmentsincludingtheInternet,dynamicdataprocessingcapabilities,andfaster,morereliabletransportationhavedrasticallychangedthedistributionenvironment.Customershavemoreoptionstochoosefromwhenlookingforsourcesofsupply.

• Lower margins.This“buyersmarket”hasforcedmanydistributorstolowertheirprofitmarginsinordertoremaincompetitive.

•More customer demands.Lowermarginsarenottheonlyresultofthisincreasedcompetition.Customersareinapositiontodemandmorevalue-addedservicesandgreaterproductavailability.

• Economic challenges.Challengingeconomictimeshaveforcedmanydistributorstoreducetheirmaterialpurchases.Theynolongercanaffordtomaintainunneededinventoryor“fat”intheirwarehouses.Everydollarinvestedininventoryhastoworkhardtogenerateprofits.

Theresult:Distributorshavetoprovidethebestpossiblematerialavailabilityaswellasmoreserviceswithasmallerinvestmentinstockinventory.Theyhavetodomorewithless.Inordertoaccomplishthisgoal,estimatesoffutureusageofstockeditemsmustbeasaccurateaspossible.Inthisdocumentwewillexploresomeideaswehavefoundtobeeffectiveindevelopingaccuratedemandforecastsforyourstockproducts.

Traditional Forecasting MethodsOneofthemostcommonmethodsdistributorsutilizetoforecastfuturedemandofproductsistoaveragetheusagerecordedduringthepreviousseveralmonths.Considertheusagehistoryofthisproduct:

ToforecastdemandforJuly,wemightaveragetheusagerecordedduringtheprevioussixmonths:

(100 + 133 + 145 + 90 + 154 + 80) ÷ 6 = 117 pieces

Usage 78 100 133 145 90 154 80 ?

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Asthefollowinggraphshows,aforecastof117piecesseemstobeareasonableestimateofJuly’susage(thegreenlinereflectstheforecastforJuly):

The Effect of Unusual UsageButaveragingpastusagedoesnotalwaysresultinanaccurateforecastoffuturedemand.Ifthedistributorexperiencedunusuallylargesalesofaproduct,averagingtheusageinthepastsixmonthswouldresultinaninaccurateforecast.Forexample,supposethedistributorexperiencedanunusual1,000-piecesaleoftheproductweexaminedbefore(thatis,usageinJuneis1,080piecesinsteadof80pieces):

AveragingtheprevioussixmonthsresultsinaforecastforJulyof284pieces:

(100 + 133 + 145 + 90 + 154 + 1080) ÷ 6 ≈ 284 pieces

Butis284piecesagoodestimateofJuly’sdemand?Probablynot.Tohelpensureforecastaccuracywemustadjustusagehistoryforanyunusualactivitythatwillprobablynotreoccur.Itisagoodideatoexamineallinstanceswherethedemandforecastdifferssignificantlyfromactualusage.Abnormallylargesalesarejustonetypeofunusualactivity.Considerasituationwheretherewasnousageoftheproductinthemonthjustcompleted:

AforecastforJulybasedontheusagerecordedintheprevioussixmonthsequals104pieces:

(100 + 133 + 145 + 90 + 154 + 0) ÷ 6 ≈ 104 pieces

Usage 78 100 133 145 90 154 1080 ?Dec

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Usage 78 100 133 145 90 154 0 ?

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Neither284nor104piecesappearstobeagoodforecastforJuly.Toensuretheaccuracyofdemandforecasts,itiscriticalthatbuyersorsalespeopleexaminepossibleunusualusage.Areportorinquiryshouldlistproductswhoseusageinthemonthjustcompletedisgreaterthan“x”percent,orlessthan“y”percent,oftheforecast.Forexample,somedistributorswillscrutinizeanyitemwhoseusageisgreaterthan300percentorlessthan20percentofthepredicteddemand.Thesepercentagesarenot“castinstone”andshouldbemodifiedtomeeteachdistributor’sspecificsituation.Therearethreereasonswhyanitemwouldbeincludedonthispossibleunusualactivitylist:

1. Activitythatwillnotreoccur.Thisincludesabnormallylargesalesaswellasunusuallylowusagethatwascausedbystockouts,temporarycustomershutdowns,orsomeotherreason.

2. Thestartofanewsalestrend.Thereisadramaticincreaseordecreaseinusagethatisrepresentativeofprobablefutureusageoftheproduct.

3. Thewrongformulaisbeingusedtoforecastfuturedemandoftheitem.

Ifthepossibleunusualusagewascausedbyactivitythatwillnotreoccur,usageshouldbeadjustedtoequalwhatusagewouldhavebeenunder“normal”circumstances.Ifanewsalestrendisdetected,youmightwanttoeitheradjustpastusagetoreflectcurrentmarketconditionsoroverridetheactualforecastuntilanadequatehistorythatreflectsthenewtrendhasbeenaccumulated.

Different Patterns of Usage Require Different Forecast Formulas Itwouldbewonderfulifwecouldforecastfuturedemandofeveryproductbyaveragingtheusage(oradjustedusage)fromtheprevioussixmonths.Butwehavefoundthatdifferentpatternsofusagerequiredifferentforecastformulas.We’vealsodiscoveredthatanaverageofpastusageisjustoneelementofagoodforecastformula.Infact,comprehensiveforecastingconsidersfiveelements:

• Aweightedaverageofpastusage

• Anoptionaltrendfactor

• Possiblecollaborativeinformationfromcustomersand/orsalespeople

• Theeffectofpromotionsorevents

• Identificationofthepropertimeframefortheforecast,alsoknownastheforecasthorizon

Weighted Average of Past UsageLookatthisproduct’susagehistory:

Usage 78 80 90 100 133 145 156 ?

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AforecastforJulycalculatedbyaveragingtheprevioussixmonthsusageisagain117pieces[(80+90+100+133+145+156)÷6≈117].Butnoticehowusagehasincreasedduringthepastseveralmonths:

ItislogicalthatJune’susageof156piecesshouldhavemoreofaneffectonJuly’sdemandthanJanuary’susageof80pieces.Weneedtobeabletoemphasizethehistoryofcertainmonthsinourforecastdemandcalculations.Thiscanbeaccomplishedbyutilizingasetofweightswiththeaverageusagecalculation.ForthisitemwewillplacethegreatestemphasisorweightonJune’susageandgraduallydecreasetheweightoverthepreviousfourmonths:

Month Usage Weight ExtensionJune 156 3.0 468.0May 145 2.5 362.5April 133 2.0 266.0March 100 1.5 150.0February 90 1.0 90.0

Total 10.0 1336.5

Thetotalextensionof1336.5isdividedbythetotalweightof10toequalaweightedaverageofabout134pieces.Aforecastof134piecesappearstobebetterthanthepreviousestimateof117pieces,butitwillprobablystillfallshortofJuly’sactualusage.Butrememberthatpastusageisjustoneelementofacomprehensiveforecast.

TrendsNoaverageofpastusagecanresultinaforecastthatisgreater(orless)thanthelargest(orsmallest)usagequantityincludedinthecalculation.Ifusageiseitherconsistentlyincreasingordecreasingovertime,a“trendfactor”shouldbeappliedtotheresultsoftheweightedaverageformula.Goingbacktoourexample,let’slookathowusagehasincreased,monthtomonth,duringthepastseveralmonths:

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+9.0% 7.6%

100 145 156133

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Thisitemhasexperiencedanaverageincreaseof16.5percent[(33.0+9.0+7.6)÷3]duringthepreviousfourmonths.Ifweincreasetheweightedaverageof134pieces(calculatedabove)by16.5percenttheresultisaforecastof156pieces.Thisisprobablyafairlygoodforecastconsideringthatthepercentageincreaseinusage,monthtomonth,isgraduallygettingsmaller.

Itisimportanttonotethatatrendpercentageshouldnotbeappliedtoeveryforecastcalculation.Afterall,noteveryitemhasaconsistentincreaseordecreaseinusageovertime.Appliedtrendpercentagesalsomayhavetobeadjustedtoreflectchangesininterestrates,generalbusinessactivity,housingstarts,orothereconomicfactors.

Finding the Best Forecast FormulaBecausevariousweightscanbeappliedtoanypreviousmonth’susage,thereareliterallythousandsofsetsofweightsthatcanbeusedtoforecastthefuturedemandofproducts.Sohowdoyoudeterminewhatsetofweightstousewitheachitem?Thoughthismayseemlikeaformidabletask,itisactuallynotthatdifficult.Wetypicallystartbyselectingtheeightmostcommonsetsofweights.InthisexampleweareforecastingdemandforMarch2010:

Feb ‘10 Jan’10 Dec’09 Nov ‘09 Oct ‘09 Sep ‘09 Aug ‘09 Jul ‘09 Jun ‘09 May ‘09 Apr ‘09 Mar ‘09A 3.0 2.5 2.0 1.5 1.0 B 1.0 1.0 1.0 1.0 1.0 1.0 1.0C 1.0 1.0 1.0 D 5.0 2.0 1.0 E 1.0 1.0 1.0F 1.0 2.0G 1.0 1.0 1.0H 1.0 4.0

Acomputerprogramorprocesscalculatestheforecastforeachitem,usingeachformula(withandwithoutatrendfactor),foreachofthepastseveralmonths.Eachforecastiscomparedtotheactualusage(oradjustedusage)forthatmonthwiththeequation:

[(The absolute value of the forecast – Actual usage) ÷ The smaller of the forecast or actual usage]

Forexample,ifanitemforecastis100piecesand50piecesweresoldlastmonth,theresultingforecasterrorwouldbe100percent:

Absolute value of (100 – 50) ÷ 50 = 100%

Wereceivethesameforecasterrorifthenumbersarereversed(aforecastof50piecesandactualsalesof100pieces):

Absolute value of (50 – 100) ÷ 50 = 100%

Thismethodofcalculatingtheforecasterrorrealizesthatbeingoverstockedisjustasbadasbeingunder-stocked.Theforecastformulathathasthelowestaverageforecasterrorduringthepreviousseveralmonthswillbeassignedtothatproducttoforecastdemandinthefuture.Ifnoneoftheseformulasgenerateanacceptablylowforecasterror,othersetsofweightsorforecastingmethodswillbetested.

NotethatformulasF,G,andHusehistoryfortheupcomingmonths,lastyear.Theseformulasareappropriateforseasonalitems.Theseareproductswhoseusagenormallyfluctuatesinanormalpatternthroughouttheyear(forexample,beachumbrellas,snowshovels).Trendpercentagesappliedtoseasonalforecastformulascompareusageinthelastseveralmonthsthisyeartousageinthesamemonthsoflastyear.

Ifyourcomputersystemdoesnotallowyoutoplaceweightsontheusagehistoryinforecastcalculations,tryaveragingtheusageinthelastthree,four,andsixmonthstodeterminewhataverageusagecalculationismostappropriateforeachitem.

The Effect of Promotions and Other EventsApromotionoreventissomethingthatweanticipatewillhaveaneffectonusagebutdoesnotoccuratexactlythesametimeeachyear.Itisimportanttoaccuratelyanalyzetheresultsofpromotionsandevents,correcttheusagehistorytoremovetheeffectofeachpromotionorevent,andapplytheanticipatedeffectofapromotionoreventonafutureforecastwhenthatpromotion/eventoccursagaininthefuture.Weaccomplishthiswithafour-stepprocess:

1. Hypothesize:Definethoseeventsandpromotionsthatyouthinkwillaffectusage.

2. Test:Determineiftheevent/promotiondidaffectusage.

3. Recordresults:Whenthisevent/promotionoccursagainadjusttheforecasttotakeintoaccountthepreviousresults.

4. Cleanusagehistory:Adjusttheeffectoftheeventoutofusagehistory.Afterall,theeventwillnotoccuratexactlythesametimenextyear.

Collaborative InformationSometimesacustomer’sorsalesperson’sestimateoffutureusageprovidesabetterforecastthananaverageofpasthistory.Ifyoucanobtainreliableestimatesofwhatwillbeneeded,thisinformationcanbeincludedasacomponentinyour“total”forecastcalculation:

Results of the forecast formula based on past usage + Effects of a trend percentage+ Anticipated effects of events/promotions+ Collaborative forecast information

Total forecast

Ifacustomerregularlysuppliescollaborativeinformationforyourforecast,makesurethatyoudonotincludeshipmentstothatcustomerinyourusagehistory.Ifthesamecustomerdemandisreflectedinbothusagehistoryandcollaborativeinformation,theresultingforecastwillreflecttwicethecustomer’sactualneeds.

Forecast HorizonForecastingisalotlikegoingtoariflerange.Youhavetobesuretoaimattherighttarget.Thatis,youhavetobesurethatyouareforecastingdemandforthecorrectinventoryperiod.Forexample,supposeaproducthasa90-dayleadtime.IfyouareforecastingdemandattheendofJanuary,yourforecastneedstoreflectyouranticipatedusageinMay:

Afterall,anorderplacedwiththevendorwillnotarriveuntillateAprilorearlyMay.ForecastsforFebruary,March,andAprilshouldhavenoeffectonyourcurrentreplenishmentplans.

Anticipated Lead Time

Place Order withSupplier

Anticipated StockReceipt

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Identify Products Whose Future Demand Cannot Be ForecastWe’vespentalotoftimediscussinghowtoaccuratelyforecastfuturedemandofproducts.Unfortunately,likelotterywinners,futureusageofsomeproductscannotbeaccuratelypredicted.Theseitemstendtohavesporadicorirregularusage.Hereistheusagehistoryofoneoftheseproducts:

TherewasusageofonepieceinJanuaryandonepieceinMay.Anyaverageofpastmonthlyusagewillresultinaforecastoflessthanonepiece.Whatarethechancesacustomerwillaskforone-sixthofaunit?

Hereisthehistoryofanotheritemwithsporadicusage:

Itappearsthatcustomersbuy50piecesoftheproductatatime.Anyaverageofpastmonthlyusagewillresultinaforecastdemandoflessthan50pieces(thatis,150piecesdividedbysixmonthsequals25piecespermonth).Butevenaforecastof50piecespermonthwillnotbeaccurate.Afterall,theitemhassporadicusage.Fiftypiecesarenotsoldeverysinglemonth!

Productswithsporadicusagecanusuallybeidentifiedasthosewhoseaveragequantitysoldorusedinonetransactionisgreaterthantheaveragemonthlyusage.Insteadofaforecastoffuturedemand,theseproductsshouldbemaintainedwithatargetstocklevel.Thetargetstocklevelisamultipleoftheaverageornormalquantityusedinonetransaction.Forexample,youmightwanttomaintainatargetinventoryofonenormalusagequantityoftheproductsold50piecesatatime.Theresultwouldbeanorderuptoquantityof50pieces.Thatis,whenthestockleveldropsbelowtheminimumof50pieces,enoughoftheproductwillbeorderedtobringthestocklevelbackupto50pieces.Iftheitemhasalongleadtimeorrequiresaveryhighlevelofcustomerservice,youmightconsidermaintainingatargetstockleveloftwonormalusequantities.Fortheitemwe’vebeendiscussingwe’dreordertheproductassoonastheavailablequantitydroppedbelow100pieces(thatis,twoordersof50pieces).

Accuratedemandforecastsareacriticalfactorinachievingeffectiveinventorymanagement.Ifyoudonothavegoodestimatesoffutureusage,youareforcedtooverstockinordertomaintainahighlevelofcustomerservice.Thisistheequivalentofadding“fat”toyourwarehouse.Itcostsalotofmoneytomaintainthisexcessinventory,moneythatprobablycouldbeputtobetteruse.Intoday’scompetitiveenvironmentyoumustbe“leanandmean”toprosperandmaximizeyourcompany’sprofitability.Youneedtodevelopthemostaccurateforecastoffuturedemandpossibleforeverystockedproductinyourinventory!

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About the AuthorJon Schreibfeder

JonSchreibfederispresidentofEffectiveInventoryManagement,Inc.,afirmdedicatedtohelpingmanufacturers,distributors,andlargeretailersgetthemostoutoftheirinvestmentinstockinventory.Forover20years,Jonhashelpedovertwothousandfirmsimprovetheirproductivityandprofitabilitythroughbetterinventorymanagement.Jonhasdesignedseveralinventorymanagementcomputersystemsandhasalsoservedasadistributionindustry“troubleshooter”fortwomajorcomputercompanies.Heistheauthorofnumerousarticlesandaseriesofbooksoneffectiveinventorymanagement,includingtherecentlypublishedAchieving Effective Inventory Management (5th edition)andtheNational Association of Wholesale Distributors’ Guess Right – Best Practices in Demand Forecasting for Distributors.

AfeaturedspeakeratseminarsandconventionsthroughoutNorthAmerica,LatinAmerica,Europe,Asia,andthePacificRim,Jonhasbeenawardedthetitle“SubjectMatterExpert”ininventorymanagementbytheAmericanProductivityandQualityCenter.HeisanadvisorandguestlecturerintheIndustrialDistributionProgramatPurdueUniversity.

About Microsoft DynamicsMicrosoftDynamics®isalineofintegrated,adaptablebusinessmanagementsolutionsthatenablesyouandyourpeopletomakebusinessdecisionswithgreaterconfidence.MicrosoftDynamicsworkslikefamiliarMicrosoftsoftware,suchasMicrosoft®Office,whichmeanslessofalearningcurveforyourpeople,sotheycangetupandrunningquicklyandfocusonwhat’smostimportant.BuilttoworkwithMicrosofttechnologies,itworkseasilywiththesystemsyourcompanyalreadyhasimplemented.Byautomatingandstreamliningfinancial,customerrelationship,andsupplychainprocesses,MicrosoftDynamicsbringstogetherpeople,processes,andtechnologies,helpingincreasetheproductivityandeffectivenessofyourbusinessandhelpingyoudrivebusinesssuccess.Visitwww.microsoft.com/dynamicstolearnmore.

Worldwide(1)(701)281-6500UnitedStatesandCanada,tollfree,(888)477-7989www.microsoft.com/dynamics

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