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Suzana de França Dantas Daher
                     Adiel Teixeira de Almeida

  Group preference aggregation
based on ELECTRE methods for ERP
         system selection
Agenda
 Introduction / Motivation
 Model Proposed
 Numerical application
 Final Remarks
Motivation
   Enterprise resource planning (ERP) improves
    operational efficiency by integrating business
    processes and providing better access to
    integrated data across the entire enterprise

   Deciding which is the most suitable ERP solution is
    often a difficult task for many companies.
      ERP not developed in-house
Motivation
   Organizations select and implement ERP systems
    so as to obtain a variety of tangible and intangible
    benefits and for strategic reasons.

   The evaluation process of ERP systems needs to
    take many criteria into account
       organizational factors such as the complexity of the
        business;
       dealing with change management,
       cost drivers,
       its functional requirements,
       system flexibility and system scalability,
       external factors
Motivation
   There is a strong possibility that in several
    organizations, an ERP system will be
    selected by a group.

   Multicriteria group decision making involves
    individuals who provide their preferences
    for a set of alternatives with respect to a
    set of attributes
       Diverging opinions may arise
Introduction
   This study deals with how support a group
    of individuals to achieve a collective
    decision when selecting an ERP system.

   The methodology adopted considered that
    DMs act in accordance with their own
    interests and there is no information about
    their relative importance to each other
Introduction
   Which decision making method
    should be used?


    The selection of the most suitable
    decision making method should be
    based on the preference structures of
    the DMs
Additive Model
   For instance, an additive model could be
    considered, such as in
       Daher, S F D ; Almeida, A T (2012) The Use of Ranking Veto Concept
        to Mitigate the Compensatory Effects of Additive Aggregation in Group
        Decisions on a Water Utility Automation Investment. Group Decision
        and Negotiation Journal, v. 21, p. 185-204.

   In this case the additive model is considered for
       aggregating the multiple criteria, and
       Aggregating the decision makers’ preferences
   A veto concept is applied for the additive
    Aggregation of decision makers’ preferences
Outranking methods
   An alternative approach is considered in this
    work:
       Outranking methods
   Other properties are assumed for the decision
    maker preference
Outranking methods
   Outranking methods are particulary suitable
    for decision-making through the notion of
    weak preference and incomparability.

   Outranking relations (S): “at least as good as”
       aSb and not bSa     a P b (a is strictly preferred to
        b)
       bSa and not aSb     b P a (b is strictly preferred to
        a)
       aSb and bSa     a I b (a is indifferent to b)
       not aSb and not bSa     a R b (a is incomparable to
        b)
Outranking methods
   Construction of an outranking relation is
    based on 2 major concepts:
      CONCORDANCE
         Foran outranking aSb to be validated, a
         sufficient majority of criteria should be in
         favor of the assertion “a is at least as good
         as b”.
       NON-DISCORDANCE
         When  the concordance condition holds, none
         of the criteria in the minority should oppose
         too strongly to the assertion aSb.
ELECTRE Family methods
   ELECTRE ( Elimination Et Choix Traduisant la
    Realité / Elimination and Choice Expressing
    the Reality)
   This family seeks to obtain a set of N
    alternatives that outrank those which do not
    belong to the subset N.
   Choice problematic:
       ELECTRE I, ELECTRE Iv, ELECTRE IS;
   Ranking problematic:
       ELECTRE II, ELECTRE III, ELECTRE IV
   Sorting problematic:
       ELECTRE A, ELECTRE TRI
Model Proposed
   Based on a combination of two outranking
    methods: ELECTRE II and ELECTRE IV.

   Assumptions:
       The decision problem is well structured
       Prior definition of a set n alternatives
       Prior definition of a set k criteria


   The model is organized in three steps
          Alencar, L.H., Almeida, A. T., Morais, D. C.: A Multicriteria Group Decision Model
             Aggregating the Preferences of Decision-Makers Based on ELECTRE Methods.
                                          Pesquisa Operacional 30, Issue 3, 687-702 (2010)
Model Proposed
Individual ranking of alternatives (ELECTRE II)



        Matrix of global evaluation



 Group ranking of alternatives (ELECTRE IV)
Model Proposed
First step
   ELECTRE II
       Generation of individual rankings of alternatives


   For each Decision Maker:
       Decision Matrix D
       Concordance and discordance indices
       Criteria weights (inter-criteria information)
Model Proposed
First step
   A concordance index C(a,b):
       represents the coalition of arguments in favor
        of the statement “a is at least as good as b ” or
        in other words “a outranks b ” C (a, b)        wj
                                                            { j:g j ( a ) g j (b )}


   A discordance index D(a,b) :
       is used to measure the arguments that may
        cast some doubt upon the latter statement.
             D ( a, b)         max            {g j (b) g j (a)}
                         j: g j ( a ) g j ( b )

       No veto condition: g j (a) v( g j (a)) g j (b), j J
Model Proposed
First step
   These indices are used to construct two
    pre-orders:
       strong outranking relation (a SS b)
       weak outranking relation (a Sw b)
                                          C ( a, b)          c
                                          D ( a, b)          d
                    S     iff
              aS b                             wj
                                 j: gj ( a ) gj (b )
                                                                       wj
                                                        j:gj ( a ) gj ( b )



                                         C ( a, b) c
                                         D ( a, b) d
               aSW b       iff                wj                    wj
                                 j: gj ( a ) gj (b )   j: gj ( a ) gj ( b )
Model Proposed
Second step
   Obtain a matrix of global evaluation

   Analyst must collect all individual ranking
    and compile them in a global evaluation
    matrix.

   DMs are considered as criteria and their
    rankings correpond to the evaluation of the
    alternatives (ranking position of the
    alternative)
Model Proposed
Third step
   ELECTRE IV
       Obtain a group ranking


   ELECTRE IV is used in cases in which there
    is a pseudo-criterion family and its main
    feature is the absence of a weighting
    related to the relative importance of the
    criteria
Numerical application
   Fictitious case study: ERP selection for a
    Brazilian airlines

   The company has to deal with inefficient
    operational procedures and an IT/IS legacy
    system.

   In order to improve its competitiveness,
    the company launch of several projects
    including, an ERP system and the
    reengineering of some business processes
Numerical application
   Four decision makers:
     the   financial manager (DM1)
     the   IT/IS manager (DM2)
     the   operational manager (DM3)
     the   customer relation manager (DM4)


   Analyst should conduct the decision
    making process.

   Number of alternatives: 4
Numerical application
   Criteria selection
   Based on: ISO/IEC 9126-1
       a standard that addresses quality model
        definition and its use as framework for software
        evaluation


   Group of criteria:
       functional, portability, maintainability,
        efficiency, vendor, cost
Numerical application
Criteria adopted
       Criteria                          Criteria group
 C1    Completeness                      Functional

 C2    Number of simultaneous users      Functional
 C3    DBMS Standards                    Portability
 C4    Number of modules                 Maintainability
 C5    Time behavior                     Efficiency

 C6    Length of experience              Vendor
 C7    License cost                      Cost
       Installation and implementation
 C8                                      Cost
      cost
Numerical application
    Decision matrix
         C1   C2      C3   C4      C5      C6   C7    C8
A1       4    5000    4    8       0.3     4    0.7   1.8

A2       5    3000    5    12      0.6     5    0.5   1.3

A3       3    4500    4    10      0.2     4    0.6   1.7

A4       5    4000    3    5       0.7     5    0.4   2.0

    Concordance and Discordance coefficients
                     c+    c-    d+      d-
              DM1    0.8   0.6   0.4     0.5
              DM2    0.7   0.5   0.3     0.4
              DM3    0.8   0.5   0.3     0.4
              DM4    0.9   0.6   0.3     0.5
Numerical application
          Criteria weights
      C1      C2          C3        C4       C5       C6          C7      C8
DM1   0.175   0.175       0.082     0.221    0.043    0.117       0.093   0.094
DM2   0.081   0.101       0.086     0.333    0.005    0.081       0.188   0.125
DM3   0.102   0.004       0.005     0.200    0.136    0.149       0.370   0.034
DM4   0.035   0.035       0.198     0.167    0.056    0.232       0.211   0.066



          Table of preorders for each DM
                Ranking       DM1      DM2    DM3         DM4
                1st           A2       A2     A3,A4       A2
                2nd           A3       A1     A2          A3
                3rd           A1       A3     A1          A1,A4
                4th           A4       A4
Numerical application
   Matrix of global evaluation
                   DM1   DM2         DM3   DM4

        A1         2     3           1     1
        A2         4     4           2     3
        A3         3     2           3     2
        A4         1     1           3     1



   Global ranking of alternatives
             Ranking     Alternatives

             1st         A2
             2nd         A1, A3,A4
Final remarks
   In general, decisions made in organizations
    involve a group of people, from different
    departments or sectors
       Analyst should guarantee that client’s interest
        are as well represented as possible.


   An approach to support a group of decision
    makers to select na ERP system
   Different results could appear if ELECTRE
    IV be changed to another method such as
    Borda Count or Condorcet.
Thank you.
                               Suzana Daher
             Federal University of Pernambuco
                                         Brazil
                           sfdd@uol.com.br
                  suzanadaher@gmail.com

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Group preference aggregation based on ELECTRE methods for ERP system selection

  • 1. Suzana de França Dantas Daher Adiel Teixeira de Almeida Group preference aggregation based on ELECTRE methods for ERP system selection
  • 2. Agenda  Introduction / Motivation  Model Proposed  Numerical application  Final Remarks
  • 3. Motivation  Enterprise resource planning (ERP) improves operational efficiency by integrating business processes and providing better access to integrated data across the entire enterprise  Deciding which is the most suitable ERP solution is often a difficult task for many companies.  ERP not developed in-house
  • 4. Motivation  Organizations select and implement ERP systems so as to obtain a variety of tangible and intangible benefits and for strategic reasons.  The evaluation process of ERP systems needs to take many criteria into account  organizational factors such as the complexity of the business;  dealing with change management,  cost drivers,  its functional requirements,  system flexibility and system scalability,  external factors
  • 5. Motivation  There is a strong possibility that in several organizations, an ERP system will be selected by a group.  Multicriteria group decision making involves individuals who provide their preferences for a set of alternatives with respect to a set of attributes  Diverging opinions may arise
  • 6. Introduction  This study deals with how support a group of individuals to achieve a collective decision when selecting an ERP system.  The methodology adopted considered that DMs act in accordance with their own interests and there is no information about their relative importance to each other
  • 7. Introduction  Which decision making method should be used? The selection of the most suitable decision making method should be based on the preference structures of the DMs
  • 8. Additive Model  For instance, an additive model could be considered, such as in  Daher, S F D ; Almeida, A T (2012) The Use of Ranking Veto Concept to Mitigate the Compensatory Effects of Additive Aggregation in Group Decisions on a Water Utility Automation Investment. Group Decision and Negotiation Journal, v. 21, p. 185-204.  In this case the additive model is considered for  aggregating the multiple criteria, and  Aggregating the decision makers’ preferences  A veto concept is applied for the additive Aggregation of decision makers’ preferences
  • 9. Outranking methods  An alternative approach is considered in this work:  Outranking methods  Other properties are assumed for the decision maker preference
  • 10. Outranking methods  Outranking methods are particulary suitable for decision-making through the notion of weak preference and incomparability.  Outranking relations (S): “at least as good as”  aSb and not bSa a P b (a is strictly preferred to b)  bSa and not aSb b P a (b is strictly preferred to a)  aSb and bSa a I b (a is indifferent to b)  not aSb and not bSa a R b (a is incomparable to b)
  • 11. Outranking methods  Construction of an outranking relation is based on 2 major concepts:  CONCORDANCE  Foran outranking aSb to be validated, a sufficient majority of criteria should be in favor of the assertion “a is at least as good as b”.  NON-DISCORDANCE  When the concordance condition holds, none of the criteria in the minority should oppose too strongly to the assertion aSb.
  • 12. ELECTRE Family methods  ELECTRE ( Elimination Et Choix Traduisant la Realité / Elimination and Choice Expressing the Reality)  This family seeks to obtain a set of N alternatives that outrank those which do not belong to the subset N.  Choice problematic:  ELECTRE I, ELECTRE Iv, ELECTRE IS;  Ranking problematic:  ELECTRE II, ELECTRE III, ELECTRE IV  Sorting problematic:  ELECTRE A, ELECTRE TRI
  • 13. Model Proposed  Based on a combination of two outranking methods: ELECTRE II and ELECTRE IV.  Assumptions:  The decision problem is well structured  Prior definition of a set n alternatives  Prior definition of a set k criteria  The model is organized in three steps Alencar, L.H., Almeida, A. T., Morais, D. C.: A Multicriteria Group Decision Model Aggregating the Preferences of Decision-Makers Based on ELECTRE Methods. Pesquisa Operacional 30, Issue 3, 687-702 (2010)
  • 14. Model Proposed Individual ranking of alternatives (ELECTRE II) Matrix of global evaluation Group ranking of alternatives (ELECTRE IV)
  • 15. Model Proposed First step  ELECTRE II  Generation of individual rankings of alternatives  For each Decision Maker:  Decision Matrix D  Concordance and discordance indices  Criteria weights (inter-criteria information)
  • 16. Model Proposed First step  A concordance index C(a,b):  represents the coalition of arguments in favor of the statement “a is at least as good as b ” or in other words “a outranks b ” C (a, b) wj { j:g j ( a ) g j (b )}  A discordance index D(a,b) :  is used to measure the arguments that may cast some doubt upon the latter statement. D ( a, b) max {g j (b) g j (a)} j: g j ( a ) g j ( b )  No veto condition: g j (a) v( g j (a)) g j (b), j J
  • 17. Model Proposed First step  These indices are used to construct two pre-orders:  strong outranking relation (a SS b)  weak outranking relation (a Sw b) C ( a, b) c D ( a, b) d S iff aS b wj j: gj ( a ) gj (b ) wj j:gj ( a ) gj ( b ) C ( a, b) c D ( a, b) d aSW b iff wj wj j: gj ( a ) gj (b ) j: gj ( a ) gj ( b )
  • 18. Model Proposed Second step  Obtain a matrix of global evaluation  Analyst must collect all individual ranking and compile them in a global evaluation matrix.  DMs are considered as criteria and their rankings correpond to the evaluation of the alternatives (ranking position of the alternative)
  • 19. Model Proposed Third step  ELECTRE IV  Obtain a group ranking  ELECTRE IV is used in cases in which there is a pseudo-criterion family and its main feature is the absence of a weighting related to the relative importance of the criteria
  • 20. Numerical application  Fictitious case study: ERP selection for a Brazilian airlines  The company has to deal with inefficient operational procedures and an IT/IS legacy system.  In order to improve its competitiveness, the company launch of several projects including, an ERP system and the reengineering of some business processes
  • 21. Numerical application  Four decision makers:  the financial manager (DM1)  the IT/IS manager (DM2)  the operational manager (DM3)  the customer relation manager (DM4)  Analyst should conduct the decision making process.  Number of alternatives: 4
  • 22. Numerical application  Criteria selection  Based on: ISO/IEC 9126-1  a standard that addresses quality model definition and its use as framework for software evaluation  Group of criteria:  functional, portability, maintainability, efficiency, vendor, cost
  • 23. Numerical application Criteria adopted Criteria Criteria group C1 Completeness Functional C2 Number of simultaneous users Functional C3 DBMS Standards Portability C4 Number of modules Maintainability C5 Time behavior Efficiency C6 Length of experience Vendor C7 License cost Cost Installation and implementation C8 Cost cost
  • 24. Numerical application  Decision matrix C1 C2 C3 C4 C5 C6 C7 C8 A1 4 5000 4 8 0.3 4 0.7 1.8 A2 5 3000 5 12 0.6 5 0.5 1.3 A3 3 4500 4 10 0.2 4 0.6 1.7 A4 5 4000 3 5 0.7 5 0.4 2.0  Concordance and Discordance coefficients c+ c- d+ d- DM1 0.8 0.6 0.4 0.5 DM2 0.7 0.5 0.3 0.4 DM3 0.8 0.5 0.3 0.4 DM4 0.9 0.6 0.3 0.5
  • 25. Numerical application  Criteria weights C1 C2 C3 C4 C5 C6 C7 C8 DM1 0.175 0.175 0.082 0.221 0.043 0.117 0.093 0.094 DM2 0.081 0.101 0.086 0.333 0.005 0.081 0.188 0.125 DM3 0.102 0.004 0.005 0.200 0.136 0.149 0.370 0.034 DM4 0.035 0.035 0.198 0.167 0.056 0.232 0.211 0.066  Table of preorders for each DM Ranking DM1 DM2 DM3 DM4 1st A2 A2 A3,A4 A2 2nd A3 A1 A2 A3 3rd A1 A3 A1 A1,A4 4th A4 A4
  • 26. Numerical application  Matrix of global evaluation DM1 DM2 DM3 DM4 A1 2 3 1 1 A2 4 4 2 3 A3 3 2 3 2 A4 1 1 3 1  Global ranking of alternatives Ranking Alternatives 1st A2 2nd A1, A3,A4
  • 27. Final remarks  In general, decisions made in organizations involve a group of people, from different departments or sectors  Analyst should guarantee that client’s interest are as well represented as possible.  An approach to support a group of decision makers to select na ERP system  Different results could appear if ELECTRE IV be changed to another method such as Borda Count or Condorcet.
  • 28. Thank you. Suzana Daher Federal University of Pernambuco Brazil sfdd@uol.com.br suzanadaher@gmail.com