379
AdvancesinProductionEngineering&Management ISSN1854β6250
Volume14|Number3|September2019|pp379β390 Journalhome:apemβjournal.org
https://doi.org/10.14743/apem2019.3.335 Originalscientificpaper
Optimal timing of price change with strategic customers under demand uncertainty: A real option approach
Lee, Y.a, Lee, J.P.b, Kim, S.b,* aDepartment of Mechanical Engineering, University of Texas at San Antonio, San Antonio, United States of America (USA) bCollege of Business Administration, Hongik University, Seoul, South Korea
A B S T R A C T A R T I C L E I N F O
Thispaperproposesamodeltodeterminetheoptimalmarkdowntimingforacompanywithstrategiccustomerpurchasingbehaviour. Since strategic cusβtomers are aware of potentialmarkdownunder the posted pricing scheme,theymaychoosetowaitlongertomaximisetheirutilisationinsteadofbuyingaproductand fulfillingan instant surplus.On theotherhand, thesellercandelaythemarkdowndecisionuntilitisprovedtobeprofitableandhencehasanoptiontodeterminethetiming. Inestimatingthevalueof themarkdowndecision,thesellerβsoptionneedstobeestimated.However,thevalueoftheoptionishardtobecapturedbytheconventionalnetpresentvalueanalysis.Under market uncertainty where potential customer demand evolves overtime,thesellerβsrevenuefunctionisintheformofastochasticdynamicproβgrammingmodel.Applyingarealoptionapproach,weinvestigatetheoptimalprice path and propose the optimal markdown threshold. Given the markβdowncostsincurred,wefindthattheoptimaldiscounttimingforthefirmisdeterminedbyathresholdpolicy.Furthermore,ourresultsshowthatiffuturemarketbecomesmoreuncertain,thesellerneedstowaitlongerordelaythemarkdown decision. In addition, the optimal threshold of the markdowndecreasesexponentiallyinadecliningmarket,whichexplainstheearlymarkβdownpolicyofsomeconsumerproductcompanies.
Β©2019CPE,UniversityofMaribor.Allrightsreserved.
Keywords:Strategiccustomers;Pricechange;Postedpricing;Markdown;Demanduncertainty;Realoption
*Correspondingauthor:[email protected](Kim,S.)
Articlehistory:Received28February2019Revised10September2019Accepted12September2019
1. Introduction
Demandmanagementbecomesthebasisforthedecisionmakingofthefirms;fromproductionplanningtoinventorymanagement[1,20].Pricingpoliciesarefrequentlyusedtoolswhenfirmsmanagetheirdemand[20].Thepricingpoliciesofafirmareoftencomplexanddiversedependβingonthebusinessenvironmentinwhichthecompanylies[1,5,6,14,20].Inthefashionindusβtry, for instance,simplemarkdownpricing iswidelyusedtoselloutremainingstockaftertheregularsalesseason[21].Somecustomersmaychoosetowaitandpurchasetheproductlateratthemarkdownpriceratherthanbuyingitrightaway.Ontheotherhand,airlinecompaniesconβtinuouslymarkupthepricesofticketsupondeparture.Whenlookingforanairlineticket,cusβtomerscanexpectan increase inprices if theydelaytheirpurchase.Therefore,understandingcustomerpurchasingbehaviouriscriticalforthefirmtomakepricingdecisions.
Strategic customers in theoperationsmanagement literature aredefined as thosewhoareawareofthefirm'sdynamicpricingpoliciesandmakeinterβtemporalpurchasingdecisions[20].Sincesuchcustomersareconsciousofpotentialchangesinpricesatalaterpointintime,theyarebeingstrategicratherthanmyopic.Insteadofbuyingaproductandfulfillinganinstantsurβ
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plus, they strategicallywait for a future pricemarkdown and thereby seek tomaximise theirutilisation[5,6,18].Assuch,strategiccustomershavebecomeasubstituteformyopiccustomβerswhosimplymakeabuyingdecisionifthepriceislowerthantheirvaluation[5,6,20].Thereβfore, firmsmustcomprehendthestrategicbehaviourofcustomersandfindanoptimalpricingschemebasedonittomaximiserevenue.
Inresponsetothestrategicbehaviourofcustomers, the firm'sdecisionsaregenerallytwoβfold:thetimingofpricechangesandtheavailabilityoftheproduct[3,20].Thefirmsellsaprodβuctforadurationoftime,afterwhichitmaydecidetochangethepriceatacertainpointintime.Limited supply could also be used as a marketing strategy to increase the sense of urgencyamongcustomers.Therefore,customersinthemarketchooseeithertopurchaseaproductatitscurrentpriceor to revisit it after theprice goesdown, consideringnotonly the timingof themarkdownbutalsothepossibilityofsellouts.
Inthispaper,weinvestigatethemarkdowndecisionofamonopolistwhowishestomaximiseexpectedrevenuesinthepresenceofstrategiccustomers.Ourmodelcapturesseveralimportantpropertiesofthemarketenvironmentforconsumerproducts.First,thesellercommitstoafixedpath of two prices: itmay sell a product for a duration of time, afterwhich itmay decide tochangetheprice.Themarkdowndecisioncanbemadenomorethanonceoverthesaleshorizonand is hence irreversible. Second, customers show strategic purchasing behaviour towardsfirms:evenifthevaluationoftheproductexceedsthepriceoftheproductduringthefirstpartofthesaleshorizon,customersmaynotsimplypurchaseit.Instead,theirdecisiontopurchaseisbasedonthevaluationthatexceedsacertainlevel,thusfollowingathresholdpolicy.Third,poβtentialcustomerdemandisstochastic.Inparticular,themarketsizefollowsageometricBrownβianmotionthatevolvesdynamicallyovertime.
Weconsideraseller'sproblemondecidingtheoptimalpricepathandthetimingofamarkβdown under demand uncertainty. Specifically,we present a stochastic dynamic programmingmodelwherethesellerhasasingleopportunity todiscountthepriceof theproductatasunkcost.Customersinthemarketareawareofapotentialmarkdownandthelikelihoodofasellout.Basedoncustomers'valuationinregardtothetwoprices,thevalueofthefirmisexpressedasastreamofexpectedrevenues.Solvingtheproblemusingarealoptionapproach,weshowthattheseller'soptimalmarkdowntimingdecision isbasedonthethresholdpolicy.Tothebestofourknowledge,thisisoneofthefirststudiesthatconsidersapostedpricingschemeundermarβketuncertainty.
Theremainderofthepaperisorganisedasfollows.Section2outlinespreviousrelatedworkstosummariseextantresearch.Section3proposesarevenuemaximisationmodel,andSection4continueswiththetopicbyanalysingthesolutionofthemodel.Finally,wediscussbroaderfindβings,conclusions,andpotentialfutureresearchopportunitiesinSection5.
2. Literature review
Strategiccustomersandfirms'pricingpolicyproblemshavebecomeanincreasinglyproductiveresearch area.Amongothers, studies regarding customerpurchasingbehavior are thoroughlyreviewedbyShenandSu[20].Mostofthepapersintheliteratureconsidertwoimportanteleβments inmodellingstrategiccustomerbehaviour.Thefirst isthearrivalprocessofcustomers.Whethercustomerspreexistedinthemarket[7,13,18]orsequentiallyarrivedinthemarket[3,14,21,23]isaquestionbasedonthispremise.Thesecondisthedecisionmakingofthecustomβersandhowthedecisionmakingultimatelyformsanequilibrium.Regardlessofthemarketsize,thedecisionmakingofanindividualcustomermakesanimpactonthedynamicsofthemarketto some extent. For instance, when many customers purchase goods in the early stages, theproductmayrunoutofstockforsomeofthosewhoinitiallydecidedtodelaythepurchase[13].Sometimescustomersmayhavetopurchasethegoodsatanevenhigherpriceifthefirmorsellβerchoosestoadoptamarkuppricingpolicy[24].Astrategiccustomertriestomakeanoptimaldecision,foreseeingthesesituations,andthisprocessmay, inturn,compriseanequilibriumindecisionmaking.Theseller,ontheotherhand,setshisorherpricingpolicybasedonthisequiβlibriuminanefforttomaximiseprofit.Therefore,thisgameβtheoreticrelationshipwithconflictβ
Optimal timing of price change with strategic customers under demand uncertainty: A real option approach
Advances in Production Engineering & Management 14(3) 2019 381
ing interestsbetweenthesellerandthestrategicbuyersnecessarily leadstoahighlycomplexmodelinmanystudies.
Thereare twotypesofsimplifications todealwith thecomplexity in themodelling.Firstly,thetimeofthepricechangeisoftenfixed.Aspecificnumberofperiodsarepresumed,andstaticpricingismaintainedforthedurationoftheperiods.Inotherwords,theanalysisofoptimalpricβingisbasedonthedefinitenumberofperiodsinwhichafixedpriceisoffered,ratherthanfindβingthepricechangingperiodonebyone[5β7,13].Thesecondcaseisthesizeofthemarket:inapplyingagametheoryapproach,asmallmarketsizeisassumed.Inthissituation,acustomerpredicts the decisionmaking of other consumers tomake his or her optimal decision and anequilibriumofstrategicpurchases isachieved.AvivandPazgal[3] foundthata firm'sbenefitsfrompricedifferentiationmaydecreaseascustomersbecomemorestrategic,andhenceoptimalpricingpoliciesmayresultinpotentialrevenuelossesinthepresenceofstrategiccustomers.
Customers'purchasingdecisionsdependontheinteractionamongthepricingpolicy,availaβbility,customervaluations,remainingtime,andsoforth.Underthepostedpricing,forinstance,wherethesellerannouncesitspricepathinadvance,theavailabilityoftheproductorthepossiβbilitytopurchaseitlaterintimewillbethemajorconcernforthecustomer[2,7,13].AsDasuandTong[7]specificallypointedout,theseller'sdynamicpricingdecisionismeaningfulonlyifcustomersareawareofthestockβoutpossibility,whiletheimpactoftheperceptiononstrategiccustomerbehaviourisdifferent inheterogeneouscustomervaluations[23]. Inmanystudies,atwoβperiodpostedpricingschemehasbeenusedduetoitssimplicityandapplicability,althoughthesellercanstillmakeapricechangeatanytime[4,7,13,15].DasuandTong[7],inparticular,foundthat theapproximationclose to themaximumrevenuecanbeachievedbytwoor threepricingchanges.Inthisstudy,ourmodelwillalsobebasedonthetwoβperiodpostedpricingincontinuoustimeperiodstofindtheoptimaltimingofpricechange,whiletheavailabilityoftheitemislimitedafterthemarkdown.
Asforthefirm'spointofview,ontheotherhand,marketsizeisthemainsourceofuncertainβty.Giventhepriceandthetimingofthepricechange,thefirm'srevenuemustbesignificantlydifferentdependingonchangesindemandatthemoment.Undermarketuncertainty,thesellercaneithermakean immediateprice changeor intentionallydelay thedecision toobserve theactualdemandmovement.Thissituationisverycommoninmanyoperationalpractices:compaβnieshaveanopportunitytoinvestbuttheycanstillwaitfornewinformation.Inotherwords,afirmwith theability topostponeadecisionhas theoption,not theobligation, toexercise it βmaking it analogous to holding a financial call option. Since first proposed by Pindyck [19],McDonaldandSiegel[17],DixitandPindyck[8]andothers,thisrealoptionapproachhasbeenwidelyborrowedintheareasofmarketingandoperationsmanagementbecauseithelpsustobetterunderstandthetruevalueoftheinvestmentopportunity.
Adoptingtherealoptionconceptisnotcompletelynewinrevenuemanagementliterature.Innumerouspapers,thedynamicpricingdecisionisdeterminedbyconsideringtheoptionvalueofunsoldproducts[11,16].Sincethisoptionvaluedecreasestowardstheendofthetimehorizon,theoptimalpricepathalsodecreasesovertime.Inanotherpaper,GallegoandSahin[10]usedthe real option approach tomodel uncertain customer valuations. In this paper, however,weassumethatpotentialcustomerdemandevolvesovertimeandfollowsthegeometricBrownianmotion(GBM).Assumingtheknowndistributiononcustomervaluationsandthelevelofavailaβbility,weexploretheoptimalmarkdowntimingproblembasedonthenetpresentvalueoftheseller'sexpectedrevenue.Tothebestofourknowledge,intheliteratureonstrategiccustomers,there areonly ahandful of studies thatdealwith theoptimal timingof price change, andyetfewerstillthatatthesametimeaddressoptimalpricingwithstrategiccustomersundermarketuncertainty.
3. Model description
Inthispaper,weconsideramonopolisticfirmthatsellsasingleitemtopotentialcustomersovertwoperiods.Thefirmwantstomaximiseitsnetpresentvalueofexpectedrevenue.Below,weexplainfurtherassumptionsbeforebuildingourmodel.
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Assumption1.Themonopolistic firm followsatwoβperiodmarkdownpricingschemeandcomβmitstothepricepathinbothphases.
Assumption2.Theoriginal( )andmarkdownprices( )arepreβannouncedandthe inβstockprobability(Ο)inthesecondperiodisalsogiveninformation.
Assumption 3. Customersare strategic rather thanmyopicandareawareofmarkdownsandpossibilitiesofstockβouts.
Assumption4.Thedistributionofcustomervaluations( β )isknown.
3.1 Valuation of a strategic customer
Supposethatthereisamonopolistwhohasasufficientlylargenumberofanitem.Untiltime ,theitemisinitiallysoldatprice ,andafter theitemissoldatthemarkdownprice .Thetwoprices, and ,arepreβannounced.CustomerdemandfollowsageometricBrownianmotion(GBM),andeachcustomerissupposedtopurchaseonlyoneunitoftheitem.Whentheselleroffersamarkdownprice,weassumethatthesellercancontrolthelevelofproductavailaβbility, ,toinducescarcity.Inotherwords,inthesecondperiod,theinβstockprobabilitydecidedbythesellerwillbesetto 1.Controllingtheavailabilityofservicesoritemsofdifferentclasβsesisprevalentinrevenuemanagement[24]andinducingalevelofscarcityisalsooneofthemostcommonstrategiesinmarketing[9,22].
Let denotethecustomer'ssurplus.Thentheutilisationofthecustomerwhopurchasestheitemrightnowisasfollows:
(1)
where isthesurplusofthecustomerand isthecurrentpriceoftheproduct.Similarly,theutilisationofthecustomerwhodecidestowaitforthediscountisasfollows:
(2)
where is the customer'svaluationof theproduct, is the currentpriceof theproduct, isthe future price of the product, and is the service level of the product at the lowerprice .Thus, the stockβout probability is1 . stands for the customer's preference for risk; 0indicates riskβaverse, 0riskβtaking, and 0riskβneutral attitude. Furthermore, we asβsumethatthecustomersareeitherriskβaverseorriskβneutral,whichisaprevalentassumptionmadebymanyresearchers[13,15].
Inthissetting,thestrategiccustomersdecidetopurchaseinthefirstperiodiftheirvaluationisgreaterthanorequaltothethresholdvalue.Thepurchasingdecisionofthestrategiccustomerisdeterminedbythefollowinglemma.
Lemma1.Thethresholdofastrategiccustomer'svaluationisgivenby:
1 (3)
Proof.Thetwochoices,purchasingrightnoworwaitingforadiscount,generatethesamesurβpluswhen .Solvingtheequation,astrategiccustomerwillhavethefollowingthreshold.Thatis,
. (4)Thiswouldfinishtheproof.
If 1,nocustomerswouldbuyinthefirstperiod.Furthermore,weassumethatcustomersdonotpurchaseiftheutilisationislessthanzerowithoutlossofgenerality.Thatmeans, isnotlessthan .Therefore,itissufficienttoconsideronlythecasewherethethresholdisbetweenthefirstperiod'spricingandone.Thatis,
1 (5)
Thisalsodecidestheupperandthelowerboundsof accordingly.Followingtheliterature,potentialcustomerdemandisassumedtobeamultiplicationofthe
customervaluefunctionandthetimeβvaryingpotentialdemand.Thatis,thedemandfunctionisgivenby
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1 (6)
where is a knowndistribution function of the product at customer valuation , and isthemultiplicativedemandshockprocess.Thismaybethoughtasdemandinwhichtheproducthasaunitprice.
In the firstperiod,a strategiccustomerwouldpurchase theproduct, if and 0.Therefore,fromtheconditions,
and (7)
wehave
. (8)
Since forthecustomerspurchasinginthefirstperiod,thecurrentdemandfunctionis
1 11
. (9)
Ontheotherhand,aproportionofcustomerswouldwaitandpurchaselateratalowerprice,if and 0.Thevaluationofsuchcustomersisasfollows:
. (10)
Hence,thedemandfunctionofthecustomerswhocomebacklaterinthesecondperiodtopurβchasewillbe:
1. (11)
Finally,whentheproductstartsbeingsoldatamarkdownprice ,anycustomerwhosevalβuationisatleastgreaterthanthepricewouldpurchaseit.Namely,thedemandfunctionwillbe:
1 . (12)
Without loss of generality, let the valuation of customers, , be uniformly distributed over[0,1].Thenthedemandforeachcaseisgivenasfollows:
1 11
1 (13)
1 1 1 (14)
1 . (15)
3.2 Customer demand
Inthispaper,weuseageometricBrownianmotion(GBM)toformulatethemultiβplicativedeβmandshock attime .Thatmeanstherelativechangeindemand, / , withinashorttimeinterval, , ,canvarywithtime .Thedynamicsofdemandarerepresentedbythefollowβingformula:
, (16)
where isthegrowthrateordriftrateindemand, isthevolatilityoftheprocess,and isastandardWienerprocess. If 0,market size is increasingover time. If 0,market size isdecreasing.
Thiscontinuousrandomvariable issaidtohavelognormaldistributionbecausetheinteβgralofEq.16givesthefollowingdemandfunction(seeAppendixAforthederivation):
β , (17)
where istheinitialdemand.WhilethebellβshapedpatternofdemandisexpectedbyEq.17,therealisationofdemandwillsubstantiallydeviatefromit,dependingonthemarketvolatility,asshowninFig.1.
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Fig.1 SamplepathofpotentialdemandfromthegeometricBrownianmotioninadecreasingmarket.
Note that the threesamplepaths inFig.1aredrawn fromEq.16withameandrift rateof
0.1and three standard deviations of 0.05, 0.1, and 0.2. As shown in the figure, thesamplepathwithalargerstandarddeviationtendstofluctuatesignificantly,whileallthreetraβjectorieshaveadecreasingtrendincommonduetothenegativemeandriftrate.
3.3 Optimal timing of price discount
Next,weconsidertheoptimaltimingproblemconditionedonthecustomer'spurchasingstratβegy.Wedevelopamodelforanoptimaldiscounttimingdecisionusingarealoptionmodel.Inpractice,thecompanyhasan"option"todelaythediscountandhenceneedstodeterminewhenthepriceshouldbediscounted.Aftermarkdown,thecompanywouldmakerevenue ,withanirreversiblesunkcost beingincurredfromsalespromotion,inventorymanagement,andsoforth.
Hereinweformulatethevaluefunctionofthefirmwithanopportunityofthediscounttiming.Whentheproductissoldattheoriginalprice ,aproportionofcustomers, ,whosevaluaβtionisfarhigherthan ,orgreaterthan ,willdecidetopurchasetheitem.Agroupofstrategiccustomers, , whose valuation is between and would like to wait and see if the price ismarkeddown.Once the firmdecides todiscount theoriginalprice to themarkdownprice, ,theycomebacktopurchasetheproductbutonly ofthemwillbeabletogetone.Weassumethat suchdemand is instantaneous,meaning that customerdemandaccumulatedup to time willberealisedattime [3].Fromtime ,anycustomerswhosevaluationisatleasthigherthanwouldliketopurchasetheproductbut,again,only ofthemwouldgetone.Webeginwith thevalue functionof the firm for theoptimaldiscount timingproblem.The
valueofthefirm, ,isthestreamofrevenue,whichconsistsofthreecasesstatedearlier.Weusedynamicprogramming,stipulatinganexogenousdiscountrate .Then istheexpectedpresentvalue
max 1 (18)
where
1 . (19)
Sincethedemandofstrategiccustomerswillberealisedattime ,werearrangetheformulasothattherevenueisincludedintheterminalpayoff .Then
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max 1 (20)
where
1 (21)
1 (22)
1 (23)
Since 1 / (See Appendix A) and β , we
finallyhave:
1 1 (24)
Substituting inEq.3intotheformula,thevaluefunctionofthefirmissummarisedasfollows.
Proposition1.Thevaluefunctionofthefirmforoptimaldiscounttiming isgivenby:
max1
1 (25)
where
11 1 (26)
Bysolvingthisstochasticdynamicprogrammingproblem,wecanobtaintheoptimaltimingforamarkdown.Theoptionβlikeapproachshownin[17]and[19]isusedtosolvethedynamicstochasticproblem.Asthepotentialdemand evolvesstochastically,theoptimalstrategyistoexercise(markdown)sothatthevalueisatleastgreaterthanthecriticalvalue β.Afirmβsopβtimalmarkdowntimingsolutionisrepresentedinthefollowingproposition.Proposition2.Acompanyconsideringmarkdownoftheretailpricewillhaveavaluefunctionasfollows:
1 β
1 1 β (27)
whereβ
1β
1 11 (28)
1 1β
β
(29)
and12
12
2 (30)
Proof.Let denotethetimingatwhichthefirmdiscountstheoriginalpriceoftheitem.Asdeβscribedearlier,thefirmmakesrevenueflowsof 1 beforethemarkdown.Attime ,thefirmwouldmakerevenueflow withtheirreversiblesunkcost .Therefore,asshownin[8],theBellmanequationinthecontinuationregion,wherevaluesof arenotoptimaltomarkβdown,isgivenby:
1 , (31)
whichimpliesthatoveratimeinterval ,thetotalexpectedreturnonthemarkdownopportuβnityisequaltoitsexpectedrateofcapitalappreciation.
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ApplyingIto'slemma,wehave
12
, (32)
where β and β .SubstitutingEq.16anddividingthroughby ,wehavethefollowingBellmanequation(see
AppendixBforproof):
12
1 0(33)
Toensuretheexistenceoftheoptimalsolution,weassumethat .Thedifferentialequaβtion mustsatisfythefollowingthreeboundaryconditions:
0 0 (34)
β 1 1β
(35)
β 1 (36)
Eq.34holdsbasedon theobservation that itwill stayzero if thestochasticprocess goes tozero.Theothertwoequationsaretoimposecontinuityandsmoothnessatthecriticalpoint β,thepotentialdemandatwhichitisoptimaltodiscount.Eq.35isthevalueβmatchingcondition,indicatingtherevenuethefirmmakesuponmarkdown.Eq.36isthesmoothβpastingconditionatthepoint.
Therefore,thesolutionofthedifferentialEq.33musttaketheform
1, (37)
where isaconstanttobedeterminedand isoneofthesolutionsofthefollowingquadratureequation:
12
1 0. (38)
Solvingtheequationandtake
12
12
21 (39)
toensuretheboundarycondition.Fromthesmoothpastingandthevalueβmatchingconditions,wehave
β β 1 β 1 1β
(40)
and
β β 1 1. (41)
Solvingtheseequationsresultsin:
β1
β 1 1
1 (42)
1 1β
β
(43)
and12
12
2. (44)
Herein, only if 1 / and 1 1 ,we have a positive
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Advances in Production Engineering & Management 14(3) 2019 387
threshold β 0.ThisresultleadstoProposition3.Again,thethreshold βdeterminestheoptimalmarkdowntimingforafirm.Whentheactual
customerdemandofthefirmattime islowerthanthethreshold β,itisbeneficialtoselltheproductattheoriginalretailprice ,makingtherevenuestreamof 1 / aswellasgiving the flexibility that the firmcanhold for thepricemarkdown,measuredbyΞ±X .On theotherhand,whentheactualdemandisgreaterthanthethresholdXβ,thefirmwilldecidetodisβ
countthepriceandtakethebenefitofmarkdown 1 byspendβ
inginvestmentcost .
4. Analysis and discussion
Thissectionexplainssomeof the importantcharacteristics foroptimalmarkdownapproachessuggestedearlier.First,thefollowingpropositionillustratesthatthereexistsapositivethreshβoldforthefirmatanygiventime underspecificconditionsfor and .
Proposition3. Let βdenotetheoptimaltimingofmarkdowntomaximisethe firmvalue.Thentheoptimalmarkdowntimeisfinite β β[12],andthefirstepochthatdemandexceedsthethreβsholdisestimatedatthefollowingtime:
β inf 0 | β , (45)
where there exists a positive threshold β β 1
attime if 1 / and 1 1 / .Proof.ByProposition2.
Notethatweassumeadecreasingmarketsize( 0).Astime increases,therefore,wecanobserve that the threshold βdecreases exponentially,while theminimumvalue for the fixedcost thatisrequiredforthisapproachtobefeasibleincreasesexponentiallybeforehittingthelowerbound asshowninthefollowingproposition.
Proposition 4. As β β, we have a threshold β β 0and the lower bound of the fixed costβ / .Again,as the threshold βfor themarkdowndecreasesexponentially,a firmis likely todeβ
cideonapricediscountintherelativelyearlystages.Italsoindicatesthatnosignificantrevenueis expected after a certain amountof timebecauseof the reduction in customerdemand, andhencethefirmnolongerneedstoinvestmoreinlaterstagesunderthisapproach.
Astheoptimaltiming βisrepresentedbysomeexogenousfactors,weexploittheimpactoftheparametersonthethreshold.
Proposition5.Theoptimaltimingthresholdincreaseswithrespecttodemandvolatility.Thatis,
β
0 (46)
Proof.Notingthefractionalvalue 0,wetakethederivativeof fromEq.16withrespecttothedemandvolatility, .Weknow 11isoneofthesolutionstothefollowingquadraticfunction
0(theothersolutionis 0),where12
1 .(47)
Takingaderivativeoftheequation,wehave
0. (48)
Since β 0and β 0,wehave
0. (49)
Furthermore,
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111
0. (50)
Finally,thederivativeoftheoptimalthreshold βwithrespecttoΟis,β
1 1 11 0 (51)
Thispropositionindicatesthattheoptimalmarkdownthresholdincreasesasthevariationindemandincreases.Simplyput,itismorebeneficialforthefirmtowaitanddelaythemarkdown,therebyavoidingtheriskofmakingtheinstantaneousdecisionwhenthemarketishighlyuncerβtain. The firm iswilling tomake themarkdown decision, onlywhen excessive revenue is exβpectedwheretheamountofuncertaintyregardingfuturedemandislarger.
5. Conclusion
Inthispaper,theoptimalpricingpolicyofamonopolisticfirmisinvestigatedwithstrategiccusβtomerbehaviour.Whencustomersstrategicallywait foradiscount, themonopolisthasanopβtiontoofferamarkdowntomaximiseitsrevenue.Assumingthattheunderlyingcustomerdeβmandisstochastic,evolvingdynamicallyovertime,wedevelopavaluefunctionforthefirmtofindtheoptimaltimeforthediscount.Usingarealoptionapproach,thestochasticdynamicproβgrammingmodelissolved.Giventhefixedcostofthemarkdown,servicelevel,andaknowndisβcountedprice,theoptimalpolicyforthefirmistofollowthethresholdpolicy.Thesellermaxβimisesitsrevenuebydiscountingthepriceoftheproductwhenthepotentialcustomerdemandisgreaterthanthethresholdvalue.
Thecontributionofthispaperisasfollows:Consideringtheoptimalmarkdowndecisionforamonopolistic sellerwith strategic customers,we address the gap in other literature on thesecustomerswithproblemsundermarketuncertainty.Astochasticdynamicoptimisationmodelisproposedtofindtheoptimalmarkdownstrategyoftheseller.Arealoptionapproachisappliedtoobtainaclosedβformsolutionofthefirmβsdemandthreshold.Theanalysisoftheoptimaltimβingrevealstherelationshipbetweenthedegreeofmarketuncertaintyandthemarkdowndeciβsionβmaking.
Althoughtheoptimalthresholdpolicyisfound,carefulinterpretationsoftheresultareneedβed.First,customersareawareofpotentialmarkdownswhilethediscountedpriceisknown.Theseller may not exercise the option to markdown if the potential demand never exceeds thethreshold. Second,we found that there is an exponential decrease in the threshold value in adecliningmarket,whichjustifiestheearlymarkdowninsomeindustries.Ontheotherhand,theoptimalmarkdownthresholdincreasesasthevariationindemandincreases.Thisindicatesthata firmneeds to avoid the riskof committingmarkdownpricing too earlywhen themarket ishighlyuncertain.Thecompanyβsmanufacturingandproductionplanningmustbealignedwiththisstrategicdecisiononthemarkdowntiming.
There aremany challenges involved in the proposed study for future research. Discussionoverpotentialdemandisrecommended.Furtherinvestigationonthepostedpricingschemeofdemanddiffusioncanbedevelopedwherethenewproductgetsadoptedinthepopulationovertime.Anotherpotentialareaofresearchwouldbethepredictionofstrategiccustomerdemandbyapplyingdataβdrivenapproaches,suchasmetaβheuristicsandmachinelearningalgorithms.Finally,aninterestingextensionwouldbetoimplementtheproposedframeworkonrealβworldproblemstodemonstratethepracticalimplicationsofourmodel.
Conflict of interests
Theauthors thank theeditorand tworeviewers for their constructivecomments,whichhelpedus to improve thispaper.Theauthorsdeclarethatthereisnoconflictofinterestsregardingthepublicationofthispaper.
Optimal timing of price change with strategic customers under demand uncertainty: A real option approach
Acknowledgement This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A5A8019542).
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Advances in Production Engineering & Management 14(3) 2019 389
Lee, Lee, Kim
Appendix A: Proof of Proposition 1
Since πΈπΈ οΏ½β« πππ‘π‘ππ0 πππποΏ½ = β« πΈπΈ ππ
0 [πππ‘π‘]ππππ by Fubini's Theorem, we first apply Ito's lemma to ππ lnπππ‘π‘ , to find πΈπΈ[πππ‘π‘]:
ππ lnπππ‘π‘ =1πππ‘π‘πππππ‘π‘ β
12
1πππ‘π‘2
(πππππ‘π‘)2 (52)
=1πππ‘π‘
(πππππ‘π‘ + πππππ‘π‘ππππ)β12
1πππ‘π‘2
(πππ‘π‘2ππ2ππ2) (53)
= ππ ππππ + ππ ππππ β12ππ2 ππππ (54)
After integrating and applying the fundamental theorem of calculus, we obtain:
lnπππ‘π‘ β lnππ0 = οΏ½ππ β12ππ2οΏ½ ππ + πππππ‘π‘ (55)
πππ‘π‘ = ππ0πποΏ½ππβ12ππ
2οΏ½π‘π‘+πππππ‘π‘ (56)
The general form of expectation for Gaussian random variable is πΈπΈ[ππππ] = πΈπΈ οΏ½ππππ+12ππ
2οΏ½, where ππ
has the law of a normal random variable with mean ππ and variance ππ2. Since we know the stand-ard Brownian motion πππ‘π‘~ππ(0, ππ), taking expectation on both sides yields the following [9]:
πΈπΈ[πππ‘π‘] = ππ0πποΏ½ππβ12ππ
2οΏ½π‘π‘πΈπΈ[πππππππ‘π‘] (57)
= ππ0πποΏ½ππβ12ππ
2οΏ½π‘π‘ππ0+12ππ
2π‘π‘ (58)
= ππ0πππππ‘π‘ (59) Finally taking integral produces the following results:
οΏ½ πΈπΈ ππ
0[ππππ]ππππ = οΏ½ ππ0πππππ‘π‘ππππ
ππ
0=ππ0ππ
(ππππππ β 1) (60)
Appendix B: Proof of Theorem 1 Substituting Eq. 16 into Eq. 32, we have the following equation:
ππππ = ππβ²(ππππ ππππ + ππππ ππππ) +12ππβ²β²(ππππππππ + ππππ ππππ)2 (61)
= ππππππβ²ππππ + ππππ ππβ²ππππ +12ππ2ππ2ππβ²β²(ππππ)2 + ππππππ2ππβ²β²(ππππ)(ππππ) +
12ππ2ππ2ππβ²β²(ππππ)2 (62)
Taking expectations on both sides to apply some properties of GBM and discarding all terms involving dt to a power higher than 1, we have
E[ππππ] = οΏ½ππππππβ² +12ππβ²β²ππ2ππ2οΏ½dt = [ππππ β ππππ(1 β ππ)]ππππ. (63)
Note that the term (ππππ)(ππππ) has magnitude (ππππ)3/2, πΈπΈ[ππππ] = 0, πΈπΈ[(ππππ)2] = ππππ, and πΈπΈ[ππππ] = 0. After dividing through by ππππ, we have the Bellman Eq. 33.
390 Advances in Production Engineering & Management 14(3) 2019