The impact of project introduction heuristics on research and development performance

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Availableonlineatwww.sciencedirect.com

http://www.rai-imr.com.br/pt/ RAIRevistadeAdministraçãoeInovação14(2017)151–161

The

impact

of

project

introduction

heuristics

on

research

and

development

performance

Paulo

S. Figueiredo

∗

,

Elisabeth

Loiola

UniversidadeFederaldaBahia(UFBA),Bahia,BA,Brazil

Received15April2016;accepted25January2017 Availableonline18March2017 ScientificEditor:LeonardoAugustoGomes

Abstract

AssumingafixedtotalR&Dbudget,theproductpipelinemanagement(PPM)problemhastwoparts:(1)Whichandhowmanyprojectsshouldbe initiated?(2)Whichprojectsshouldcontinuetobeinvestedinorterminated?Weuseadynamicmodelcalibratedtoapharmaceuticalcompanyto studyPPM,focusingonthreetypesofheuristics—gradualincreaseordecrease,random-normalchoice,andtarget-basedsearch—toevaluatethe impactoftheintroductionofinnovationprojectsinthepipelineontheperformanceinR&D.Wefindthatagradualdecreaseofprojectintroduction ratesresultsinconvergence,butthesizeoftheadjustmentsanddelaysinthepipelinecanlimittheprecisionoftheresults.Arandomchoiceis detrimentaltoperformanceevenwhentheaveragevalueistheoptimal.Atarget-basedsearchresultsinoscillation.Theresultsofouranalysisshow thatthespecificproblemofchoosingtheprojectintroductionratecanbesignificantlyimprovedbyusinganadequateruleofthumborheuristic. ©2017DepartamentodeAdministração,FaculdadedeEconomia,AdministraçãoeContabilidadedaUniversidadedeSãoPaulo–FEA/USP. PublishedbyElsevierEditoraLtda.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).

Keywords: Systemdynamics;Productportfoliomanagement;Productpipelinemanagement;Heuristics;Behaviouraloperationsmanagement;Pharmaceutical industry

Introduction

Itiscleartomostcompaniesthat“today’snewproductswill decidetomorrow’scompanyprofile”as innovationisdiffused (Bhushan,2012,2013).Previousstudiesintheinnovationand productpipelinemanagement(PPM)literaturehaveexamined factorsthatinfluencethe variousdimensions ofR&D perfor-mance,suchasquality,cost,leadtime,andvaluecreated(Clark &Wheelwright,1993;Cooper,Edgett,&Kleinschmidt,1998; Griffin,1997).However,muchisstillunknownabouthow man-agerialdecisionsaffectperformanceinadynamic settingand across the New Product Development (NPD) pipeline (Azar, 2012).AssumingafixedtotalR&Dbudget,thePPMproblem hastwo parts:(1)decidingonwhichprojects tostart,and(2)

∗_{Corresponding}_author.

E-mails:paulosfigueiredo@hotmail.com(P.S.Figueiredo),beloi@ufba.br (E.Loiola).

PeerReviewundertheresponsibilityofDepartamentodeAdministrac¸ão, FaculdadedeEconomia,Administrac¸ãoeContabilidadedaUniversidadede SãoPaulo–FEA/USP.

decidingwhichprojectstocontinueandwhichtoterminateat variousstagesofdevelopment,aswellasanddecidinghowmuch toinvestoneachprojectateachphaseandhowtoallocatepeople acrossthedifferentstagesoftheprocess.Thetotalamountof resourcesavailableforallocationacrossthestagesisdetermined byabudgetingexercise(Chao,Kavadias,&Gaimon,2009).In makingthesedecisions,managersfaceasetoftradeoffsbetween therisks,returns,andtimehorizonsforpayoffs(Gino&Pisano, 2006).AswasnotedbyGinoandPisano(2005b):

“Intheory,suchtradeoffsareoptimizationproblemsthatcan betackledwithatechniquesuchasdynamicprogramming.In reality,thesheercomplexity,ambiguity,anduncertaintyofmost companies’R&Dportfoliosmakethisanessentiallyimpossible optimizationproblemtosolve.”

Theseproblemsare notsolvable, atleastinaclosed form —i.e.,itisanNP-hardproblem(Anderson&Morrice,2006; Anderson, Morrice,& Lundeen, 2005; Browning &Yassine, 2008). A few studieshave focused on behaviour (heuristics) intheschedulingofprojectsataspecificstage(Gino&Pisano, 2005a;Loch&Kavadias,2002;Kavadias&Loch,2004;Varma, Uzsoy,Pekny,&Blau,2007;Yan&Wu,2001).However,these

http://dx.doi.org/10.1016/j.rai.2017.03.004

1809-2039/©2017DepartamentodeAdministração,FaculdadedeEconomia,AdministraçãoeContabilidadedaUniversidadedeSãoPaulo–FEA/USP.Published byElsevierEditoraLtda.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).

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studiesdidnotfocusonprojectintroductionpoliciesacrossthe productdevelopmentpipelineattheportfolio level.The vari-ousmethodsandtoolsmostcommonlyused formanagement trainingareinsufficientfordealingwiththecomplexityof orga-nizationalprocesses such asproductpipeline management.It seems clear that the system dynamics (SD) approach would allowthetreatmentofcomplexityinamorerealisticway(Azar, 2012).

Some empirical studies have explored the patterns, best practicesorbenchmarks inthemanagerial decisions concern-ing PPM (Figueiredo & Loiola, 2012; Schmidt, Sarangee, & Montoya-weiss, 2009). The theoretic models proposed in the literature havenot become a tool that iscommonly used inmanagementpracticeduetotheirhighlycomplexity.Dueto thecomplexityofportfolioselectionandindividuals’bounded rationality(Simon,1956),companiescommonlyutilize heuris-tics for managing their R&D portfolios rather thantrying to optimize them. This decision-making behaviour is very well accepted but researchon the impactof specific heuristics on R&Dperformanceisstilllimited(Gino&Pisano,2005b).

Itisimportanttonotethatlargecompanies,suchas pharma-ceuticalandchemicalcompanies,generatenewpatentsregularly andhave structured productdevelopment processes, as illus-tratedinLoiolaandMascarenhas(2013).

Theexistenceofoptimallevelsintheproductpipelineisnot obviousbecausetheproblemcannotbesolvedinclosedform— i.e.,itisanNP-hardproblemthatrequiresseveresimplifications inordertobesolvable(Anderson&Morrice,2006;Browning& Yassine,2008).Thisisaclearindicatorthatasimulationstudy isparticularlyusefulfortheproblemunderscrutiny.

RudiandDrake(2009)recognizethatbehaviouralaspectsin operationalsettingshavereceivedincreasingattention, includ-ing areas such as the consumer estimation of household inventories(Chandon&Wansink,2004),revenuemanagement (Bearden, Murphy, & Rapoport, 2008), the bullwhip effect (Bloomfield,Gino,&Kulp, 2007;Croson&Donohue,2006; Croson, Katok, Donohue, & Sterman, 2005), and the effect of social preferences (Loch & Wu, 2007) and service-level agreements (Katok, Thomas, & Davis, 2008) for the supply chain coordination. Many papersfocus on portfolio manage-ment(managingonestageofapipeline),butfewerpapersfocus onmanagingthepipelineasawhole.Oneexceptionisthestudy byGinoandPisano(2005b),whichgeneratedresource alloca-tioninsightsforproductportfoliomanagement.Theseinsights adoptedabehaviouralviewpoint intermsoftheheuristicsfor resource allocation at one stage of the pharmaceutical R&D process.

Organizationsoften committomore productdevelopment projectsthantheycanhandle.Theover-commitmentof develop-mentresources(i.e.,whentoomanyprojectsareintroducedinto thepipeline)isacommonphenomenon,asevidencedbycase studies.Theevidencesuggeststhatmanyorganizationshavefar moreproductdevelopmentprojectsinprogressthantheir capac-ityallows(Gino&Pisano,2006).Forinstance,Wheelwright andClark(1992)mentionedthatorganizationstendtopursuea largernumberofprojectsthantheyhavetheresourcestofund andsuggestedthatcompaniesoftenoperatetheirdevelopment

organizations at 200–300% capacity utilization. Ash (2009) findsthatloadingaresourcepoolto300%or400%of capac-itywhileallowingpreemptionmaybegoodfortheengineering talentutilizationrate;however,thisprocedureisdetrimentalfor completingindividualprojectsontheirduedates.Ash,however, didnotfocusontherelationshipbetweencapacityutilizationand thequalityofthedevelopmentactivities.

For most firms that operate with high capacityutilization rates, the simplest form of heuristics would be to gradually decreasetheprojectintroductionrates(alsoreferredtohereas starts)fromthehighlevelstolowerlevels,aimingtobalancethe pipelineandincreasevaluecreation.

Yu,Figueiredo,andDeSouzaNascimento(2010)developed asimple,staticmodeloftheproductdevelopmentpipelinethat establishes the upper limits for the capacity to develop and launchnewproductfamilies.Thisidealnumberofprojectsmay functionas awarningforfirmsthatare tryingtodevelopand launchtoomanyproductfamilies.FigueiredoandLoiola(2012), Figueiredo and Loiola (2014a, 2014b, 2016), and Figueiredo, Travassos, and Loiola (2015) reached similar conclusionswithadynamic modelthat establishedaconcave relationshipbetweenthenumberofprojectsstartedandthetotal valuecreatedinthepipeline.Basedonanotherdynamicmodel, Repenning(2001)showedhowasurgeofresourcedemandcan causehavocintheNPDprocessinthephenomenonknownas firefighting.

Thetraditionalapproachtotheproblemofover-commitment istodevelopbettermodelsforproject managementandmore sophisticated in-process management tools(such as realtime scheduling) and to undertake more planning activities. Gino and Pisano(2006) suggest that thesemodels would be more usefuliftheyrestoncognitivelyandbehaviorally compatible assumptions,i.e.,incorporatingelementsinto themodels that willreducecommoncognitivebiasesthatpeopleincurintheir decisionmakingprocesses.

In this paper, we use a dynamic simulation model adapted and modified from Figueiredo and Loiola (2012), Figueiredo and Loiola (2014a, 2014b, 2016), and Figueiredo et al. (2015) to explore such phenomena in thepharmaceuticalindustry,withaspecificfocusontheimpact of project introduction heuristics on NPD performance. The useofheuristicsisawayofsearchingforabetterpolicyand/or making necessaryadjustments wheneverthere arechangesin theshapeandperformanceofthepipeline.Itimportanttopoint out that the working problem at hand is not actually solved by the use of heuristics. Heuristics are a tool to deal with complex, dynamic problems ina limited, simplified manner, aimingtentatively toachievebetterresults.Aswasexplained previously, heuristics are a very common tool and reflect managerial behaviour. Itisnotargued thatthe toolshouldbe adoptedasthebestone,butitisperhapsthemostpractical.It isarguablethattheworkingproblemissignificantlylargeand the planning horizon isalso large, demanding severe, drastic simplifications in case a solution in closed form is needed (Anderson&Morrice,2006;Browning&Yassine,2008).

Themodelwasrebuiltandadaptedtoreflecttheuseofsuch heuristics.Inparticular, wefocusonthreetypesof heuristics:

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(1)gradualdecreaseorincrease,(2)randomnormalchoiceand (3)target-basedadjustment.Theimpactoftheseheuristicson performanceandtheirefficiencyinimprovingthePPMprocess arediscussed.Wearguethattheover-commitmentofresources distancesmanagersfrommakingbetterdecisions.However,the under-commitmentof resources is alsodetrimental to perfor-mance.Asearchforabetternumberofprojectstobeintroduced intothepipeline,basedonagradualincrease(or decrease)of projectintroduction rates,resultsinconvergence, butthesize of theperiodic adjustmentandthe delaysinthe pipeline can limittheprecisionoftheprocess.Asmallperiodicadjustment ismoreprecise;however,itslowsdownthesearchprocess.A randomnormalchoiceisdetrimentaltoperformancewhenthe averagevalueis optimal.A target-basedadjustmenthaspoor performanceduetothelongdelaysinthepipeline,whichcan causeoscillationinthechain.Thistypeofeffect,referredtoas thebullwhipeffect,wasdetectedinotherchainssuchassupply chains(Goodwin&Franklin,1994;Sterman,1995).

Theresearchquestionofthestudyis,therefore,the follow-ing: isit possible tostudy thefinancial impactof the use of basicandsimpleprojectintroductionheuristicsonthefinancial performanceofaproductdevelopmentpipelinesuchastheone beingstudied?Whichofthese“rulesofthumb”yieldabetter resultforthecompanybeingstudied?

Themodel

Beforepresentingthemodelusedinthisstudy,itisimportant tosingleout thedifferencesbetweenthisstudyandthe other studiesbasedonthemodel.Thestudiesbasedonthemodeluseit inawaythatismarkedlydifferentfromthewayitisusedhere.In FigueiredoandLoiola(2012),theoptimalpoliciesforthemain decisionvariablesaredefined,exceptforthe variable“starts” ornumberofprojectsstarted.InFigueiredoandLoiola(2014a), thewayofscreeningprojectsindynamicallyispresented,asthis paperisaconceptualstudy.InFigueiredoandLoiola(2014b) abehavioural study with business administration students as subjectsisperformedtostudytheeffectof thecomplexityof theproductpipelinemanagementdecisionsontheperformance ofthesystem.InFigueiredoetal.(2015),anewsectorisadded tothemodel tostudy the impactof longerdevelopmentlead timesontheperformanceofthepipeline,takingintoaccountthe shortlifespanofthepatents.InFigueiredoandLoiola(2016), thedynamicbehaviourofthemodelisanalyzedandpoliciesfor mitigatingthebullwhipeffectonthechainareevaluated.

In the model adapted for this study, we steer away from analyzing the conventional phase-gate processes that do not screen out or select products and instead focus on theinnovation pipelines,especially theiruncertainfront ends (Figueiredo & Loiola, 2012; Jugend & Silva, 2012; Khu-rana&Rosenthal,1997;Zapata&Cantú,2008).Themodelhas beenpresentedfivetimesindifferentpublicationsandvirtually alltheequationshavebeenlistedandexplainedinthe aforemen-tionedpapers.FigueiredoandLoiola(2012)listandexplainthe mostimportant equationsandFigueiredo andLoiola(2014b) presentcompletemodeldocumentation.

Thebasicstructureandlogicofthemodelaresimple,andcan befoundinFigueiredoandLoiola(2012).Beforebeingreleased intomarketplace,projectsthatarestartedandintroducedintothe pipelinearedevelopedandscreened,insequence.Valuecreation occurswhileprojectsaredevelopedateachstage,anddepends onhowintensivelytheteamsareworking.Theaveragevalue cre-atedperprojectintermsofnetpresentvalueincreasesthrough the process of screening because only higher value projects will beapprovedfor thenextstage. Basedonthenet present values (NPVs)of the populationof projects that are tracked, managersdecidewhichfractionofprojectswillbeterminated ateachstage(Krishnan&Ulrich,2001;Terwiesch&Ulrich, 2009).

Asidefrom deciding onwhichprojects willbe terminated (i.e.,definingathresholdorminimumNPVforaprojecttobe approved), managers also decideon three variables:capacity adjustment (a given tendency between two extremes: work-ing fast toreduce the backlogs of projects or workingat the rate that maximizesvaluecreation),resourceallocation (peo-ple)across stages,andtheaveragecomplexityoftheprojects (measured bythe proxyman-hoursperproject ateach stage) (Figueiredo&Loiola,2012).Itisassumedthatthetotalamount of resources (people)isfixed. Eachof thesevariablesaffects capacity utilization, i.e. how intensively the teams are work-ing and, therefore, value creation, since there is a concave relationshipbetweenworkintensityandvalueadded(Clark& Wheelwright,1993).

For the purposes of thisstudy, we assume that managers adapt theworkintensity ofthe developmentteamsasneeded and workfaster/slower depending on the numberof projects athand.Aswaspreviouslyexplained,thisadjustmentofwork intensityhasanimpactonthevaluecreationratesateachstage (Figueiredo&Loiola,2012).Inaproductdevelopmentpipeline, the available capacity of the development teams is adjusted locally(ateach stage)toeitheradapt tothe workdemandor keeptheutilizationlevel arounditsnominalvalue.Therefore, the capacityutilizationbias isdefined as the managerial ten-dency toworkbetweenthesetwoextremes. Teamswillwork moreorlessintensivelydependingonthecapacityadjustment choice.Ifmoreimportanceisgiventotheobjectiveofobtaining thefastestratetoreducebacklogsinsteadofworkingatthe nom-inalutilizationlevel,theincreaseinthevalueofprojectsasthey gothroughthegatesshouldbeproportionallysmallerbecause thecapacityutilizationwillbeaboveorbelowitsnominal lev-els(Clark&Wheelwright,1993;Figueiredo&Loiola,2012; Girotra,Terwisch,&Ulrich,2005).Thistrade-offisrepresented inFig.1.

As was explainedby Figueiredo andLoiola (2012): “The resourceallocationbiasreflectsmanagers’ tendenciesto allo-catemorepeopletoworkontheinitial,middle,orfinalstages ofthepipeline.Managersalsohaveabiastowardstheallocation ofcomplexity,i.e.,theycanhaveatendencytoincrease/decrease thecomplexityoftheprojectsatanystageoftheprocess”.The complexityofprojectscanbemeasuredinmanyways depend-ingonwhattypeofproductisbeingdeveloped(linesofcodefor software,numberofpartsforacar,etc.)butinthisstudy,the aver-agesizeoftheprojects(intermsofinvestments)isadoptedas

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1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.00 0.20 0.49 0.76 1.00 1.33 Utilization∗ V alue creation, % 1.90 2.50 3.15 4.10

Fig.1.Relationship betweentheresource utilizationandtheNPVcreation multiplier(adaptedfromClark&Wheelwright,1993).*Thenominalvalueof capacityutilizationissettobeatunity.

ameasureandproxyforcomplexity,meaningthatamore com-plexproject wouldrequirethat moredesignanddevelopment activitiesneedtobeperformed.Thisproxywasalsoadoptedby Yuetal.(2010).

AsdefinedinFigueiredoandLoiola(2012),theperformance variablesinthemodelaretotalvaluecreated(NPV)attheendof thepipelineduringagiventime;valuecreationrates(monetary valueperunitoftime)ateachstageandtherespectiveflowsof projects.TheadoptionofNPVasthesoleperformancecriterion forprojectscreeningisanecessarysimplification;inmost com-panies,however,morethanonefactorcanbeusedinorderto decidewhethertoterminateaproject,anddifferentfactorsmay beuseddependingonthestageofdevelopmentoftheproject. Forexample,abiotechcompanymaybemorefocusedonthe safetyofasubstanceattheearlystagesandonmanufacturability atlaterstages.

Themodelstructureiscomprisedofthreeprocesses:capacity management,valuecreation,andscreeningatanystageofthe pipeline.Fig.2.1showsastylizedrepresentationofthemodel,

inordertodisplaythemainflowofprojectsandthekey deci-sionvariables.SeeinFig.2.2thestockandflowstructureofa typicalstage.Theseprocessesarebrieflydescribedbelow.More details,includingallofthemodel’sequations,canbefoundin FigueiredoandLoiola(2012,2014b).Thebacklogsofprojects arestockswhereprojectsaccumulatebeforebeingscreened,at eachstage.TheaverageNPVatthesestocksisthekeyvariable thatenablesthescreeningofprojects,basedontheGumbelfilter. Fig.2.2demonstratesjustonestageofthemodel.Itis actu-ally comprised of three stages. The upper part of the figure displaystheflowofprojects.Theaforementioneddecision vari-ablesareshowninblackboxes.“Stage1WIP”representsthe stockofprojectsaccumulatingastheyaredeveloped(workin process).Projectsarethenreviewedatthe“Stage1inReview” andare eitherapproved forthe nextstageor eliminated.The screeningprocessisperformedbymeansofaGumbelfilter,i.e. itisassumedthatthepopulationofprojectsfollowsaGumbel probabilitydistributionfunction(Figueiredo&Loiola,2012).If agivenprojecthasanNPVthatislowerthanapredetermined threshold,itisautomaticallyterminated.

ThelowerpartofFig.2.2displaystheflowofNPV,ina con-figurationcalledaco-flow(Sterman,2000).Valueiscreatedat thebeginningofeachstageanddependsoncapacityutilization. Thestockofvalueaccumulatesatthe“ValueinStage1Review” stockandsuchvalueisusedtocalculatetheAverageNPVata givenstage.Suchvariableiskeyinthemodelbecauseitallows thescreening(orselection)ofprojectsateachstage,usingthe aforementionedGumbelfilter.Itisimportanttopointoutthat as projectsareterminated,theirrespectiveNPVsarealso dis-cardedfromthestockofvalue,simultaneously(VTerminate1). TheflowofNPVscontinuestothenextstage,i.e.,tothe“Value inNextStageWIP”stockandalltheprocessstartsonceagain untilprojectsandtheirNPVsgothroughallthreestagesorare terminated. α1, β1, γ1, T1 α2, β2, γ2, T2 α3, β3, γ3, T3 Independent variables (2.1) Stage 2 parameters Stage 3 parameters Stage 1 parameters Starts per month Stage 1 Project backlog in stage 1 Average NPV of stage 1 backlog Project backlog in stage 2 Average NPV of stage 2 backlog Intermediate output shown in grey Project backlog in stage 3 Average NPV of stage 3 backlog Stage 2 Stage 3 Screen 1 _{Screen 2} Screen 3 Output measured by total NPV

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(2.2) Terminated Stage 1 in review Starts Resources βi _Available capacity Capacity utilization Value creation rate 1 Average NPV at start Terminate 1 Value in stage 1 review Average NPV Value approval rate 1 Value in next stage WIP Screening threshold based on gumbel filter Ti Stage 2 WIP Stage 1 WIP Stage 1 to review Average complexity in stage 1 γ i Stage 1 completions Backlog reduction bias αi

Fig.2.2.Stockandflowstructureofatypicalstage.

Modelstructure

Thissection presentsthe threebasicprocesses inthePPM model.

Capacitymanagementprocess

Akey conceptandstructureinthemodelisthe utilization ofcapacity.WheelwrightandClark(1992,p.91)demonstrate thatemployeeproductivity(thefractionoftimespenton value-addingtasks)initiallyincreasesandthendecreasesasthenumber of developmentactivities givensimultaneously toeach engi-neerincreases.Thiseffectiscapturedinafunctionthatlinks utilizationandthevaluecreated.

AswasexplainedbyFigueiredoandLoiola(2012):

“Ateachstage,managershaveafixedamountofresources (employees).Assumingthattheallocationofresourcesisfixed, anincreaseincapacity—measuredbyman-hourspermonth— isonlypossiblebyusingtheexistingresourcesmoreintensively, therebyincreasingtheirutilization.Intheeventofovercapacity, theutilizationequalsthedemandedcapacitybasedonthe back-log.The capacity isadjusted continuously,depending on the valueofthetargetcapacityandonthetimetoadjustcapacity. Thetargetcapacityisdefined asthe demandedrate of devel-opmentat each gate based on the backlog. If the backlog is filledwithprojects,thetargetcapacitywillbehigher,resulting inhigherworkintensityorcapacityutilizationbytheteams.” Valuecreationprocess

Theavailablecapacityisusedwithineachstage,asshownin Fig.2.2,duringtheprocessofvaluecreation.Acertainnumber ofprojectsenterthestage1backlog.Thevalueoftheprojects is trackedby the model, along withtheir number.The NPV valueoftheprojectsismultipliedbyafactor,dependingonthe

capacityutilization,astheprojectsthatwereinthebacklogare developedandproceedtothenextphasetobereviewed.The rate stage toreviewis equal tothe available capacity,unless thereisovercapacity.Theprojectsthenreachgate1orstage1 inreview.Inthisphase,projectsare reviewed,anddepending ontheaverageNPV,someofthemwillbediscardedandtherest willfollowtheflowtothenextstage,i.e.thebacklogofthenext stage.Projectsthatareapprovedinthefinalphasearereleased inthemarketplace.

Projectscreeningprocess

AswasexplainedbyFigueiredoandLoiola(2012):

“TheaverageNPVoftheprojects feedsintothe screening process: the decision to proceed or terminate a fraction of projects is made depending on the average NPV and a pre-determinedthreshold.ThepopulationofNPVsofprojectsafter areviewis assumedtofollowaGumbel distributionbecause projectscreeningisasearchprocessthatselectsNPVextreme values(Dahan&Mendelson,2001;Galambos,1978;Gumbel, 1958).TheGumbeldistributionistheprobabilitydistribution forthemaximummultipledrawsfromexponential-tailed distri-butions.ItappliestoNPDproblemsespeciallywellwhenthere arenospecificlimitsonthepotentialNPVofaproject(Dahan andMendelson,2001).”

Calibration

ThemodelwascalibratedtotheNovartisInnovationPipeline (Reyck, Degraeve, &Crama, 2004). This case study has all of the data needed for the calibration. The Novartis inno-vation funnel has four stages, but the initial stage of basic research was discarded and only the rest of the chain was modelled. The pipeline was calibrated for a “steady state

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condition”,wherevaluecreationisoptimalandthereisabias towards workingmore intensely in orderto reducebacklogs (Figueiredo&Loiola,2012).In thecalibrationprocedure,the followingparameterswerekeptconstant,withtheexactsame valuesasinthedataset:averageprojectcomplexity,starts, ter-minationratesandresourceallocationfractions.Thecalibration hadagoodnessoffitof±5%forallvariables,exceptfornominal developmenttimes(Figueiredo&Loiola,2012).

Projectintroductionheuristics

Wefocus onthreetypesof heuristicsfor theproject intro-duction;theseheuristicsaresimplifiedwaysoftryingtobalance thepipelineandsearchingfortherightlevelofproject introduc-tions.First,we examinehow gradual,monotonicadjustments oftheprojectintroductionratesaffectperformanceandifsuch adjustmentsareeffectiveinreachingtheoptimallevelofstarts. Becausemostdevelopmentteamsoperateatahighlevelofwork intensity or capacityutilization, it isinteresting tocheckif a gradualdecreasefromahighlevelofstartscanbeeffectivein thesearchfortherightpolicy.Theimpactofagradualincrease, fromaninitiallylowlevelofstarts,isalsostudied.

Second,weinvestigatewhetherarandomchoiceforproject introductionratescanbeeffectiveoncetheaveragevalueofthe randomchoices isclose tothe optimalchoice. Itis assumed thatthemanagershaveabenchmarkfortheprojectintroduction rate,butthereisuncertaintyintheprocesssuchthatthenumber ofstartsvariesaroundthebenchmark.Anormallydistributed choiceforstartsisused.

Third,wediscusstheimpactoftarget-basedadjustmentson performance.Thisheuristictakesintoaccountthepresentand previous performances, changing the direction of the adjust-mentswheneverperformancedecreases.Wedescribehowthe PPMheuristicsweremodelledinthenextsection.

Gradualmonotonicdecrease

Thissimpleheuristicsadjuststhenumberofstartsgradually, reducing theirnumber byaconstantvalue (deltaadjustment) everyperiod.Thenumberofstartshasahighinitialvalueof60 projectsperyearandisgraduallyreduced.Theperformanceof thepreviousperiod,intermsofthevaluecreationrateattheend ofthepipeline,iscomparedtothepresentperformance,andthe adjustmentstopswheneverthevaluecreationrateattheendof thepipelineceasestoincrease.

Gradualmonotonicincrease

Thisheuristicsadjuststhenumberofstartsbyincreasingtheir numberbyaconstantvalue(deltaadjustment)everyperiod.The numberofstartshasalowinitialvalue(20projectsperyear)

andisgraduallyincreased.Onceagain,theperformanceofthe previous period,intermsof thevaluecreationrateattheend ofthepipeline,iscomparedtothepresentperformance,andthe adjustmentstopswheneverthevaluecreationrateattheendof thepipelineceasestoincrease.

Randomnormalchoices

Eventhoughmanagersmaymakeanintuitivejudgmentsand educatedguesseswhiledecidingonaprojectintroductionpolicy, uncertaintyplaysanimportantroleinPPM.Onceabenchmark ischosen,itisexpectedthattheuncertaintyinherentinthe inno-vation process will affect their decisions. Although Novartis introduces40newprojectseveryyearonaverage,thisnumber isnotalwayskeptconstant(Reycketal.,2004).Itistherefore interesting todeterminehowdifferentlevels ofuncertaintyin therateofstartsaffectperformance.Forsuchapurpose,a ran-domnormaldistributionwasappliedtothedecisiononproject introduction,anddifferentlevelsofvariationwereaddedtothe optimalvalue.

Target-basedheuristics

The most obvious choicefor adecision rulethat searches for the optimalchoiceof startsis onethattakesinto account the effects of the previous choices,changingthe direction of the adjustment (increase/decrease)if the previousadjustment resultedinpoorerperformance.ThisisrepresentedbyEqs.(1) and(2).Suchanoptimizationeffortwouldbebasedonatarget forperformance(valueapprovalrate),andthesearchwouldstop oncetheperformanceisequalorabovethetargetlevel(Target VALUEOutflow).Thisheuristicsisbasedon(a)aruleto deter-minethedirectionofthenextadjustment,taking intoaccount the effectof a previous decision onthe present performance (Eq. (2)),and (b) an equation to calculate the newvalue for startsusingapre-determinedmagnitudefortheperiodic adjust-ments(deltastarts).Thedirectionofadjustmentiscalculatedby comparingthepresentvaluecreationratewiththepreviousone. Ifthedifferenceispositive,thenthedirectionoftheadjustment shouldbekeptthesame.Otherwise,the directionischanged. Seefurtherdetailsbelow.

Starts=IFThenElse(ValueApprovalRate)≥

TargetVALUEOutflow,previousstarts,previousstarts + (DirectionofAdjustment∗DeltaStarts)

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DirectionofAdjustment=IfThenElse(ValueApprovalRate>

PreviousPerformance,1∗PreviousPerformance,−1∗PreviousPerformance (2)

Analysisofresults

ThemodelusedinthisstudywascalibratedtotheNovartis innovationpipeline(Reycketal.,2004).TheNovartispipeline hasfourstages,butthefirststage(basicresearch)wasexcluded andonlythreestageswereconsidered.Forthepurposesofthe study,allvariablesexcepttheprojectintroductionrate (starts) werekeptconstantatbasecasevalues,whichweretakendirectly

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Starts (3.1) (3.2) 60 45 30 15 0 8 16 24 32 40 48 56 64 72 80

Time (years) Time (years)

P ro jec ts /y ea r Delta=7 Delta=5 Delta=3 Delta=1 Delta=2 0

Value approval rate 40 30 20 10 0 0 8 16 24 32 40 48 56 64 72 80 $/ y ea r

Figs.3.1and3.2.Behaviourwithgradualmonotonicdecrease.

fromthedata.Basecasevaluesareoptimaldecisions,meaning that valuecreation atthe endof the pipelineis maximal and teamsoperateatanominalcapacity(100%),thepointwherethe valueaddedtothetasksismaximal.

Theinitialvaluesfor the stockswere obtained byrunning simulationssothat the steady-state(equilibrium)valueswere determined. Forexample, when studying the random normal heuristics,oneoftheinitialvaluesforthestartsis40.A simu-lationwasrunusingaconstantvalueofstartsat40.Themodel reachesequilibriumafteracertainperiod,andthesevaluesare usedastheinitialvaluesforthestocksinthestudy.

Thecalibratedmodelhasanoptimallevelofstartssetat40 projectsperyear,whichisthebasecasevalueforthevariable. Ifmoreorfewerprojects areinitiated,thetotalvaluecreated decreases.

Gradualmonotonicdecrease

While gradually reducing the number of starts, managers havetodecideon the sizeof periodic adjustment;ifthe size istoolarge,thenumberofstartsmayincreasetoavaluebeyond theoptimalleveloftheconcavecurvebecausetheadjustment islarge andstopswhenever the value creation rate ceases to improve.Thedelaysinthepipelinealsocontribute tothe dis-tancebetweenthefinalchoiceofstartsandtheoptimalvalue. Thishappens becausesometimeis requiredbetweenmaking achange in startsand noticingthe results of such achange. However, there is a trade-off in this decision process; even

thoughasmallperiodicadjustmentismoreprecise,italsotakes longer tofind the right value for the decision variable. This processindicatesthatamixedpolicycouldresultinbetter per-formance;managerscouldinitiatelargeradjustmentsandreduce themastheperformanceapproachesabenchmark.

Figs.3.1and3.2belowshowtheadjustmentprocessandthe impactoftheheuristic onperformancefor differentvaluesof deltaadjustment.

Gradualmonotonicincrease

A gradual monotonic increase of starts exhibits similar behaviourtoagradualdecrease,asshowninFigs.3.3and3.4. Smalleradjustmentsaremorepreciseinattainingavaluenear theoptimallevelof40projectsperyear,buttheyslowdownthe searchfortherightpolicy.

Randomnormalchoices

Todeterminetheeffectofuncertaintyinprojectintroduction policies,wecompareafixed,stableintroductionofprojectswith anormalrandomintroduction.Inbothconditions,theaverage projectintroductionrateissetattheoptimallevel.Thefigures belowshowhowperformanceisaffectedbythedifferentvalues ofthestandarddeviation(σ)ofthenormaldistribution.Asthe standarddeviationoftherandomnormaldistributionincreases, theperformanceleveldecreases.Thisphenomenonisshownin Figs.3.5and3.6. Starts (3.3) (3.4) 200 150 100 50 0 32 24 16 8 0 40 48 56 64 72 80 Time (year) Projects/year Delta=7 Delta=5 Delta=3 Delta=2 Delta=1

Value approval rate 40 30 20 10 0 80 72 64 56 48 40 32 24 16 8 0 Time (year) $/year

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Starts (3.5) (3.6) 80 60 40 20 0 80 72 64 56 48 40 32 24 16 8 0 Time (year) P rojects/year σ=0 σ=10 σ=5

Figs.3.5and3.6.Behaviourwithrandomnormalchoices.

Starts (3.7) (3.8) 100 75 50 25 0 80 72 64 56 48 40 32 24 16 8 0 Time (year) Projects/year Delta= 5 Delta=1

Figs.3.7and3.8.Behaviourwithtarget-basedheuristics.

Target-basedheuristics

Thelongdelaysinthe NPDprocessmakean ideal target-basedsearchquitecomplex; thereisnosimpleruleofthumb to tackle the problem efficiently. The average lead time of a pharmaceutical project in the Novartis innovation chain is approximately 11 years. Because of such large delays, it becomesextremelydifficulttodeterminethecorrectdirectionof adjustments.Thepresentdecisionwillhaveaneffectonthefinal performanceafteralongperiod,andthecombinedeffectof poli-ciesfromdifferentperiodsisunknown.Aheuristicsthattakes intoaccountthechangeinperformanceandthedirectionofthe adjustmentmadeinthepreviousperiodwillhaveverylimited efficiency and will createoscillations in the choice of starts. However, this phenomenon is not unexpected. The presence oftheoscillationcausedbydelayshasbeenwell-documented in other processes that are modelled as ageing chains, such as the Beer Game (Croson & Donohue, 2006; Goodwin & Franklin, 1994; Sterman, 1995; Steckel, Gupta, & Banerji, 2004).

Theresultsofthisheuristicareshownbelow.Thetargetvalue creationrateissetatthemaximumpossiblevalue(28.066US$M permonth),andthesearchprocessissettostopwheneverthe valuecreationisbetweenthe±10%intervalrelativetothetarget. However,thesearchprocessdoesnotconvergeandthedecision onstartsexhibitsoscillationandamplificationofvariance.This phenomenon,calledthebullwhipeffect,isobservedinchains or distribution channels and refersto a trendof increasingly largerswings(oscillationandamplification)intheinventoryor

backlog.Thistrendisinresponsetochangesintheupperpart of the chain as onelooksat stagesfurtherbackinthe chain. TheconceptfirstappearedinJayForrester’sIndustrial Dynam-ics(Forrester,1961)and,thus,itisalsoknownastheForrester effect.Asstatedearlier,thebullwhipeffectisanobserved phe-nomenon in chains,such as supply chains or NPD pipelines (Forrester,1961)(Figs.3.7and3.8).

Conclusionsandimplications

In the pharmaceutical industry, value can be destroyed through longerproductdevelopmenttimes.Thiscanbeeasily demonstrated:itusuallytakesaround12yearstolaunchanew product, amuchlongerperiodthantheonethatwascommon approximately a decade or so ago(Cook,2006; Paich,Peck, & Valant,2004).Given that patentlivesare (normally)fixed at 20years, the impactof increasing timetomarket is clear, i.e.,higherresearchanddevelopmentcostsandlesstimeinthe marketbeforegenericproductsareabletoenterthemarketplace. The problem of time to market is especially complex for pharmaceuticalcompanies,sincethereareregulationsregarding testingandpre-testing,bothnationallyandabroad,andethical, moralandlegalimplicationsconcerningtheuseoftheir prod-ucts.Thesefactorshavetobetakenintoaccountinanyeffort to reducetimetomarket. Thereare limitationstohow much improvement can be made in this dimension, since financial returns might be compromised, inthe long run,if all neces-saryactivitiesandsafetytestsarenotfollowedaccordingly.For

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example,litigationcostsorlossesduetoatarnishedreputation mightarise.

Pharmaceutical and biotech companies have been dealing withthisissuebysteeringawayfrominternalresearchand devel-opment,andinsteadperformingacquisitions,licensingdealsand partnershipswithinnovative,andmanytimesyounger biotech-nologycompanies (Figueiredo et al., 2015). Agreements are beingmadebetweenpharmaceuticalcompaniesthathaveempty innovationfunnelsbutpossessalargerinfrastructuretomarket newdrugs,andnewercompaniesthathavetechnologybutnot enoughinfra-structure(Wilson,2010).Akeyproblemwithsuch policiesisdeterminingtherightamountofprojectstoacquire or createinternally.An overlysmall numberof projects may compromisethefuturesalesrevenueofthecompany,asthere wouldnotbeenoughproductsinthecompany’sportfolio.An excessivelyhighnumberofprojectscanreduceefficiencyand createbottlenecksinthe process,delayingthe releaseof new productsandreducingtheirnetpatentlife.Thisconditionwould alsocompromisefuturerevenues.Themaincontributionofthis studyistoshowoneexampleofhowmanagerialbehaviour— morespecifically,the useof heuristics whiledetermining the correctamountofprojectstointroduceintothepipeline—has animpactonthefinancialperformanceofthepipeline.

Althougha stylizedmodel was usedandour analysiswas focused on a narrow set of management heuristics, it does havesomepotentiallyinterestingimplicationsforthe product pipeline structure andproject introduction strategies and can contribute to the theory and practices in the pharmaceutical industryindifferentways.

The model and the procedures created for studying the financialimpactofprojectintroductionheuristicscouldbean accessory tool to help managers create strategies for project introductionthroughexternalacquisitionand/orin-house devel-opmentandavoidtheproblemof copingwithanexcessively largenumberofprojects.Aswehaveshown,acalibratedmodel canshowsignsthatagivenheuristicyieldsbetterresults.Onthe presentstudy,itwasfoundthatasimplerheuristicofagradual decreaseorincreaseofprojectintroductionratesfromahigh/low workintensityconditionwasbetterthanatarget-based heuris-ticsinwhichthedirectionoftheadjustmentcanbechanged.It wasalsofoundthathavingatargetforvaluecreationadds con-siderablecomplexitytotheproblembecausethedelaysinthe processcreatedifficultiesindecidingontherightdirectionof theadjustments,whichindicatesthat,intheabsenceofamore sophisticatedmodeltodetermineoptimalpolicies,managersof the Novartis pipeline shouldadjust project introduction rates gradually.Itwasalsofoundthatthesizeofperiodicadjustments wasthekeydriverofprecisioninsuchadecisionprocessatthe company;asmalleradjustmentismoreprecisebuttakeslonger toconverge.Alargeradjustmentconvergesfasterbutwithless precision.Furthermore,aconsistent,stableprojectintroduction policiesyieldbetterresultsthanpolicies thatvaryintermsof theoptimalintroductionrate.

Thepharmaceuticalindustryisfacedwithcomplexproblems, whichareNP-hardorNP-complete(Anderson&Morrice,2006; Andersonetal.,2005;Browning&Yassine,2008).Theresults ofouranalysisshowthatthespecificproblemofchoosingthe

projectintroductionratecanbeimprovedbyusinganadequate rule ofthumb or heuristic.Theresults presentedhereare not fullygeneralizable.Therefore,thestylizedmodelpresentedhere shouldbecalibratedandadaptedtospecificconditionsfoundat othercompanies,andresultsmightchange.

Intermsoftheoreticalcontribution,thispaperpresentsa for-mal andoriginal model of a pharmaceuticalproduct pipeline thatiscalibratedtotheconditionsfoundinaspecificcompany. Developingformalmodelsoftheeconomicsofscreeninginthe presenceofcomplexityandresourcetradeoffs,eitherata sin-glestageorinacascadeofstages,andaccountingforbehaviour bias(Gino&Pisano,2005a,b)offersopportunitiesforfollow-up work.Wepresentthismodelasavaluabletoolfortheanalysis ofthedynamicsoftheproductdevelopmentpipelineandhope that thismodel will serveas abasis for futureanalysis.This paperusesthedatapresentedinReycketal.(2004),whichisa simplecasestudy,toserveasadatasourceforthecalibrationof themodel.

Thelimitsofthepresentpapersuggestseverallinesoffuture research.First,intermsoffuturesimulationwork,itwouldbe helpfultoexploreabroaderrangeofR&Dcontextsapartfrom pharmaceuticalpipelines.Second,eventhoughsimplerulescan beeffective infinding abetterpolicyforstarts(undercertain conditions),thePPMproblemisacomplexone.Inareal com-pany,updateswouldhavetobemadeperiodicallytothepolicies or adopted guidelines because of the changesin the pipeline configuration.Itwasassumedthatallotherdecisionvariables andparametersinthemodelwerekeptconstant;however,other parameterscouldchangeovertimeandaddconsiderable com-plexity to the task,as thereare many significant interactions betweenthekeydecisionvariables(Figueiredo&Loiola,2012). Whilesimulationisusefulforexploringthespecificeffects ina controlledmanner,someof the richness of anempirical settingwaslost.Forinstance,itwouldbeinterestingtocapture managerialbehaviourbymeansofanexperimental,game-based study. Weleave thatfor afollow-upstudy.Wehopethat this paperhashighlightedsomefruitfulavenuesforfurtherempirical validationandexploration.

Conﬂictsofinterest

Theauthorsdeclarenoconflictsofinterest. References

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