A Multi-agent Infrastructure for Enhancing ERP system Intelligence

A MULTI-AGENT INFRASTRUCTURE FOR ENHANCINGERP SYSTEM

INTELLIGENCE

ANDREAS L. SYMEONIDIS

†‡∗

, KYRIAKOS C. CHATZIDIMITRIOU

†

, DIONYSIOS KEHAGIAS

‡

, AND PERICLES

A. MITKAS

†‡

Abstrat. EnterpriseResourePlanningsystemseientlyadministeralltasksonerningreal-timeplanningand

manufa-turing, materialprourementand inventorymonitoring,ustomer andsuppliermanagement. Nevertheless,the inorporationof

domainknowledgeandtheappliationofadaptivedeisionmakingintosuhsystemsrequireextremeustomizationwithaostthat

beomesunaordable,espeiallyintheaseofSMEs.Inthispaperwepresentanalternativeapproahforinorporatingadaptive

businessintelligeneintotheompany'sbakbone.Wehavedesignedanddevelopedahighlyreongurable,adaptive,osteient

multi-agentframeworkthatatsasanadd-ontoERPsoftware,employingDataMiningandSoftComputingtehniquesinorderto

provideintelligentreommendationsonustomer,supplierandinventorymanagement. Inthispaper,wepresentthearhiteture

anddevelopmentdetailsofthedevelopedframework,anddemonstrateitsappliationonarealtestase.

Keywords. ERPsystems,DataMining,SoftComputing,Multi-AgentSystems,AdaptiveDeisionMaking

1. Introdution. Enterprise ResourePlanning (ERP)systemsarebusiness managementtoolsthat

au-tomate and integrate all ompany faets, inluding real-time planning, manufaturing, sales, and marketing.

These proessesproduelargeamountsof enterprise data that are,in turn, usedby managersandemployees

tohandle allsorts ofbusiness taskssuhasinventoryontrol,ordertraking,ustomer servie,naningand

humanresoures[16℄.

Despite the support urrent ERP systems provide on proess oordination and data organization, most

ofthem espeially legaysystemslakadvaned Deision-Support(DS) apabilities, resultingthereforein

dereasedompanyompetitiveness. Inaddition,fromafuntionalityperspetive,mostERPsystemsarelimited

tomeretransationalITsystems,apableofaquiring,proessing,andommuniatingraworunsophistiated

proesseddataontheompany'spastand presentsupplyhain operations[25℄. Inordertooptimizebusiness

proessesinthetatialsupplyhainmanagementlevel,theneedforanalytialITsystemsthatwillworkinlose

ooperationwiththealreadyinstalledERPsystemshasalreadybeenidentied,andDS-enabledsystemsstand

outas the most suessful gatewaytowardsthe development of more eient and more protable solutions.

Probingevenfurther,Davenport[7℄suggeststhatdeision-makingapabilitiesshouldatasanextensionofthe

humanability toproessknowledgeandproposestheuniation ofknowledgemanagementsystemswith the

lassialtransation-basedsystems,whileCarlssonand Turban [3℄laimthat theintegrationof smartadd-on

modules tothe alreadyestablishedERPsystemsould makestandardsoftwaremoreeetiveandprodutive

fortheend-users.

ThebenetsofinorporatingsuhsophistiatedDS-enabledsystemsinsidetheompany'sITinfrastruture

areanalyzedbyHolsappleandSenna[14℄. Themostsigniant,among others,are:

1. Enhanementofthedeisionmaker'sabilitytoproessknowledge.

2. Improvementofreliabilityofthedeisionsupportproesses.

3. Provisionofevidene insupportofadeision.

4. Improvementorsustainabilityoforganizationalompetitiveness.

5. Redutionofeortandtimeassoiatedwithdeision-making,and

6. Augmentationofthedeisionmakers'abilitiesto taklelarge-sale,omplexproblems.

WithintheontextofSmallandMediumsizedEnterprises(SMEs)however,applyinganalytialand

math-ematialmethods asthe meansforoptimization of thesupply hain managementtasks ishighly impratial,

beingbothmoneyandtimeonsuming[5,31℄. Thisiswhyalternativetehnologies,suhasDataMiningand

AgentTehnologyhavealreadybeenemployed,inordertoprovideeientDS-enabledsolutions. Theinreased

exibilityof multi-agentappliations, whihprovidemultiple loi ofontrol [30℄ anleadto lessdevelopment

eort,while theooperationprimitivesthatAgentTehnologyadopts pointtoMASasthebest hoiefor

ad-dressingomplextasksinsystemsthatrequiresynergyofmultipleentities. Moreover,DMhasrepeatedlybeen

∗

Correspondingauthor

†

EletrialandComputerEngineeringDept.,AristotleUniversityofThessaloniki,GR54124,Thessaloniki,GREECE

‡

IntelligentSystemsandSoftwareEngineeringLaboratory,InformatisandTelematisInstitute-CERTH,GR570

usedforMarket TrendAnalysis,UserSegmentation,andForeasting. Knowledgederivedfromtheappliation

of DM tehniques onexisting ERP historial data anprovidemanagerswith useful information, whih may

enhanetheirdeision-makingapabilities.

Going briey through related work, we see that DM and MAS have been used separately for eient

enterprisemanagementanddeisionsupport. Rygielskiet. al. [24℄haveexploitedDMtehniquesforCustomer

RelationshipManagement(CRM),whileChoyet. al. [4,5℄haveusedahybridmahine learningmethodology

for performing Supplier Relationship Management (SRM). On the other hand, MAS integrated with ERP

systemshavebeenused forprodution planning[22℄, and fortheidentiation andmaintenaneof oversights

andmalfuntionsinsidetheERPsystems[15℄.

Elaborating on previous work, we have integrated AT and DM advantages into a versatile and adaptive

multi-agentsystemthatatsasanadd-ontoestablishedERPsystems. OurapproahemploysSoftComputing,

DM,ExpertSystems,standardSupplyChainManagement(SCM)andATprimitives,inordertoprovide

intel-ligentreommendationsonustomer,supplier,andinventoryissues. Thesystemisdesignatedtoassistnotonly

themanagersofaompanyManagingbywireapproah[12℄,butalsothelower-level,distributeddeision

makers Cowboys approah [18℄. Our framework utilizes the vast amount of orporate data stored inside

ERPsystemstoprodueknowledge,byapplyingdataminingtehniquesonthem. Theextratedknowledgeis

diused to all interestedparties viathe multi-agentarhiteture, while domain knowledge and business rules

are inorporated into the system by the use of rule-based agents. It merges the, already proven apabilities

ofdataminingwith theadvantagesofmulti-agentsystemsin termsofautonomyand exibility, andtherefore

promisesagreatlikelihoodofsuess.

Therestofthepaperisorganizedasfollows. Setion2presentstheextensiveReommendationFramework

indetailanddesribesthefuntionalharateristisofthedierenttypesofagentsthatompriseit. Setion3

illustratesthebasifuntionaloperationsofIPRA,analreadydevelopedadd-oninarealenterpriseenvironment.

Finally,Setion4summarizesthework presented,andonludesthispaper.

2. TheIntelligentReommendationFramework. Thearrivalofanewustomerorderdesignatesthe

initialization of theIntelligentReommendation Framework (IRF)operation. All ustomer order preferenes

are, at rst, gatheredbythesystem operatorviaa front-endagentand arethen transferredto the bakbone

(order)agentsforproessing. Theorderproessingagentsareofdierenttypes,eahonerelatedtoaspei

entityofthesupplyhain(ompany,ustomers,suppliers,produts),andmanageentity-speidata. Inorder

to establish onnetivity to the ERP system's database and aess ERP data, another agent has also been

implemented. BytheuseofDMtehniques,allrelatedentities'prolesareonstrutedforthereommendation

proedure to bebased on. When all proesses are nalized, the front-end agent returnsto the operator the

intelligentreommendationsprodued by theframework,along withanexplanatorymemo. These

reommen-dationsarenotdesignedtosubstitutethehumanoperator,rathertoaidhim/herandtheompanytoinrease

protandeientlymanageustomerordersandompanysupplies.

2.1. IRFArhiteture. ThegeneralIRFarhitetureisillustratedinFigure2.1. TheIRFagentsbelong

to one of six dierent agent types (

Q

1

−

Q

6

) and are listed in Table 2.1. The main harateristis and the funtionalityof eahtypearedisussedinthefollowingparagraphs.

Table2.1

TheIRFagenttypesandtheirfuntionality

Agenttype Name Funtionality

Q

1

COACustomerOrderAgent GUIagent

Q

2

RAReommendationAgent Organization&DeisionMakingagent

Q

3

CPIACustomerProleIdentiationAgent KnowledgeExtrationagent

Q

4

SPIASupplierProleIdentiationAgent KnowledgeExtrationagent

Q

5

IPIAInventoryProleIdentiationAgent KnowledgeExtrationagent

Q

6

ERPAEnterpriseResourePlanningAgent Interfaeagent

2.1.1. Customer Order Agent type (COA). COA is an interfae agent that may operate at the

distributionpoints,oratthetelephoneenterofanenterprise. COAenablesthesystemoperatorto: a)transfer

Customers

Operator

Customer

Order

Agent

ERP

Agent

XML-SQL

Connector

ERP

Data Source

Layer

Middleware Layer

Information

Processing

Layer

Organization

&

Decision Making

Layer

Graphical User

Interface

Layer

End Users

Customers

Operator

Customers

Operator

Customers

Operator

Supplier

Pattern

Identification

Agent

Inventory

Pattern

Identification

Agent

Customer

Pattern

Identification

Agent

Customer

Order

Agent

Customer

Order

Agent

Customer

Order

Agent

Recommendation

Agent

Fig.2.1. TheIRFarhiteturaldiagram

humanagentwith basifuntionalitiesfor insertinginformation onthe ustomer,the order details (produts

and their orresponding quantities), payment terms (ash, hek, redit et.), bakorder poliies and, nally,

theparty(lientorompany)responsiblefortransportationosts. COAalsoenompassesaunit thatdisplays

informationinvariousforms toexplainandjustifythereommendationsissued bytheRA.

2.1.2. Reommendation Agent type (RA). The RA is responsible for gathering the proles of the

entities involved in the urrent order and for issuing reommendations. By distributing the prole requests

tothe appropriateInformation Proessing Layer agents(CPIA,SPIA andIPIA -eahoneofthem operating

onits ownontrol thread), and byexerisingonurrenyontrol,this agentdiminishes theyle-timeof the

reommendationproess. RAisarule-basedagentimplementedusingtheJavaExpertSystemShell(JESS)[9℄.

StatianddynamibusinessrulesanbeinorporatedintotheRA.Thelattermustbewrittenintoadoument

that the agent an read during its exeution phase. In this way, business rules an be modied on-the-y,

withouttheneedofreompiling,orevenrestartingtheappliation.

2.1.3. CustomerProleIdentiationAgentType(CPIA). CPIAisdesignedtoidentifyustomer

proles,utilizing thehistorialdata maintainedin theERP system. Theproessanbedesribed asfollows:

Initially,managersand appliationdevelopersprodueamodel forgeneratingtheprolesofustomers. They

onthe desiredlassiationofustomers, i.e., added-valueto theompany,disountdue to pasttransations

et. CPIA,bytheuseoflusteringtehniques,analyzesustomerprolesperiodially,andstorestheoutome

of this analysis into a prole repositoryfor posterior retrieval. Whena CPIA isasked to providethe prole

of a ustomer, the urrentattributes of the spei ustomer arerequested from the ERP database and are

mathedagainstthoseintheprolerepository,resultingintotheidentiationofthegroupthespeiustomer

belongsto. Duringthedevelopmentphase,oneormoreCPIA agentsanbeinstantiated,and thedistintion

ofCPIAsintotrainingandreommendationones,resultstoquikerresponsetimeswhenlearningandinferene

proeduresoverlap.

2.1.4. Supplier Pattern Identiation Agent Type (SPIA). SPIA is responsible for identifying

supplierprolesaordingtothehistorialreordsfoundin theERPdatabase. Inasimilar toCPIA manner,

managers identify the keyattributes for determining a supplier'svalue to the ompanyand theirredibility.

SPIA thengeneratessupplierprolesand updates themperiodially. Foreveryrequesteditem in theurrent

order,theRAidentiesoneormorepotentialsuppliersandrequeststheirprolesfromtheSPIA.SPIAhasto

retrievetheurrent reordsofall thesuppliers,identify for eah onethebest math in theprolerepository,

andreturntheorrespondingprolestotheRA.ThenRAanseletthemostappropriatesupplierombination

(aordingto itsruleengine), andreommenditto thehumanoperator. SPIA isalsoresponsibleforfething

to RA information aboutaspei supplier, suh asstatistial data on lead-times,quantities to beproured

et.

2.1.5. Inventory ProleIdentiationAgentType(IPIA). IPIAisresponsibleforidentifying

prod-ut proles. Produt proles omprise raw data from the ERP database (i.e., produt prie, related store,

remaining quantities), unsophistiated proessed data (for example statistial data on produt demand) and

intelligent reommendationson produts (suh asrelated produts that the ustomer may bewilling to

pur-hase). Onemore,managersandappliation developershaveto identify theompanyprioritiesand mapthe

proletothedatamaintainedbytheERP.Besidesthediretlyderiveddata,IPIAisresponsibleforidentifying

buying patterns. Market basket analysis anbeperformed with the help of assoiation rule extration

teh-niques. Sinethisproessis,ingeneral,time-onsuming,twoormoreIPIAsanbeinstantiatedtoseparatethe

reommendationfromthelearningproedure.

2.1.6. Enterprise Resoure Planning Agent Type (ERPA). ERPAs provide the middleware

be-tween the MAS appliation and the ERP system. These agents behave like transduers [11℄, beause they

are responsible for transforming data from heterogeneousappliations into messageformats that agentsan

omprehend. An ERPA handles all queries posted by CPIAs, IPIAs, and SPIAs by onneting to the ERP

database and fething all the requested data. It works in lose ooperation with an XML onnetor whih

relaysXML-SQLqueriestotheERPandreeivesdatainXMLformat. ERPA istheonlyIRFagenttypethat

needstobeonguredproperly,in ordertomeet theonnetionrequirementsofdierentERPsystems.

2.1.7. Tehnologies adopted. IRFhasbeendevelopedwith theuseofAgentAademy(AA)[20,27℄a

platform fordevelopingMAS arhitetures and forenhaningtheir funtionality and intelligenethroughthe

useofDMtehniques. AlltheagentsaredevelopedovertheJavaAgentDevelopmentFramework(JADE)([2℄,

whihonformsto theFIPA speiations[28℄,whiletherequiredontologieshavebeendevelopedthroughthe

Agent Fatory module (AF) ofAA. Data mining hasbeen performed onERP data that are importedto AA

in XML format, and are forwarded to the Data Miner (DM) of AA, a DM suite that expands the Waikato

EnvironmentforKnowledgeAnalysis(WEKA)tool[29℄.

The extrated knowledge strutures are represented in PMML (Preditive Model Markup Language), a

language that eiently desribes lustering, lassiation and assoiation rule knowledge models [6℄. The

resultingknowledgehasbeeninorporatedintotheagentsbytheuseof theAgentTrainingModule (ATM) of

AA.Allneessarydatales(ERPdata,agentbehaviordata,knowledgestrutures,agentontologies)arestored

intoAA's maindatabase,theAgentUseRepository(AUR).Agentsanbeperiodiallyrealledforretraining,

sineappropriateagenttrakingtoolshavebeeninorporatedintoAgentAademy,in orderto monitoragent

ativityafter theirdeployment.

2.2. Installation and RuntimeWorkows. Oneaompanyhoosesto addIRFto itsalready

Business Process Assessment

Business Processes Analysis and Mapping

Business Rules Development

Ontology Creation

System Parameter Configuration

XML-SQL Queries Construction

Agent Type Instantiation

System Ready

Reconfiguration

No Reconfiguration

Fig.2.2. InstallingIRFontopofanexistingERP

Atrst,theompany'sbusinessproessexpert,alongwiththeIRFappliationdevelopershavetomakea

detailedanalysisandassessmentoftheurrentustomerorder,inventoryandprodutsprourementproesses.

Theresultsaremappedtothereommendationproessoftheadd-onandtherelevantdatasetsaredelineated

intheERP.

After modeling the reommendationproedure aordingto the needsof the ompany, parallel ativities

for produing required doumentsand templates for the onguration of theMAS appliation follow. Fixed

business rules inorporating ompany poliy are transformed to expert system rules, XML-SQL queries are

builtandstoredintheXMLdoumentsrepository,ontologies(inRDFSformat)aredevelopedforthemessages

exhanged and for the deision on the workow of the agents, agent types instantiation requirements are

dened(atdierentworkstationsandardinalities)andotheradditionalparametersareongured(i.e.,simple

retraining time-thresholds, parameters for the data-mining algorithms, suh as support and ondene for

marketbasketanalysiset).

Onebootstrapped,reongurationofthesystemparametersisquiteeasy,sineallrelatedparametersare

doumentsthat anbeonveniently re-engineered. Figure2.3 illustrates theworkowof theSPIA, where all

thetasksdesribedearlierin thissetion,anbedeteted. InaseIRFneedstobemodiedduetoahangein

theompanyproesses,thereongurationpathmustbetraversed. TheIPIAandCPIAworkowsaresimilar

and,thus,theyareomitted.

2.3. SystemIntelligene.

2.3.1. Benhmarkingustomerandsuppliers. Inordertoperformustomerandsuppliersegregation,

CPIAandSPIAuseahybridapproahthatombinesdataminingandsoftomputingmethodologies. Clustering

tehniquesand fuzzyinferening areadopted,in order todeideon ustomerand supplierquality. Initially,

the humanexperts selet the attributes on whih the prole extration proedures will be based on. These

attributes an either be soio-demographi, managerial or nanial data, deterministi or probabilisti. We

representthedeterministiattributes,whiharediretlyextratedfromtheERPdatabasebyERPA,as

Det

i

,

i

= 1

, ...n

,where

n

istheardinalityoftheseleteddeterministiattributes. Ontheotherhand,werepresent

the average (

AV G

) and standarddeviation values(

ST D

) of probabilisti variables, whih are alulated by

COA sends order preferences to RA

RA requests order profiles

CPIA recieves request for customer's profiles

SPIA recieves request for suppliers' profiles

RA applies fixed business policies to profiles

COA communicates order to MAS operator

Query ERPA for all supplier data

Preprocess

Maximin

Kmeans

Characterize clusters through fuzzy inference

Query order specific supplier data

Preprocess

Match current with stored profiles

Send profiles to RA

[Profiles exist]

IPIA recieves request for products' profiles

Pre-specified time-window

for analysis has exceeded

Fig.2.3. TheWorkowofSPIA

Eahustomer/supplieristhusrepresentedbyatuple:

< Det

1

, ..., Det

n

, AV G

1

, ST D

1

, ..., AV G

m

, ST D

m

)

>

(2.1)

where

i

= 1

..n

,

j

= 1

..m

,

i

+

j >

0

.

Sinereal-worlddatabasesontainmissing,unknownand erroneousdata

[13℄,ERPApreproessesdatapriortosendingtheorrespondingdatasets totheInformation ProessingLayer

Agents. Typialpreproessingtasksaretupleomissionandllingofmissingvalues.

AfterthedatasetshavebeenpreproessedbyERPA,theyareforwardedtoCPIAandSPIA.Clusteringis

performedin order to separateustomers/suppliers into distintgroups. The Maximinalgorithm [17℄ is used

toprovidethenumberoftheenters

K

that areformulatedbytheappliation oftheK-meansalgorithm [19℄.

Thisway

K

disjointustomer/supplierlusters arereated.

Inordertodeideonustomer/supplierlusters' added-value,CPIA andSPIAemployanAdaptiveFuzzy

Logi InfereneEngine (AFLIE), whih haraterizesthealreadyreatedlusters withrespetto anoutome

TheattributesoftheresultinglustersaretheinputstoAFLIEandtheymayhavepositive(

ր

)ornegative (

ց

)preferredtendenies,dependingontheirbeneiaryorharmfulimpatonompanyrevenue. Onedomain knowledgeis introduedto AFLIEin theform ofpreferredtendenies anddesiredoutputs, theattributes are

fuzziedaordingtoTable2.2.

Table2.2

FuzzyvariabledenitionandInterestingnessofdatasetattributes

Variable FuzzyTuple

Input PreferredTendeny

Det

i

ր

h

Det

i

,

[

LOW, M EDIU M, HIGH

]

,

[

Det

i

1

, Det

i

2

]

, T riangular

i

Det

i

ց

h

Det

i

,

[

LOW, M EDIU M, HIGH

]

,

[

Det

i

1

, Det

i

2

]

, T riangular

i

AV G

j

ր

h

AV Gj,

[

LOW, M EDIU M, HIGH

]

,

[

AV G

j

1

, AV G

j

2

]

, T riangular

i

AV G

j

ց

h

AV Gj,

[

LOW, M EDIU M, HIGH

]

,

[

AV G

j

1

, AV G

j

2

]

, T riangular

i

Output ValueRange

Y

Variesfrom

Y

1

to

Y

2

withastepof

x

h

Y,

[#(

Y

2

−

Y

1

)

/x

InrementalFuzzyValues

]

,

[

Y

1

, Y

2

]

, T riangular

i

Theprobabilistivariablesarehandledin anadaptivewayandareused asinputsonlywhenChebyshev's

inequality(Eq. 2.2)issatised[21℄:

P

{|

P

j

−

AV G

j

|

ǫ

} ≤

(

ST D

j

)

2

ǫ

2

,

for any

ǫ>

0

(2.2)

Eq. 2.2ensurestheonentrationofprobabilistivariablesneartheirmeanvalue,intheinterval

(

AV G

j

−

ǫ, AV G

j

+

ǫ

)

. Noattributeswithhighdistributionaretakenasinputstothenalinfereneproedure,avoiding

thereforedeisionpolarization.

Theformulationoftheinputs(3values:

[

LOW, M EDIU M, HIGH

]

)leadsto3

ν

FuzzyRules (

F R

),where

ν

isthenumberofAFLIEinputs.

F Rs

areoftype:

If

X

1

is

LX

1

(

k

)

and

X

2

is

LX

2

(

k

)

and...and

X

n

is

LX

n

(

k

)

Then

Y

is

LY

(

l

)

,

k

= 1

..

3

,

l

= 1

..q,

where

q

istheardinalityofthefuzzyvaluesoftheoutput. Triangularmembership funtionsareadopted

foralltheinputsandoutputs,whereasmaximumdefuzziation isusedforrispingthe

F Rs

.

All inputs are assigned aCorresponding Value (

CV

), ranging from

−

1

to

1

, aording to theirompany benetriterion(Table2.2). TheOutput Value (

OV

)of

Y

isthenalulatedforeah

F R

as:

OV

=

X

i=1..n+m

w

i

·

CV

i

(2.3)

where

w

i

istheweightofimportane(0

≤

w

i

≤

1) ofthe

i

th

inputattribute.

The

OV s

aremappedtoFuzzyValues(

F V

),aordingtothedegreeofdisriminationoftheoutputdeision

variables. Byategorizing therangeof the outputinto

q

fuzzy values,the

OV

−→

F V

mappingis basedon thefollowingformula:

F V

(

OV

) =

RN D

OV

·

[

2(

n

+

m

)

q

]

(2.4)

where

RN D

(

x

)

istheroundingfuntionof

x

tothelosestinteger(i.e.,

M EDIU M

for

x

= 3

,

Afteralllustershavebeenharaterized,theorresponding

OV s

,alongwiththelusterenters,arestored

inside a prolerepositoryfor posterior retrieval. This proess signalsthe end of thetraining phaseof CPIA

andSPIA.

Inrealtime,whenaneworderomesintothesystem,RArequeststheorrespondingustomerproleand

the proles of the suppliersthat are related to the ordered produts. CPIA and SPIA request, in turn, the

attributesoftheseentitiesfromERPA,andmaththemagainsttheprolesstoredinsidetheprolerepository,

by theuse of theAssigned Cluster(

AC

)riterion.

AC

is a loseness-to-luster-enter funtion, given by the

followingequation:

AC

= min

i=1..k

{

v

u

u

t

n+m

X

i=1

(

c

i

−

xc

ji

)

}

(2.5)

where

k

isthenumberoflusters,

n

thenumberofattributes,

c

i

isthe

i

th

attributevalueofthelusterenter

vetor

c

= (

c

1

, c

2

, ..., c

n

)

, and

xc

ij

the

i

th

attributevalue ofthe

j

th

urrentvetor

xc

j

= (

xc

j1

, xc

j2

, ..., xc

jn

)

.

Thewinninglusteralongwithits

OV

isreturnedtoRA.

2.3.2. IPIAproduts prole. TheIPIAplaysadualrolein thesystem:

1. It fethes information on prie,stok, statistial data aboutdemand faedby the ordered produts,

and

2. It provides reommendations on additional items to buy, based on assoiation rule extration

teh-niques.

Inorderto provideadaptivereommendationsonorderinghabits, IPIAinorporatesknowledgeextrated

by the Apriori algorithm ([1, 10℄. The assoiationrules extrated are stored inside theprole repository for

laterretrieval.

Speialattentionshouldbedrawntothefatthatthetransationsinludedintothedatasettobeminedmay

spanseveraldierentustomerorderperiods. XML-SQLqueriesanbeadaptedtoperformdataminingeither

to the whole datasetorthe datasets of spei periods. Thus, IPIA ishighly adaptable, both for ompanies

in the general merhandize domain, but also for ompanies that sellseasonal goods (forexample toys). The

reommendationsofIPIA,aswellastheinformationonerningstokavailabilityandprie,aresenttotheRA.

2.3.3. RA Intelligene. As mentioned earlier, RA is an expert agent that inorporates xed business

poliiesappliedtoustomers,inventories,andsuppliers. Theserulesarerelated,notonlytorawdataretrieved

from the ERP database and order preferenes provided by ustomers, but also to the extrated knowledge

providedbytheInformationProessingagents. TherearethreedistintruletypesthatRAanrealize:

1. Simple

h

If . . . T hen . . .

i

statements,

2. Rulesdesribingmathematialformulas,and

3. Rulesprovidingsolutionstosearhproblemsandonstraintsatisfationproblems.

Anexampleisprovidedbelowforeahoneoftheseruletypes:

Example1: SimpleRules

Additionaldisountsorburdenstothetotalprieofanorderanbeimplementedbytheuseofsimplerules

(knowledgeextratedisdenotedin bold):

1. IF

(

T otalOrderRevenue >

= 100)

AND

(

CustomerValue

=

LOW

)

THEN

T otalDiscount

+ = 5%;

2. IF

(

CustomerValue

=

LOW

)

THEN

T otalDiscount

−

= 5%;

3. IF

(

P roductT ype

=

ChristmasP roducts

)

AND

(

T otalQuantity >

= 100)

THEN

ProductDiscount

+ = 10%;

4. IF

(

RecommendedProductsPurchased

=

T rue

)

THEN

P roductDiscount

+ = 5%;

Example2: MathematialFormulas

There-order/order-up-to-level-pointmetri(

sS

)provideseientinventorymanagementforeitherno-xed

ost orders orxed ost orders [16℄. In the aseof no-xed ost orders (where

s

=

S

), thereorder point is

alulatedas:

sS

=

AV GD

·

AV GL

+

z

·

p

AV GL

·

ST DD

2

₊

_{AV GD}

2

_·

_{ST DL}

2

(2.6)

where

z

is aonstanthosenfrom statistial tables to ensure thesatisfation of apre-speiedvalue for the

ompany'sservielevel. Table2.3illustratesthevalueof

z

inorrelationwiththedesiredservielevel. Inmost

legayERPsystemssuh attributeshavetobeprovidedbyusersandannotbederivedautomatially.

Table2.3

ServieLevelandorrespondingzValue

ServieLevel 90% 91% 92% 93% 94% 95% 96% 97% 98% 99% 99.9%

z

1.29 1.34 1.41 1.48 1.56 1.65 1.75 1.88 2.05 2.33 3.08

Ordered quantity

LOB

UOB

0%

100%

Do not split order and fulfill order later

Split order and fulfill a part now and the rest later

Do not split order and fulfill order now

Fig.2.4. RAordersplittingpoliy

(b)Splitting Poliy

Asplittingpoliy isappliedwhenompanystokavailabilityannotsatisfyorderneeds. Uponarrivalofa

neworder,thequantityof ordereditemsandavailable stokareross-heked. Iftherequestedquantities are

available,theorder isfullled immediately. Otherwise,the nalsupplyingpoliy that theRAreommends is

setaordingto theshemaillustratedinFigure2.4.

The

LOB

and

U OB

thresholdsdependontheestimatedustomervalue. Inasewehoosetoinorporate

produt disount and ustomer priority into our splitting poliy (for example, ustomers that enjoy better

disountandhaveahigherprioritytohavealower

LOB

andanhigher

U OB

),wemayadjust

LOB

and

U OB

aordingtothefollowingequations:

LOB

=

α

l

·

exp[

−

(

b

pl

p

ˆ

+

b

dl

d

ˆ

)]

(2.7)

U OB

=

α

u

·

exp(

b

pu

p

ˆ

+

b

du

d

ˆ

)

(2.8)

where

p

ˆ

is the priority normalized fator,

d

ˆ

is the disount normalized fator, while the weighting fators

h

α

l

, b

pl

, b

dl

, α

u

, b

pu

, b

du

i

areestimatedin ordertosatisfyminimalrequirementson

LOB

and

U OB

range.

Ifavailablestokisbelow

LOB

%

oftheorderedquantity,theentireorderisputonholduntiltheompany

issuppliedwithadequatequantitiesoftheordereditem. Whenitemavailabilityfallswithinthe

[

LOB

−

U OB

]%

rangeofthe orderedquantity,theorder is split. All available stokisimmediately deliveredtotheustomer,

whereastherestis orderedfromtheappropriatesuppliers. Finally,in asetheavailablestokexeeds

U OB

%

oftheorderedquantity,theorderisimmediatelypreproessedandtheremainingorder perentageisignored.

Example3: ProblemSearhing

(a)Problems thatrequireheuristisappliation and/or onstraintsatisfation

proximitytothedepletedompanystore,ortheidentiationandappliationofanestablishedontrat.

(b)EnhanedCustomerRelationship Management

Using the knowledgeobtained by ustomer lustering, RA an implement avariety of targeted disount

strategiesintheformofrisprules. Thus,theompanyhasadditionalexibilityinitseortstoretainvaluable

ustomersandentienewones withattrativeoers[23℄.

Table2.4

IPRAinputsandoutputs

CPIA

SPIA

IPIA

RA

Input

Preferred

Tendency

Input

Preferred

Tendency

Input

Input

Account

balance

Account balance

Stock

Availability

Ordered Quantity

Credit Limit

Credit Limit

Item price

Stock

Availability

Turnover

Turnover

Supplier ids

Re-order metric

Average Order

Periodicity

Average Order

Completion

Average Item

Turnover (AIT)

for the last two

years

Supplier

Geographic

Location

Standard

deviation of

Order

Periodicity

-

Standard

deviation of

Order

Completion

-

Monthly

Standard

Deviation of

AIT

Lower Order

Break-point

Average Order

Income

Average

Payment Terms

Upper Order

Break-point

Standard

deviation of

Order Income

-

Standard

deviation of

Payment Terms

-

Customer

Geographic

Location

Average

Payment Terms

Supplier

Geographic

Location

Standard

deviation of

Payment Terms

-

Customer

Geographic

Location

IPRA Outputs

Output

Value Range

Output

Value Range

Output

Output

DISCOUNT

Varies from 0

– 30%, using

a step of 5%

SPLITTING

POLICY

ADDITIONAL

DISCOUNT

PRIORITY

Varies from 0

– 3, using a

step of 1

CREDIBILITY

Ranging from

0 – 1, using a

step based on

the number of

supplier

clusters

PROPOSED

ORDER

ITEMS

CUSTOMER

STATISTICS

3. AnIRFDemonstrator. InordertodemonstratetheeienyofIRF,wehavedevelopedIPRA[26℄,

anIntelligentReommendermodule that employsthe methodologypresented in Chapter 5. The systemwas

integrated into the IT environment of a large retailer in the Greek market, hosting an ERP system with a

suiently large data repository. IPRA was slightly ustomized to failitate aess to the existing Orale—

database.

Oursystem proveditself apableof managingover25.000transation reords,resultingin theextration

of truly smart suggestions. The CPIA and the SPIA performed lustering of over 8.000 ustomers (

D

IQ

3

dataset) and 500 suppliers(

D

IQ

14125ustomertransations(

D

IQ

₅

dataset).

AlltheattributesusedbytheInformationProessingagentsasinputsforDM,theirorrespondingpreferred

tendeny,theinputsoftheRAJESS engine,aswellastheoutputsoftheIPRAsystemandtheirvaluerange,

arelistedinTable2.4.

The Information Proessing agents of IPRA, in order to provide RA with valid ustomer and supplier

lusters,aswellasinterestingadditionalorderitems,performedDMontherelevantdatasets. Forthespei

ompany, CPIA and SPIA haveidentied eah vemajor lusters representinganequal numberof ustomer

and suppliergroups,respetively. Resultingustomer (supplier) lusters, aswellasthe disount and priority

(redibility), alulatedbythe CPIA (SPIA) FuzzyInfereneEngine foreah luster,areillustratedin Table

3.1andTable3.2.

Table3.1

TheresultingustomerlustersandtheorrespondingDisountandPriorityvalues

CenterID Population(%) Disount(%) Priority

0 0.002 20 High

1 10.150 10 Medium

2 46.600 15 Medium

3 22.240 10 Medium

4 20.830 5 Low

Table3.2

TheresultingsupplierlustersandtheorrespondingSupplierValuetowardstheompany

CenterID Population(%) Value

0 15.203 Low

1 10.112 Medium

2 25.646 Low

3 34.521 Medium

4 13.518 High

Table3.3

Thegeneratedassoiationruleswiththepredenedsupportandondenethresholds.

GeneratedRules Support Condene

25 2% 90%

10 4% 90%

IPIA,on theother hand,hasextratedanumberofassoiation rulesfromthereordsof previousorders,

As already mentioned, upon reeiving an order, the human agent ollets all the neessary information,

in order toprovideIPRAwith input. Data olletedarehandled by COA, theGUI agentof thesystem. An

instaneoftheGUIisillustratedinFigure3.1.

Fig.3.2.ThenalIPRAReommendation

Allinformationonitemsandquantitiestobeordered,bakorderpoliy,paymentmethod,and

transporta-tionostsaregivenasinputtoIPRA.Whentheorderproessisinitialized,COAforwardstotheCPIA,SPIA,

IPIA and RArespetivelythe alreadyolletedinformation. CPIA heks ontheluster the lientfalls into,

SPIA deides on the best supplier, (aording to his/her added-value), in ase an order has to be plaed to

satisfyustomer demand,IPIAproposesadditionalitemsfortheustomertoorder,andallthese deisionsare

passedontotheRA, whihdeidesonthesplittingpoliy,(if needed)andonadditionaldisount.

Figure3.2illustratesthenalreommendationreated. Detailedinformationontheorderanditsproduts,

ustomer suggested priority and disount, ustomer lusters, supplier suggested value and supplier lusters,

additionalorderitems,suggestedorderpoliyandstatistis,areatthedisposalofthehumanagent,toevaluate

andrealizethetransationat themaximalbenetoftheompany.

4. Conlusions. AnERPsystem,althoughindispensable,onstitutesaostlyinvestmentandtheproess

of updating business rules or adding ustomization modules to it is oftenunaordable, espeially for SMEs.

TheIRFmethodologyaspirestooveromethealreadymentioneddeieniesofnonDS-enabledERPsystems,

in alow-ostyeteientmanner. Knowledgeresidingin aompany'sERPanbeidentied anddynamially

inorporatedintoversatileandadaptableCRM/SRMsolutions. IRFintegratesanumberofenhanementsinto

aonvenientpakageandestablishesanexpedientvehileforprovidingintelligentreommendationstoinoming

ustomerordersandrequestsforquotes. Reommendationsareindependentlyandperpetuallyadapted,without

an adverse impaton IRF run-timeperformane. IRF arhitetureensures reusability and re-ongurability,

withrespettotheunderlyingERP.Table3.4summarizesthekeyenhanementsprovidedbytheaugmentation

ofERPsystemswiththeIRF module.

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Static Business Rules

Yes

Provided as rule documents

changed on the fly.

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Hard-coded by the ERP vendor.

Dynamic Business Rules

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Editedby: MarinPaprzyki,NiranjanSuri

Reeived: Otober1,2006