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AdvancesinProductionEngineering&Management ISSN1854‐6250

Volume14|Number1|March2019|pp93–111 Journalhome:apem‐journal.org

https://doi.org/10.14743/apem2019.1.314 Originalscientificpaper

  

Inventory control model based on multi‐attribute material classification: An integrated grey‐rough set and probabilistic neural network approach 

Zhang, Z.L.a,b,*, Wang, Y.F.c, Li, Y.d,* 

aNortheast Forestry University, College of Economics and Management, Harbin, P.R. China bJiamusi University, College of Economics and Management, Jiamusi, P.R. China cJiamusi University, School of Clinical Medicine, Jiamusi, P.R. China dLiaoning University, Business School, Shenyang, P.R. China  

  

A B S T R A C T   A R T I C L E   I N F O

Efficient and reasonable inventory control canhelp enterprises improve in‐ventory management efficiency, reduce inventory cost, and ensure the fullutilizationofresources.Consideringthattherearemanyattributesofmateri‐al,differentmaterialshavedifferenteffectsonenterprises.Amulti‐attributematerialclassificationmodelbasedongreyroughsetandprobabilisticneuralnetworkisproposed,andaninventorycontrolstrategymodelbasedonmate‐rial classification is constructed according to the characteristics of differenttypesofmaterial.Basedontheconstructionoftherelevantmodels,takingtheinventorymaterialsofsampleEnterpriseAasanexample,thegreyroughsetalgorithmisusedtoreducetheredundantmaterialattributes,andthesampledataofnormalizedreductionattributesareusedtoclassifyanddiscriminatethematerials by probabilistic neural network. The results are simulated byMATLABtoobtaintheefficientandreasonableclassificationofthematerialsofenterprises.Finally,withthesampledataofdifferenttypesofrepresenta‐tivematerials,amatchingmodelofinventorycontrolstrategybasedonmate‐rialclassificationisappliedinpractice,andtheapplicabilityandfeasibilityofthemodelareillustrated,providingascientificbasisforenterprisestomakedecisionsonmaterialmanagementandinventorycontrol.

©2019CPE,UniversityofMaribor.Allrightsreserved.

  Keywords:Inventorycontrolstrategy;Modelling;Materialclassification;Greyroughset;Probabilisticneuralnetwork

*Correspondingauthor:jeanneettee@163.com(Zhang,Z.L.)lyfly68@163.com(Li,Y.)

Articlehistory:Received30November2018Revised4February2019Accepted24February2019

  

1. Introduction 

Material is thepremiseofenterpriseproduction. Inorder toensuretheproductionneeds,en‐terprisesshouldkeeppartofthematerialasaturnoverinventory.However,duetoavarietyofreasons, enterprises generally have high inventory, resulting in inventory overstocking andwasteofresources,affectingtheeconomicefficiencyofenterprises.Inventorycontrolofenter‐prisematerialsisahotissueinscientificmanagementofmaterialsasanimportanttacticaldeci‐sion‐makingofenterprises[1].Itsmainobjectiveistominimizetheinventorycostofmaterialsonthepremisethattheneedsofproductionandoperationofenterprisesaremet[2].

As thepremiseof enterprise inventorycontrol,material classification is the focusof enter‐prise procurement, production, and sales. Therefore, the basic task of inventory control is tocompletetheclassification,storage,andsafekeepingofmaterialsthroughwarehouses.Efficientand reasonable inventory is helpful for enterprises to expedite the flow of materials, reduce

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costs,ensurethesmoothprogressofproduction,andachieveeffectivecontrolandmanagementofresources[3].Traditionalenterpriseinventorycontrolfocusesonthequantitycontrolofin‐ventorymaterialstooptimizethesingleinventorycost;iteveninsiststhatitsmaincontentistomaintainacertainquantityofmaterials.However,asfarasthecontentof inventorycontrol isconcerned,quantitycontrolconstitutesonlyoneoftheimportantitemsandnotthewholecon‐tentofinventorycontrol.Excessiveinventoryandconfusedclassificationwillnotonlyengagealargeamountofoperatingcapitalofenterprises,affecttheturnoverofcapital,increasethecostofcommodityinventory,wasteallocationtime,butalsoincreasethemarketriskofenterprises.On the contrary, extremely low inventory quantity and too fine classification will affect thesmoothprogressofnormalproductionandoperationactivitiesof theenterprisesandwasteasubstantialamountofmanpower,materialresources,andcosts,andevenmaketheenterpriseslosemarket opportunities. Efficient inventory control is a prerequisite for production controland job routing decision‐making in a multi‐objective stochastic manufacturing system [4].Therefore,itisnecessarytoadoptscientificmaterialclassificationandinventorycontrolmeth‐odstoensurethatenterprisesestablishanefficientinventorycontrolandclassificationmecha‐nismonthebasisofthenormalproductionandbusinessoperationactivities.Inthisway,enter‐prisescanmakefulluseoftheirlimitedresources,implementfeasibleorderexecutionandsup‐plyplan, respondquickly tomarketandcustomerdemand,minimize inventoryandoperatingcost, and improve theeconomicefficiencyof enterprises. It is in this context that researchoninventory control based onmulti‐attributematerial classification has become a controversialtopicamongscholars.

2. Literature review 

In order to facilitate inventorymanagement and control, it is necessary to classify inventorymaterials according to certain rules. ABC classification is a classic and widely used method.However,ABCclassificationhasthedefectofsingleclassificationindexinpracticalapplication.Therefore,somescholarstriedimprovingthemethodfromtheperspectiveofclassificationin‐dexoptimizationbasedondifferentscenarios.Forexample,Xiaoetal.(2011)proposedaloss‐based classificationmethod to overcome the cross‐selling effect inABC classification, and theprofit loss of cross‐effectwas considered as an important rule of classification [5]. Kabir andHasin (2013) classified the inventorymaterialswith themulti‐criteriaABC classification inte‐grating fuzzy analytic hierarchy process (AHP) and neural network methods [6]. Šarić etal.(2014) concluded that the multi‐criteria classification approach combined with AHP, neuralnetworkandclusteringanalysiswasmoreeffective thanthe traditionalsingle‐criteriaABC in‐ventory classification approach in inventory control [7].Douissa and Jabeur (2016) tookABCclassificationasanassignmentproblem,and themulti‐criteriaclassificationmethodPROAFTNwasusedtoclassifytheinventorymaterials[8].Mayetal.(2017)proposedanimprovedmulti‐criteria weighted non‐linear ABC optimization method, which offered a better multi‐criteriaclassificationmethodforinventorymaterials[9].Inaddition,somescholarstriedimprovingtheclassificationalgorithmofmaterialclassificationABC.Huetal.(2015),forexample,adoptedK‐MEANSalgorithmofclusteringtoovercomethedivisionerrorintheclassificationboundaryofthetraditionalABCclassificationmethod[10].Chenetal.(2008)establishedamulti‐factorclas‐sificationmatrixmodel, inwhichABC classificationwas combinedwith the complexity of thesupplymarket and the classification criteriawas expanded from three tomultiple categories[11].Theaboveresearchonmulti‐attributematerialclassificationprovidesascientificbasisforformulatinginventorycontrolstrategy.

Studiesoninventorycontrolstrategiesareactuallyconcernedaboutthetimingandmannerofordering,leadingscholarstoformulatecorrespondinginventorycontrolstrategiesconducivetodifferentconditions.Typicalinventorycontrolstrategymodelhasbeenwidelyused.Forex‐ample,StrijboschandMoors(2006)deducedthesafetyfactorof(R,S)inventorycontrolstrategywithmodifiednormaldistributionandpointedoutthatthesafetyfactorofnormaldistributionofgeneralstandardshouldbeincreased,otherwise,thelevelofcustomerservicewouldbere‐duced[12].Rossettietal. (2013)analyzedthe influenceofaggregationutilityofdemand fore‐

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castingtimeoninventorycontrolunder(Q,R)inventorycontrolstrategy.Theresultsindicatedthatthelongertheintervalwas,themorestablethedataperformancewouldbe.Itcouldbeeasi‐lyforecastedbyasimplemodel,butitwasalsoeasytoignoretheproblemofsatisfactionrate[13]. Liu (2015) adopted (s, S) strategy for classA andBmaterialswith a stable demand forspare parts inventory control strategy, and improved the parameter calculation. The (Q, R)strategywasadoptedforclassCmaterials[14].LiuandJiang(2017)studiedtheorderingofcorematerialsof autopartsmanufacturingenterpriseswith the improved (Q,R) inventory controlstrategy[15].Itcanbeseenfromtheaboveanalysisthatdifferentscholarshavedifferentchoic‐esof inventorycontrolstrategies.Forexample, theapplicationof(Q,R)strategiesneedstobespecifictotheactualsituation.

However,inmostcases,thematerialdemandofenterprisesisnotalwaysfixedandtendstoexhibitacertaindegreeofrandomicity.Zhang(2007)putforwardaquantitativeinventorycon‐trolmodelundertheconditionofstochasticdemand,accordingtowhichtheoptimalorderingstrategywasobtained.Itusedstochasticdemandtoobtaintheexpectedvalue,andthen,adopt‐edEOQmodeltosolvetheproblemtoobtaintheoptimalorderquantitywiththisexpectedvalue[16].Güleretal.(2015)studiedastochasticdemandsituationwheredemandwasinfluencedbyprice, and pointed out that demandwould be influenced by both current price and referenceprice.Thesafetyinventorywasusedasadecisionvariableformodellingandsolving.Theresultsshowedthattheoptimallevelof inventoryincreasedwiththeincreaseofreferenceprice[17].Zhao (2016) constructed amulti‐echelon inventory controlmodel for the supply chain understochasticdemandbytheapplicationofcontrol theory[18].Gockenetal. (2017)proposedanoptimization approach to find the initial inventory, reorder point and determine the optimalvalueoftheorderinacompletelystochasticsupplychainenvironmentthroughOptimizationviaSimulation(OvS)approach[19].Moreover,demandnotonlyexhibitsstochastic,butalsoshowsthe characteristics of beingnon‐stationary. Strijboschetal. (2011) studied the interactionbe‐tweenforecastingandinventorycontrolundertheconditionofnon‐stationarydemand,extend‐edtheresearchscopetonon‐stationarydemandbyusingsimulationmethod,andanalyzedthecumulative effects of the optimal estimator, optimal forecasting parameters, and correct vari‐ance[20].Rahdaretal.(2018)putforwardathree‐tieroptimizationmodelinthecaseofuncer‐taindemandandleadtime.Thatmodelsatisfieduncertaindemandandleadtimebyrollingplan,soastominimizethetotalordercost[21].Lietal.(2011)proposedtheuseofconfidenceinfer‐encetosolvetheproblemofnon‐stationarydemand,andconfirmedthatthisapproachwassu‐periortothetraditionalapproach[22].

Basedontheaboveliterature,itcanbefoundthat,fortheinventorycontrolofmulti‐attributematerials,suchasrawmaterialsofmanufacturingenterprisesorsparepartsofacertainenter‐prise,itisgenerallynecessarytoclassifymaterialsfirstandthenadoptcorrespondinginventorycontrol strategies for different classifications. According to the currentmaterial classificationmethods,themulti‐criteriaclassificationmethod,basedonABCclassificationandmatrixclassi‐fication,isthemostwidelyusedandstudiedmethod.Ifmulti‐criteriamaterialclassificationistobe carriedout, the selectionof classification indicatorsand thedeterminationof classificationgradearekeystoachievingmaterialclassification.

Inviewoftheaboveanalysis,thefollowingquestionsareraised:(1)Howtoselectappropri‐ateindicatorsforeffectiveclassificationofmulti‐attributematerials?(2)Whichmaterialclassifi‐cationmethodisthemostsuitable?(3)Howtoproposetargetedinventorycontrolstrategiesfordifferentclassificationsofmaterials?Inordertoanswerthesethreequestions,thispaperpro‐poses amaterial classificationmethod based on rough set probabilistic neural network. Thismethod reduces the duplication and redundancy ofmaterial attributes of the enterprise. Theattributereductionalgorithmofgreyroughsetisadoptedtoreducetheattributeindexofmate‐rials.Theprobabilisticneuralnetwork(PNN)approachisusedtobuildthematerialclassifica‐tionmodelbasedonthereducedattributes;theprobabilisticdistributionofmulti‐Gaussianmix‐tureofapproximatedataindifferentmaterialclassificationsisusedtosolvetheproblemofma‐terialclassification.Onthisbasis,basedontheclassificationresults,fromthepointofviewofthedemand characteristics of different types ofmaterials, an inventory control strategymodel ofdifferenttypesofmaterialsisproposed.Finally,anempiricalanalysisiscarriedoutusingaspe‐

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cificexample.Inthispaper,amaterialclassificationmodelbasedonroughsetprobabilisticneu‐ralnetworkandaninventorycontrolstrategymodelfordifferentmaterialsareproposed,whicharetestedbypracticalcases.Itishelpfulforenterprisestoimprovetheintellectualization,cred‐ibility, and scientific nature inmaterialmanagement, andhas strongpractical significance forinventorycontrolandmanagementofenterprises.

3. Inventory model building 

3.1 Evaluation index system of material attributes 

ThetraditionalABCmaterialclassificationisbasedonthevalueofmaterial(averagecapitaloc‐cupancy),butinreality,anindicatorisobviouslyinsufficienttoshowtheimportanceofmaterial.Therefore, a set of scientific and reasonable evaluation index system ofmaterial attributes isfirstneeded,soastoexpressandrealizetheeffectiveclassificationofmulti‐attributematerialsbyquantitativeindicators,suchastheimportance,availability,difficultyinobtaining,costpro‐portion,andstrategicimportanceofmaterialstoenterprises.Withreferencetothedetermina‐tion ofmaterial attribute indexes in the relevant literature [23‐25], this paper intends to usethreefirst‐levelindicatorstodescribethecharacteristicsofmaterials,namelyprocurementrisk,valueproportion,andstrategicimportance;andthendeterminethesecond‐levelindicators.TheevaluationindexsystemofmaterialattributesisshowninTable1.

Table1Evaluationindexsystemofmaterialattributes

First‐levelIndexSecond‐levelIndexName SerialNo.

Procurementrisk

Impactofsupplierinterruption C1Numberofsuppliers C2Substitutability C3Degreeofdifficultyinobtaining C4Productcomplexity C5

Proportionofthevalue

Totalamountofpurchase C6Proportionoftotalprocurementexpenditure C7Proportionoftotalcost C8Impactoffluctuationsinthepriceofcertainmaterialsonprofits C9

StrategicimportanceBargainingpowerofsuppliers C10Influencedegree ofmaterialsonproductquality C11Lossescausedbyshortageofmaterials C12

Procurementrisk

Procurementriskmainlyreferstotheunexpectedsituationsthatmayoccurintheprocurementprocess. It ismainlyused todescribe the extent of the influenceof the unexpected situationsencountered in theprocurementprocessonproduction.Themain factors influencing the riskdegreeofmaterialprocurementshouldbefullyconsideredindeterminingthesecond‐levelin‐dex of procurement risk,whichmainly come from two aspects:material suppliers and them‐selves [23, 26]. For suppliers, factors such as the impact of interruption of suppliers and thenumberofsuppliersshouldbetakenintoaccount.Formaterialsthemselves,thesubstitutionofmaterials,thedifficultyofobtainingmaterialsandthecomplexityofproductsaretheimportantfactorsinfluencingtheriskofmaterialprocurement.

Proportionofthevalue

Theproportionofthevaluemainlyrepresentsthevalueofmaterials,thatis,thecontributionofmaterialstoproducts.Inordertobetterassignresourcesandenableenterprisestoattachgreatimportance to thosematerials that contribute greatly to enterprises, factors such as the totalamountof purchase, theproportion of total procurement expenditure, theproportionof totalcostandthe influenceof fluctuation inthepriceofcertainmaterialsonprofitsshouldbefullytakenintoaccountintheprocessofconstructingthesecond‐levelindexofvalueproportion [24].

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Strategicimportance

Fromtheperspectiveof the influenceofmaterialsonproduction,strategic importancemainlyfocusesonthestrategic influenceonproductionplan,andthe influencingfactorsmainlycomefromsuppliersandmaterialsthemselves[27].Therefore,thestrategicimportanceofmaterialsisrepresentedbythreefactorsinthispaper,namely,thebargainingpowerofsuppliers,theinflu‐encedegreeofmaterialsonproductquality,andthelossescausedbyshortageofmaterials.

3.2 Classification model of multi‐attribute materials 

Basedontheevaluationindexsystemofmaterialattributesestablishedabove,amaterialclassi‐ficationmodelbasedongreyroughsetandPNNisproposedinthispaper.Firstly,bytakingtheadvantageoftheattributereductionofgreyroughset,theimportantattributesintheclassifica‐tionandevaluationsystemareextracted,andtheinputcomplexityoftheevaluationindexsys‐tem ofmaterial attributes in the classification and decision‐making system is reduced. Then,inventoryclassificationiscarriedoutcombinedwiththestrongclassificationabilityofPNN.ThismethodfullycombinestheadvantagesofgreyroughsetandPNN,simplifiestheinputcomplexi‐ty of material classification system, reduces the complexity of sample training and machinelearning in PNN approach, improves the accuracy of material classification, and achieves thepurposeofbetterassistingenterprisestoclassifymaterialscorrectlyandguidingenterprisestoimplementdifferentinventorycontrolstrategiesaccordingtodifferentmaterialclassification.

Greyroughsetattributereductionalgorithm

Inordertosolvetheproblemofduplicationandredundancy,theattributereductionalgorithmof grey rough set is proposed in this paper. Let , , be a multi‐attribute informationsystem,while 1, 2,⋯ , isanon‐emptyfinitesetofobjectsand 2; , , ⋯ , isanon‐emptyfinitesetofattributes,includingthesetofefficiencyindexesCandthesetofcostindexes ; the larger the indexattributevalueofset , thebetter,while thesmaller the indexattributevalueofset ,thebetter.Let and bethesubscriptsetsofefficiencyindexesandcost indexes, respectively,where ∪ , ∩ ∅and 2. The indexes are dividedinto conditional attribute and decision attribute , and ∪ , ∩ ∅,∀ ∈ ,∀ ∈ , representsthesetofthevalueofindexes,and representstheobservedvalueoftheobject abouttheindicator .

Inthesystem,eachindexintheindicatorsethasdifferentdimensionsandattributes,andthetypeofattributevaluehastwoforms,namelyclearnumberandlinguisticitems,andtheattrib‐ute value of the same attribute has the same information form. For convenience, let andrespectively denote the attribute subset whose attribute values are clear number and the

formal information of the linguistic items. , , ⋯ , , , , ⋯ , ,and ∪ ;Let and bethesubscriptsetsofattributesubsets and ,respectively.

1, 2,⋯ , , 1, 2,⋯ , . For attribute values, the specific description is asfollows:

If ∈ ,then , ∈ , ∈ ,where isarealnumericvalue,withoutlosinggen‐erality,heresuppose 0.

If ∈ , then , ∈ , ∈ ,where is a linguistic item, ∈ . HereP is a

set of linguistic items, 0, 1,⋯ , 1, , 1,⋯ , , where represents the

1 ‐thlinguisticitemin ,and 1 representsthenumberofitemsin .When 6, , , , , , , = , , , , ,

, when , isbetterthanorequalto ;if isbetterthanorequalto ,then , , , ;when , ,where

isaninverseoperator.Thespecificnormalizedcalculationformulasarerespectivelyex‐pressed,asshownbelow:

(a) If ∈ ,thenthenormalizedcalculationformulaisasfollows:

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, ∈ , ∈ ∩

, ∈ , ∈ ∩(1)

where max , ∈ (2)

, ∈ (3)

(b)If ∈ ,thenthenormalizedcalculationformulaisasfollows:

, ∈ , ∈ ∩

, ∈ , ∈ ∩(4)

Linguisticitem canbeconvertedintocorrespondingtriangularfuzzynumber thatis, , , ;thecalculationformulaisshownthus:

, , max , 0 , , min , 1 (5)

(6)

Afterdimensionlessprocessingof ,dimensionless featurevaluesofobjectbehaviourcanbeobtained.Anytwoobjects , ∈ ontheindicator∀ ∈ ,greycorrelationcoef‐

ficient ,andcorrelationdegree ofindicatorset ,correlationclusteranalysiscanbecarriedoutforeachscheme.Thecalculationformulatocalculatethegreycorrelationcoefficient andcorrelationdegree ofthescheme , onattribute andattributesetAisshownasEq.7.

7

∑ (8)

Onthisbasis,greyincidencematrixbetweenobjectscanbeestablishedasfollows:

⋯ ⋯

⋯ ⋯ ⋮⋮⋱⋮⋱⋮ ⋯ ⋯ ⋮⋮⋱⋮⋱ ⋮ ⋯ ⋯

According to thegrey incidencematrix, is the correlationdegreeof thedecisionobjects

, onattributesetA,whichrepresentsthepossibilitythattheobjectbelongstothesameclassi‐fication, and the best critical value can be determined by the Bayesian criterion. The specificmethodisasfollows:

(i) , ,and respectivelydenotethatobjects , haveahighcorrelationdegree , ,

amediumcorrelationdegree , , a lowcorrelationdegree , ; , and respectively denote the expected loss function, and that object belongs to

, , , , .ThecalculationformulaofexpectedlossfunctionisshownasEq.9,Eq.10,andEq.11:

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1 (9)

1 (10)

1 (11)

where , and respectively indicate the loss function, and that the decision makerstakeunderahighcorrelationdegree , . , and respectivelyindicatethelossfunction,andthatthedecisionmakerstakeunderlowcorrelationdegree , .

(ii)AccordingtotheBayesiandecisioncriterion,theoptimalactionplanneedstobeselectedastheactionsetwiththeminimumexpectedloss.Thespecificdecisionrulesareasfollows:

Decisionrulesof , :ifboth and aretrue,then ∈ , ;Decisionrulesof , :ifboth and aretrue,then ∈ , ;Decisionrulesof , :ifboth and | | aretrue,then ∈ , .

(iii)AccordingtoBayesianreasoning,therulesforsimplifyingdecision‐makingareasfollows:

If and ,thenthecorrelationdegreeofdecisionobject , ishigh;

If and ,thenthecorrelationdegreeofdecisionobject , ismedium;

If and ,thenthecorrelationdegreeofdecisionobject , islow;where∀ , ∈ , ∀ ∈ , 0 1.Thecorrelationdegreeofobject aboutattributeset isdividedasfollows:

, ∈ (12), ∈ (13)

, ∈ (14)

Thecalculationformulasof and are:

15

16

Finally,basedontheclassificationofcriticalvalues and , theattributesarereduced,andthereductionmethodsareasfollows:

⊆ , , ∈ |∀ ∈ , , , , , and divides theobject into equivalenceclassifications,whichisdenotedas , , ⋯ , ;

istheequivalentrelationin and ∈ ,if ,thenisreduciblein ,otherwise, isirreduciblein .Ifeach isirreducible,then isindependent;

If ∈ and is independent, at the same time, , then, is the reduc‐tionof onattributeset .Tosumup,theproblemtobesolvedinthispaperishowtoobtainthereductionschemeofall

indexes throughamulti‐attribute reductionmethodbasedonmulti‐attribute information sys‐tem ,attributecorrelationcoefficient ,andcorrelationdegree .Thealgorithmofattrib‐utereductionbasedongreyroughsetisasfollows:

Step1: NormalizetheattributeindexdatawithmultipleinformationformsaccordingtoEq.1toEq.6.

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Step2: ObtainthecorrelationdegreebetweenobjectsaccordingtoEq.7toEq.8,andthenes‐tablishtheincidencematrixofcharacteristicvariables.

Step3: Obtain theoptimalcriticalvalueof thecorrelationdegreeofeachobjectaccordingtoEq.9toEq.16andcategorizetheobjectsaccordingly.

Step4: Reducetheattributeindexaccordingtothedivisionofthecorrelationdegree.

PNNmaterialclassificationdiscriminatemodel

ProbabilisticNeuralNetworkswasproposedbySpechtin1990,itisaneuralnetworksuitableforclassification[28].AccordingtoBayesianclassificationrules,ittakesthemixedformofmul‐ti‐Gaussianfunctiontoapproximatetheprobabilityofdataineachclassificationandselecttheonewiththemaximumprobabilityvalueastheclassificationasthedatabelongsto.Inessence,itisaparallelalgorithmbasedonBayesianminimumriskcriterion.Therefore,basedonthereduc‐tionresultsofmaterialattributes,thePNNapproachisusedtobuildtheclassificationanddis‐criminationmodelofmaterials.ThismodelappliesBayesiancriteriontoestimatetheposteriorclassificationprobability ,thatis,theunknownvector belongstotheprobabilityofallpossibleclassification .AccordingtoBayesiancriterion,thisprobability isproportionaltotheproductofpriorprobability (theratioof theunknownvectorbelongstoeachclassification)andtheprobabilitydensity function (probabilitydensity functionofeachclassificationofvector),thatis, ∝ ,wheretheprobabilitydensityfunctionofclassification isasperEq.17:

1

2

12

17

where is the ‐th training sample belonging to classification , is the number of trainingsamplesinclassification , isthesmoothingparameter,and isthedimensionofeachsample.Ifthepriorprobabilityisunknown,itcanbeestimatedbytheoccurrencefrequencyofeachclas‐sificationsampleinthetrainingsetasperEq.18:

18

Ifallkindsofpriorprobabilitiesareassumedtobethesameandconstanttermsareignored,then:

∝2

19

PNNcanbeobtained through three layerneuralnetwork: (a) the input layeraccepts inputvectorsandformatsthem;(b)inthelayerofradialbasisneuron,thedistancebetweentheinputvector and the training sample is first calculated and thenmultipliedby the thresholdvector,calculatedbytheradialtransferfunctionatlast;(c)inthecompetitivelayer,thecalculationre‐sultsofnodesinthefirstlayerareacceptedandtheoutputbelongingtothesameclassificationis synthesized. Finally, theclassificationofunknownvectors is judgedaccording to thesizeofeachoutputresult.ItcanbeseenthatPNNisobtainedbycombiningradialbasisfunctionneuralnetworkwithcompetitiveneuralnetwork. It isanewclassificationtool,whichconsidersboth

Fig.1ThespecificstructureofPNN

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theinhomogeneityoftheinputsamplesandtheclassificationandpatternrecognitioncapabilityofthecompetitiveneuralnetwork.ThespecificstructureofPNNisshowninFig.1.

As shown in Fig. 1, represents the number of input vector elements; is the number ofneurons in the second layer; is the sample matrix of input;∥ ∥represents the distancebetweentheinputvectorandtheweightvector; isthedistancebetweenthesamplematrixofinput . andweightmatrixmultipliedbythethreshold ; representsthe i‐thelementof;while istheoutputfromradialbasisneuronlayer, istheoutputfromcompetitivelay‐

er, and ∥ . ∥ ; . , where is theradialbasisfunction, isthecompetitivefunction; istheweightmatrixoftheradi‐albasisnetworklayer; . isthei‐throwvectoroftheweightmatrix , istheweightmatrixof thecompetitive layer;module represents thecompetitive transfer function, that isthemaximumvalueof eachelement in its inputvector is calculated, theoutputofneuronscorrespondingtothemaximumvalueissetto1,andtheoutputofneuronsofotherclassifica‐tionsaresetto0.Specifically,theinputweightofthefirstlayerofthePNNnetwork . isthetransportmatrixof the input sample ; after calculationby∥ ∥, theoutput vector of thefirstlayerrepresentstheapproximationdegreebetweentheinputvectorandthesamplevector,thenmultiplieswiththethresholdvectorandisthencalculatedbytheradialtransferfunction.Whentheinputvectorisapproximatetothesamples,alltheelementscorrespondingto willbe1s.Theweightofthesecondlayer . issetastheexpectedvaluevectormatrix ;onlyoneelement in each row vector is 1, representing the corresponding classification; the remainingelementsare0,andthentheproduct iscalculated.Finally, isobtainedthroughthecom‐petitivetransferfunctionofthesecondlayer;thelargerelementis1andtheothersare0.Atthispoint,thePNNnetworkcancompletetheclassificationofinputvectors[29,30].

Duetothelargequantityandcomplexityofthematerials,theclassificationresultsshouldbesimplifiedasfaraspossible. Theattributesofmaterialsarevarious,andthedegreeofinfluenceandimportanceofmaterialsaredifferentfordifferentequipment.Themaximumandminimumvalues of expert evaluation classification score are extracted, and then the value interval isdividedintofourequalparts;eachsub‐intervalcorrespondstoascore,equaltothescoresof1,2,3,and4.Here,thematerialsaredividedintofourgrades,accordingtotherangeofmaterials:strategic materials, bottleneck materials, general materials, and leverage materials. Amongthem,strategicmaterialsmainlyincludematerialsthatareofvitalimportancetotheproductsorindustrialprocessesoftheenterprise.Thesematerialsoftenhaveahighsupplyrisk,mainlybe‐causeoftheshortageofsupplyortransportationdifficulties.Thebottlenecktypeismainlychar‐acterizedbythefactthatthepriceofthematerialitselfmaybenotveryexpensive,butitisstilldifficulttoobtain.Themaincharacteristicsofgeneralmaterialsarerelativelyrichsupply,littleinfluenceonprocurementcosts,andhighstandardizationofproducts.Leveragedmaterialsarerelativelysimplifiedandsharedinspecifications,whichhaveasignificantinfluenceoncostandhavethecharacteristicsofalargenumberofsuppliersandfiercemarketcompetition.

3.3 Inventory control strategy model based on material classification 

Differenttypesofmaterialsaresuitablefordifferentinventorycontrolstrategies.Inthispaper,theappropriateinventorycontrolstrategymodelwillbeselectedaccordingtothecharacteris‐ticsofeachkindofmaterials (strategicmaterials,bottleneckmaterials, generalmaterials, andleveragedmaterials).Therefore,itconstructsamatchingmodelbasedonmaterialclassificationandinventorycontrolstrategy,asshowninFig.2.

 

Fig.2Matchingmodelofinventorycontrolstrategybasedonmaterialclassification

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Strategicmaterials–(T,S)strategymodel

Strategicmaterialhasahighvalue,greatcomplexity,andstrongprofessionalism,andthesuppli‐erhasgreat influenceon it.The inventory levelshouldbereducedasmuchaspossible,andagood cooperative relationship should be established with the strategic material suppliers.Therefore, the (T,S) strategymodel canbe adopted.The inventory cycle interval of the (T,S)strategyisrelativelylong;itcheckstheinventorylevelthroughtimeTandsetstheinventorytothemaximuminventorylevelS.Inthe(T,S)strategymodel,threeparametersneedtobedeter‐mined:ordercycleT,maximuminventorylevelS,andorderquantityQ.

OrdercycleT

TheordercycleTisafixedvalue.Generallyspeaking,theordercycleTneedstobedeterminedaccordingtotheconsumptionofmaterials.

MaximuminventorylevelS

S should satisfy the consumption of order cycleT and the order lead time.Meanwhile, safetyinventoryshouldalsobeconsidered inorder toprevent theuncertaintyofdemand.Assumingthat the demand in order cycle and lead time is normal distribution, themeanvalue is, thestandarddeviation is , the lead time is p, the safety inventory is Iss, and the safety coefficient is k.Then,theexpressionofthemaximuminventorylevelSis:

(20)

(21) OrderquantityQ

LettheinventorylevelattimetbeItandtheorderquantitybeQt.Fromtheoperationprocessof(T,S)strategymodel,theexpressionofQtisasfollows:

(22)

However,inpractice,materialsareusuallycomposedofaunitpackage,thatisthereisamin‐imumpackage unitQ0, and the order quantity should be several times theminimumpackageunit.Therefore,itcanbefurtherwrittenasfollows:

(23)

(24)

where,roundup()representstheupwardintegerfunction.

Bottleneckmaterials–(Q,R)strategymodel

Due to the low value of bottleneck materials—great complexity, strong professionalism, andhigh influenceofsuppliersonthem—it isnecessarytokeepabreastof the inventorystatusofsuchmaterials,setupsafeinventory,andadopthighsecurityinventorystrategytoreducetheinventory level. Therefore, the strategymodel(Q,R) is selected in this paper. The fixed‐pointquantitative(Q,R)strategyofcontinuousinventoryismainlyapplicabletothebottleneckmate‐rialswith largedemandandgreatuncertainty, andno shortage is allowed.Once the shortageoccurs,thecostofshortageisveryhigh.

Supposetheorderandpurchasecostofbottleneckmaterialiis ,then:

(25)

where representsunitpriceofmateriali; istheaveragedemandofmaterialiintimet.Thetotalorderingbusinesscost withintimetcanbeexpressedas:

∙ (26)

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where isthesingleordercost(includingstoragecost,travelcost,etc.); istheorderquantityofmateriali.

Thetotalstoragecostofmaterialiintimetis ,andthen:

∙ ∙ (27)

where representsthestoragecostofperunitmaterialinunittimeofmateriali; istheorderpointofmateriali.

Ifthedemand ofmaterialiinleadtimeisgreaterthantheorderpoint ,therewillbeashortage.Theaveragevalueofshortageis:

∙ ∙ ∙ (28)

Iftheshortagerateis,thenumberofpossibleshortagesinttimeis ∙ .Iftheunitcostoflossduetotheshortageofmaterialiis ,thentheaveragecostofshortageintimetis :

∙ ∙ ∙ ∙ ∙ ∙ (29)

Therefore,theobjectivefunctionnamely isthetotalinventorycostofmaterialiintimet:

∙ ∙ ∙2 ∙ ∙ ∙ ∙ ∙ ∙ 30

ThepartialderivativesofthesumofEq.30arerespectivelyobtained,andtheresultis:

∙ ∙ ∙ ∙ ∙ ∙

∙ ∙ ∙

(31)

SetEq.31equaltozero;thenget:

∙ ∙ ∙ ∙ ∙

∙ ∙ ∙

(32)

Generalmaterials–(s,S)strategymodel

Generalmaterialshavealowvalue,stronguniversality,andlowinfluencefromsuppliers,whichare suitable to adopt(s,S) strategymodel. The(s,S)strategy is also known asmaximumandminimumstrategy.Inthisstrategymodel,fourparametersneedtobechecked,namelyinvento‐rychecktime,andreorderpoints,orderlevels,andorderquantityQ.

Inventorychecktime

The inventory level is gradually reduced with consumption, while the continuous inspectionstrategydoesnotmeantochecktheinventoryatanytime,whichisnotfeasibleinpracticalop‐eration,especiallyforthematerialinventoryofvariousmanufacturers.Therefore,itisnecessarytodetermineaninventoryinspectioncycleandcheckpoint,whichshouldbeconsistentwiththeproductionplanthatiskeepinginstepwiththematerialdemandplan.

Reorderpoint

Inthisstrategymodel,whentheinventoryleveldropstosorbelow,theorderwillbeissued;sotheinventoryoftheorderingpointneedstomeettwoconsumptionconditions:oneistheinven‐toryconsumptioninleadtime,andtheotheristoguaranteetheservicelevelsoastoavoidtheshortagecausedbyanincreaseinsupplyordemand.

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If it is subject to thenormaldistributionduring the lead time,withameanvalueof andstandarddeviationof ,theleadtimeisp,andthedemandofeachperiodisindependentfromeachother;Disthedemandofleadtime,andthestandarddeviationis .Accordingtothena‐tureofnormaldistribution,then:

(33)

(34)

Thesafetyfactorisk,andthentheexpressionofsafetyinventoryIssis:

∙ ∙ ∙ ∙ (35)

Therefore,theexpressionofreorderpointsis:

(36)

DeterminationofmaximumS

MaximumSis theorder levelS.Since the strategicmaterials belong to thematerialwithhighvalue, their inventory levelSshouldbereducedasmuchaspossible.That is, theconsumptionwithintheleadtimecanbesatisfiedonthebasisofreorderpoints.TheexpressionofSis:

(37)

OrderquantityQ

TheorderquantityattimetissettoQtandaccordingtotheoperationprocessof(s,S)strategy,theexpressionis:

(38)

The(s,S)strategyalsoneedstoconsidertheconstraintsoftheminimumorderunitwhenor‐dering.Therefore,itcanbefurtherwrittenasfollows:

(39)

40

Leveragedmaterials–(T,s,S)strategymodel

Leveragedmaterials have a high value, strong universality, and low influence from suppliers.Somematerialsshouldbeselectedtosettheinventoryandreducethetimesofpurchase.There‐fore,the(T,s,s)strategymodelismoreappropriate.(T,s,S)strategyisacomprehensivestrate‐gy thatcombines(s,S) strategywith(T,S)strategy,whichcanprovidegreater flexibility thanthefixed‐cycle,unifiedorderingstrategy(T,s).WhereTrepresentsthebasicorderintervaltime,sandSrespectivelyrepresenttheorderpointandmaximuminventoryofmaterials.Accordingtothisstrategy,materialsareinspectedperiodically,andeachmaterialadoptsanindependentandperiodic(s,S)strategy.InventoryischeckedatintervalsofT.Iftheinventorylevelofmate‐rial iis belowor equal to its order point, itwill be replenished to themaximum inventoryS.Therefore,fourparametersneedtobedetermined,namelyT,s,SandQ.

OrdercycleT

Similarto(T,S)strategymodel,theordercycleTof(T,s,S)strategyisafixedvalue. Generallyspeaking,ordercycleTneedstobedeterminedaccordingtotheconsumptionofmaterials.

Orderpoints

Differentfromthecontinuousinventory,thecorrespondingperiodoftimetodeterminethede‐mandtobemetbytheinventoryattheorderingpointisnotonlytheleadtimeoforder,butalsoaninventorycycle.Replenishmentmaybenotreplenishedatthetimeofinventorywhentakingperiodicinventory.Ifthereisnoreplenishment,theopportunityforreplenishmentisatthenexttimeofinventory.Itcanbeseenthattheserviceleveltomeetthematerialdemandiswithinthetime range when periodic inventory is adopted. Safety inventory and order point are

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determined on the basis of total demand in time , and the value is .Where istheexpectedvalueofthetotalquantitydemandedintime ; isthesafetyinventory.Forthegivenservicelevelrequirementα,thecorrespondingsafetyfactorkcanbeobtainedbyreferring to thestandardnormaldistribution table. Inaddition tomeeting thedemandinleadtime,theorderpointofeachcyclealsomeetthedemandwithintheinventoryperiod,thus:

(41)

where isthesafetyinventory,and isthestandarddeviation.

MaximuminventoryS

ThedeterminationmethodofmaximuminventorySisthesameasin(s,S)strategy,sothevalueofmaximumSshouldbeassmallaspossible,thatis,theconsumptioninadvanceperiodcanbesatisfiedonthebasisoftheorderpointS,whichisthemeanvalueofp inadvanceperiod,andtheexpressionofSis:

(42)

OrderquantityQ

Lettheorderquantityattimetis ,andtheexpressionfor is:

(43)

Theconstraintoftheminimumorderunitshouldalsobeconsideredwhenordering.There‐fore,itcanbefurtherwrittenas:

(44)

(45)

4. Results and discussion: Inventory model application model application 

In thispaper,achemicalEnterpriseAwasselectedas theresearchsample toapply themulti‐attributematerialclassificationmodelandtheinventorycontrolstrategymatchingmodel.TheinventoryofenterpriseAmainlyincludesthefollowing60typesofmaterials,asshowninTable2.

Table2MainmaterialcategoriesofEnterpriseANo. Name No. Name No. Name

1Metallurgicalmaterialsandcastironpipes

21 Labourprotectionarticles 41 Weldingmaterials

2 Petroleumspecialpipes 22 Oilspecialequipment 42 Fasteners

3 Commonsteel 23Specialequipmentforrefiningandchemicalindustry

43 Bearing

4 Wireandmetalropes 24 Constructionmachineryandequipment 44 Valves

5Nonferrousmetalsandpro‐cessedmaterials

25 Liftingandconveyingequipment 45 Fireequipment

6 Buildinghardware 26 Generalmachineryandequipment 46 Othermechanicalequipment7 Petroleumandproducts 27 Metalworkingmachineryandequipment 47 Specialtoolsforpetroleum

8 Coal 28 Powerequipment 48Petroleumdrillingequipmentacces‐sories

9Non‐metallicbuildingmate‐rials

29 Transportationequipment 49Accessoriesforrefiningandchemi‐calequipment

10 Cementandproducts 30 Textileequipment 50 Textileequipmentandaccessories11 Woodandproducts 31 Electricalandelectricalequipment 51 Industrialandminingaccessories

12Petroleumspecialchemicalproducts

32 Electricalmaterials 52 Pipefittings

13 Catalystandadditive 33 Electricalcomponents 53 Sealingelements14 Rubberandproducts 34 Daily‐useelectricappliances 54 Internalcombustionengineparts15 Plasticandproducts 35 Communicationequipment 55 Heavy‐dutyautoparts16 Paintandpigments 36 Electronicindustrialproducts 56 Generalautoparts

17 Generalchemicalproducts 37 Petroleumspecialinstruments 57Fittingsforwaterwayrailwayequipment

18 Glassinstrument 38 Universalinstruments 58 Othermechanicalparts19 Pyrotechnicproducts 39 Smallmachinery 59 Packingmaterials20 Textileproducts 40 Toolsandmeasuringtools 60 Miscellaneousproducts

 

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4.1 Application of multi‐attribute material classification model 

Based on the evaluation index system ofmaterial attributes, this paper adopted 12 attributeindicators(Table1)toclassify60kindsofmaterialattributesofEnterpriseA. Firstly,theevalua‐tionindexofmaterialattributeswasreducedbythegreyroughsetalgorithm,andthen,basedonthereductionresults,thePNNdiscriminationmodelwasusedtorealizetheeffectiveclassifi‐cationof60typesofmaterials.

Applicationofmaterialattributereductionalgorithmbasedongreyroughsets

BeforethematerialclassificationofEnterpriseA,attributereductionwasneededtoremovedu‐plicateandredundantindicators.Thisprocessadoptedthegreyroughsetalgorithm,whichcaneffectivelysupportthewholeprocessfromdatapre‐processingtoattributereductionanalysis.Among these 12 attribute indicators, , , and were clear number information,and , , , , , , and werelinguisticiteminformation.Duetothelargevarietyofmaterials,partialmaterialsampledatawithmultipleinformationformsarelisted,asshowninTable3.

Inordertosolvetheproblemofindicatorreduction,thecalculationprocessusingthealgo‐rithmgivenaboveisbrieflyexplainedbelow.

Firstly,materialclassification indicatordatawithmultiple information formswillbestand‐ardizedaccordingtoEq,1toEq.6asshowninTable4.

Table3Partialmaterialsampledatawithmultipleinformationforms C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12

Metallurgicalmaterialsandcastironpipes

B 20 M We M 109 8 7 B B B EW

Petroleumspecialpipes EW 7 P B B 260 12 10 B EW B EWCommonsteel We 26 B M P 100 5 3 M B We WeWireandmetalropes B 18 EW P P 80 3 2 P We We M

Table4Standardizationofsampledataofsomematerialswithvariousformsofinformation C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12

Metallurgicalmaterialsandcastironpipes 0.84 0.60 0.52 0.68 0.52 0.79 0.88 0.88 0.84 0.84 0.84 0.95

Petroleumspecialpipes 0.95 0.08 0.34 0.84 0.84 0.39 0.82 0.81 0.84 0.95 0.84 0.95Commonsteel 0.68 0.84 0.84 0.52 0.34 0.73 0.93 0.96 0.52 0.84 0.68 0.68Wireandmetalropes 0.84 0.52 0.95 0.34 0.34 0.93 0.96 0.98 0.34 0.68 0.68 0.52

Secondly,thegreycorrelationdegreeofattributesetamongmaterialswascalculatedaccord‐ingtoEq.7andEq.8.Then,accordingtoEq.9toEq.16,consideringthelossesfacedbyEnter‐priseAinmaterialclassificationandassuminglossfunctions,then:

0.26, 0.64, 0.72, 0.79, 0.67, 0.09. The optimal criticalvaluecanbeobtainedasfollows:

0.600

0.558

Accordingtotheoptimalcriticalvalue,thesetofallthematerialswithahighcorrelationde‐gree,mediumcorrelationdegree, and low correlationdegree canbedetermined, as shown inTable5.

Table5Thecorrelationdegreedivisionofmaterials

U , , , 1 {1,2,5,7‐8,12,37,47} {3‐4,6,9‐11,13‐36,38‐46,59‐60} {48‐58}2 {1,2,5,8,12,24,31,37} {3‐4,6‐7,9‐11,13‐23,25‐30,32‐36,38‐48,52,56,58‐60} {49‐51,53‐55,57}3 {3‐4,6,9‐11,13‐21,32‐36,38,40‐45} {1‐2,5,7‐8,12,37,47‐60} {22‐31,39,46}4 {4,11‐19,35‐44,48‐52,60} {1‐3,5‐10,20‐23,31‐34,45‐47,53‐59} {24‐30}

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AccordingtothedivisionofmaterialcorrelationdegreeshowninTable5,thecorrelationde‐gree of materials can be determined thus: {1‐2,5,7‐9,22‐30,46}, {3‐4,6,10‐20,32‐39,42‐45,47‐49,58‐60},{21,31,40‐41,50‐57};thatis:

⁄

1 2,5,7 9,22 30,46

3 4,6,10 20,32 39,42 45,47 49,58 60

21,3

;

1,40 41,50 5

;

7

Finally,indexreductioniscarriedoutaccordingtotheclassificationofcorrelationdegree.Conditionalattributeindexesarefoundanddeleted.Bycalculation,reductionisasfollows:

;⁄ ; ;⁄⁄ ;⁄ ;⁄ ;⁄ ;⁄ ; ;⁄⁄ ;⁄ ; .⁄⁄

Reductionresultsof

,⁄ ,⁄ ,⁄ ,⁄ ,⁄

⁄ ,and ⁄

areequal,anditisfoundthatthereductiondoesnotinfluencetheclassificationresults.There‐fore,theminimumsetofattributescanbeobtainedas , , , , ;thatis,theoriginal12indicatorscanbereducedto5.

ApplicationofPNNmaterialclassificationdiscriminatemodel

Basedonthematerialattributereduction,thematerialsofEnterpriseAareclassifiedintofourgrades frommaterialvalue, importance,complexity,andriskbyexperts’evaluation,accordingto the five attributes of material attributes reduction (each attribute of each classification isscoredwithascoreof0−10points).Theclassi icationresultisveri iedbyPNN.Thefourgradesofmaterials aftermaterial classification are: I(strategicmaterial), II(bottleneckmaterial), III(generalmaterial)andIV(leveragedmaterial).

Ascanbeseen fromthematerialattribute reductionresults, thereare fivemainattributesthatinfluencethematerialclassificationofEnterpriseA,namelytheinfluenceofsupplierinter‐ruption,substitutability,productcomplexity,proportionintotalprocurementexpenditure,andtheinfluenceofmaterialsonproductquality.Therefore,theinput layerofPNNhasfivenodescorrespondingtothesefivecharacteristicparameters.Inthispaper,cross‐validationisappliedtocross‐trainandteststhe60sampledatainTable2.Thenumberofradialbasisneuronsisde‐terminedbythenumberofthetrainingsamplesandthenumberoftheneuronsinthesecondlayerofPNNisequaltothenumberoftheclassificationpatterns,whichareI(strategicmaterial),II(bottleneckmaterial),III(generalmaterial),andIV(leveragedmaterial).Therefore,thenum‐beroftheneuronsinthesecondlayerofPNNisfour.Thetransferfunctionofthesecondneuronlayer is a competitive transfer function, which selects the results with the largest distanceweightvaluesas thenetwork’soutput; that is, themostpossibleclassificationpatternresults,correspondingtotheinputvectors,aretakenastheoutput.

In thispaper,Matlab isusedtowrite thesimulationprogram,andnetworktrainingadoptsthemethodofcross‐validation[30].

Step1: Randomly select a number of sample data (32 groups) from each category of expertevaluation forPNNtrainingandtheremaining28groups for testing.Thetrainingre‐sultsandpredictioneffectofPNNareshowninFig.3andFig.4.

Step2: TestdatainStep1(28groups)areusedfortraining,whilethe32groupstrainingdatain Step 1 are used for testing. The training results and prediction effect of PNN areshowninFig.5andFig.6.

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108  Advances in Production Engineering & Management 14(1) 2019

Fig. 3 Effect and error diagram of PNN network training 1 Fig. 4 Prediction effect of PNN network 1

Fig. 5 Effect and error diagram of PNN network training 2 Fig. 6 Prediction effect of PNN network 2

This isequivalenttotestingall thedata inthisway.Theprobabilityofeachcategory isob‐tainedbycross‐validation.Finally,theclassificationsaregiven.Combiningtheresultsofthetworoundsoftrainingtests,thetotalaccuracyof98.3%isobtainedbycalculation.Fromtheclassifi‐cationresults,theestablishedPNNhastheabilityofaccurateclassificationrecognition.

4.2 Application of inventory control strategy matching model based on material classification 

Applicationof(T,S)strategymodel

Accordingtotheinventorycontrolstrategymatchingmodel,itcanbeseenthat(T,S)strategyissuitable for the inventorycontrolof strategicmaterials.Thispaper takesmaterial26 (generalmachineryandequipment)asanexample toapply the(T,S)strategymodel.According to thepersonnelexperienceofinventorymanagementofmaterial26,theinspectioncycleisgenerallysetfor12weeks,thusT=12;theleadtimeoforderisfourweeks(i.e.,p=4);theservicelevelis95%,andtheminimumnumberofpackagesisfour.Accordingtothetableofcustomerservicelevelandsafetyfactor(Table6),thesafetyfactork 1.65,andaccordingtothenormaldistribu‐tiontestresultsofenterprisedemandprediction, 41and 22.Thus,thereare:

Maximuminventory 1.65 4 22 41 12 801.2 Checkthattheinventorylevelatthebeginningofthemonthis779,andorderquantityis

801.2 779 /4 4 24.

Table6Commoncustomerservicelevelandsafetyfactortable

Servicelevel(%) 100 99 98 97 96 95 90 85 80Safetyfactor 3.09 2.33 2.05 1.88 1.75 1.65 1.28 1.04 0.84

Applicationof(Q,R)strategymodel

Fromthematchingmodelofinventorycontrolstrategy,itcanbeseenthat(Q,R)strategyissuit‐ablefortheinventorycontrolofbottleneckmaterials.Thispapertakesmaterial48(petroleumdrillingequipmentaccessories)asanexampletoapply(Q,R)strategymodel.Accordingtotheinventory informationofmaterial48, itspurchaseprice is 79.19yuan/piece, the costof asingleorderis 24885yuan/time,thesingleshortagecostis 420yuan/piece,andtheunitstoragecostis 2.5yuan/piece/week.Accordingtothefittingresultofmaterial48de‐mand data in time t t 36 , the demand distribution of material 48 in unit time is~ 185, 8 . Therefore,demand distribution in time t is ~ 6660, 36 8 ; that is: μ

6660, σ 72√2;leadtime 8,andthedemanddistributionwithintheleadtimep is:

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~ 1480, 8 8 , that is 1480, 16√2;the service level is 95 %, and the

shortagerate 0.05,andset ∙ ∙ ∙ .Eq.32isrewrittenthus:

∙ ∙

(46)

. .

.1

.

.

(47)

Take∙

.1919.11and substituteQi inEq. 1 for Eq.47 to ob‐

tain ;then inEq.2issubstitutedwithEq.47toobtain ;substitute inEq.1forEq.47toobtain ;then inEq.2issubstitutedwithEq.47toobtain ;iterateoverandoveragainuntiltheconvergencestateof isreached.Atthispoint, and aresolved.Thefinalsolutionis:

20641848

Applicationof(s,S)strategymodel

Fromthematchingmodelofinventorycontrolstrategy,itcanbeseenthat(s,S)strategyissuit‐ableforinventorycontrolofgeneralmaterials.Thispapertakesmaterial59(packagingmateri‐als)asanexampletoapply(s,S)strategymodel.Accordingtothepersonnelexperienceofinven‐torymanagers,theleadtimeofmaterial59istwoweeks,theservicelevelis90%,andthesafetyfactoris1.28,whichisobtainedbyTable6.Themeandemandinleadtimeis 7198andthestandarddeviationis 3288.Therefore,

Orderpoint ∙ ∙ 7198 2 1.28 √2 3288 20348pieces;

Maximuminventorylevel 20348 7198 2 34744pieces;

Material59isinspectedonceaweek.Iftheinventorylevelis18,000onMonday,whichislessthan20,348attheorderingpoint,theordershouldbeissued.Iftheminimumnumberofpackag‐ingunitsforthematerialis1,000,then:

Orderquantity 34744 18000 /1000 1000 17000pieces.

Applicationof(T,s,S)strategymodel

According to thematchingmodel of inventory control strategy, it canbe seen that (T,s,S)strategyissuitableforinventorycontrolofleveragedmaterials.ThispapertakesMaterial2(pe‐troleumspecialpipe)asanexampletoapply(T,s,S)strategymodel.AccordingtothepersonnelexperienceofinventorymanagementofMaterial2,theinspectioncycleisgenerallysetforeightweeks; thus,T=8, the leadtimeis twoweeks, that isp=2; theservice level is95%,andtheminimumpackingnumberis2tons.Fromthecommoncustomerservicelevelandsafetyfactortable(Table6),thesafetyfactork=1.65.Accordingtothenormaldistributionresultsofenter‐prisedemandprediction,μ 18,σ 7.Therefore,

Orderpoint 18 10 1.65 7 10 295.5tons;

Maximuminventorylevel 295.5 18 10 475.5tons.

Iftheinventorylevelafterinspectionis280tons,lessthantheorderpoint295.5tons,andtheminimumpackagequantityis2tons,then:

Orderquantity 475.5 280 /2 2 196tons.

Zhang, Wang, Li

5. Conclusion Inventory control is an important issue in supply chain management. There are many attributes of inventory materials in enterprises, and the degree of influence of different materials on en-terprises is also different. Faced with the new production and delivery, the manner of scientifi-cally classifying the materials of the enterprises and making scientific inventory control strategy are of great practical significance for effectively reducing the operating costs of enterprises, im-proving the ability of material support, and further promoting the development, transformation and upgrading of the enterprises.

In this paper, the classification and inventory control strategies of multi-attribute materials were systematically studied. Firstly, the evaluation index system of material attributes was con-structed from three aspects: procurement risk, proportion of the value and strategic importance. Then, the grey rough set algorithm was used to reduce the attribute of the material attribute index to achieve the aim of removing repetitive and redundant attributes. On this basis, the dis-criminate model of material classification was constructed based on the PNN approach. It is simple and practical; it has a fast training speed and a good output effect on network simulation, which can solve the material classification problem well. Then, based on the classification re-sults, different inventory control strategy models for strategic materials, bottleneck materials, general materials and leveraged materials were proposed. That is to say, an inventory control strategy matching model based on material classification was built to provide a powerful basis for enterprises to formulate targeted inventory control strategies. Finally, taking a chemical en-terprise (i.e., Enterprise A) as an example, using the classification approach, inventory control strategy and the corresponding model proposed in this paper, the material classification scheme of Enterprise A was obtained, and the order schemes under different inventory control strate-gies were obtained by calculation. The results strongly illustrate the feasibility and validity of the model and the method built in this paper.

The limitation of this paper is that only static inventory control strategy is considered. How-ever, the improvement of other inventory control strategies, such as the dynamic inventory con-trol strategy and the static-dynamic inventory control strategy need to be further studied.

Acknowledgement This research was partially funded by the National Science Foundation of China (71373039) and the Ministry of Edu-cation’s Program for New Century Excellent Talents (NCET-13-0712).

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