A Multi-criteria Decision Model For EOL Computers in Reverse Logistics

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A Multi-criteria Decision Model For EOL Computers in Reverse Logistics Powered By Docstoc
					                                                          (IJCSIS) International Journal of Computer Science and Information Security,
                                                          Vol. 9, No. 4, April 2011




 A Multi-Criteria Decision Model for EOL Computers
                 in Reverse Logistics
K.ArunVasantha Geethan                                                              Dr.S.Jose
Department of Mechanical Engineering                                                Loyola-ICAM College of Engineering &
Sathyabama University, Chennai. India                                               Technology ,Chennai. India

R.Devisree                                                                          S.Godwin Barnabas
Cognizant Technology Solutions                                                      St.Joseph’s College of Engineering
Chennai. India                                                                      Chennai. India.



 Abstract- With obsolescence rates on the rise the question as to          affecting the reverse logistics,         some      of    these    are
what the user ultimately does with the end-of-life (EOL) product           interdependent among each other.
becomes an issue that has both environmental and economic
implication. An important concern in EOL management for                    Analytic Network Process (ANP) is a technique that captures
electronic products is to connect the equipment owners with
potential buyers who may be interested in their EOL items,
                                                                           the interdependencies between the criteria under
whether for reuse, component retrieval or material recovery.               consideration, hence allowing for a more systematic analysis
There is an estimate that the total obsolete computers originating         [2]. It can allow inclusion of criteria, both tangible and
from government offices, business houses, industries and                   intangible, which has some bearing on making the best
household is of the order of 2.5 million numbers per year.                 decision. Further, many of these factors have some level of
Manufacturers and assemblers in a single calendar year are                 interdependency among them, thus making ANP modeling
estimated to produce around 1500 tons of electronic scrap. One             better fit for the problem under study. The ANP model
of the important problems faced by the top management in the               presented in this paper structures the problem related to
computer hardware industries is the evaluation of various                  selection of an alternative for the reverse logistics option for
alternatives for EOL computers. The paper aims at linking the
various issues of the reverse logistics in a single systematic
                                                                           EOL computers in a hierarchical form and links the
framework for the selection of an alternative for the reverse              determinants, dimensions and enablers of reverse logistics
logistics operations for EOL computers. The utility of the                 with different alternatives.
Analytic network process (ANP) in integrating both quantitative
as well as the qualitative characteristics and using C++ as the                               II. LITERATURE REVIEW
platform for arriving at the best possible solution proves to be           Stock (1992) recognized the field of reverse logistics as being
more realistic and accurate.
                                                                           relevant for business and society in general. Kopicki, Berg,
Keywords: end-of-life, obsolescence, analytic network program,
alternatives, reverse logistics.
                                                                           Legg, Dasappa, and Maggioni (1993) paid attention to the
                                                                           field and pointed out opportunities on reuse and recycling.
                       I. INTRODUCTION                                     Fleischmann, Bloemhof-Ruwaard, Dekker, van der Laan, van
                                                                           Nunen, and Van Wassenhove (1997) had given a
Reverse logistics is the process of planning, implementing and             comprehensive review of literature of the quantitative models
controlling the efficient, cost effective low of raw materials,            in reverse logistics. Reverse logistics programs in addition to
in-process inventory, finished goods and related information               the various environmental and the cost benefits can
from the point of consumption to the point of origin for the               proactively minimize the threat of government regulation and
purpose of recapturing value or proper disposal [1]. A reverse             can improve the corporate image of the companies (Carter &
logistics defines a supply chain that is redesigned to efficiently         Ellram, 1998). Reverse logistics is the process of planning,
manage the flow of products or parts destined for                          implementing, and controlling the efficient, cost effective flow
remanufacturing, recycling, or disposal and to effectively                 of raw materials, in-process inventory, finished goods and
utilize resource. According to a recent study, reverse logistics           related information from the point of consumption to the point
is one of the twenty one top warehousing trends in the twenty              of origin for the purpose of recapturing value or proper
first century (Brockmann,1999). Industries have started to                 disposal (Rogers & Tibben-Lembke, 1998). A reverse logistics
realize that the reverse logistics can be used to gain                     defines a supply chain that is redesigned to efficiently manage
competitive advantage. An evaluation framework, which                      the flow of products or parts destined for remanufacturing,
incorporates determinants and dimensions of reverse logistics,             recycling, or disposal and to effectively utilize resources
would be useful in configuring the post activities associated              (Dowlatshahi, 2000).
with the EOL computers. There are number of variables




                                                                     157                              http://sites.google.com/site/ijcsis/
                                                                                                      ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 9, No. 4, April 2011


Thus, the reverse logistics focuses on managing flows of                  multi-criteria decision making in the decision making related
material, information, and relationships for value addition as            to reverse logistics practices in the case of EOL computers.
well as for the proper disposal of products. Reverse logistics
has been used in many industries like photocopiers (Krikke,
van Harten, & Schuur, 1999a; Thierry, Salomon, Nunen, &                                        III. PROBLEM DESCRIPTION
Wassenhove, 1995; van der Laan, Dekker, & Van                             Reverse logistics have recently received much attention as
Wassenhove, 1999) single-use cameras (Toktay, Wein, &                     most of the companies are using it as a strategic tool to serve
Stefanos, 2000), jet engine components (Guide & Srivastava                their customers and can generate good revenue. It is a vital
1998), cellular telephones (Jayaraman, Guide, & Srivastava,               part of a successful business in warehousing and distribution.
1999), automotive parts (van der Laan, 1997) and refillable               Recognizing that both the forward and reverse channels of the
containers (Kelle & Silver, 1989). In all the cases, one of the           supply chain can be combined and doing this correctly will
major concerns is to assess whether or not the recovery of                save a significant amount of money for the business.
used products is economically more attractive than the                    The representation of the model and decision environment
disposal of the products [3]. Reverse logistics are also                  clearly shows that the overall objective is to carry out reverse
extensively practiced in the computer hardware industry. IBM              logistics processes for EOL computers. The determinants of
and Dell Computer Corporation have embraced reverse                       reverse logistics (economic factors, legislation, business
logistics by taking steps to streamline the way they deploy old           strategy and customer service initiatives) are modeled to have
systems; and in the process make it easier for the customers to           dominance over the dimensions of reverse logistics. The
refurbish existing computers or buy new parts (Ferguson,                  reverse logistics attribute enablers are those that assist in
2000). Grenchus, Johnson, and McDonell (2001) reported that               achieving the controlling dimension of reverse logistics. Thus
the Global Asset Recovery Services (GARS) organization of                 these are dependent on the dimension. Also, there are some
IBM’s Global Financing division has integrated some of the                interdependencies among the enablers. The reverse logistics
key components of its reverse logistics network to support and            implementation alternatives in this model are the specific
enhance environmental performance. Moyer and Gupta (1997)                 projects or policies that a decision maker wishes to evaluate,
have conducted a comprehensive survey of previous works                   given the various attribute levels of the reverse logistics. The
related to environmentally conscious manufacturing practices,             various alternatives available to the decision maker in this case
recycling, and the complexities of disassembly in the                     include third party reverse logistics, Self Support Logistics
electronics industry. Gungor and Gupta (1999) have presented              and Virtual reverse logistics network.
the development of research in environmentally conscious
manufacturing and product recovery (ECMPRO) and provided                                    REVERSE LOGISTICS OVERALL
a state-of-the-art survey of the published work in this area.                                WEIGHTED INDEX (RLOWI)
Veerakamolmal and Gupta (1997) have discussed a technique                                     CALCULATION FOR BEST
                                                                                                  ALTERNATIVE
for analyzing the design efficiency of electronic products, in
order to study the effect of end-of-life disassembly and
disposal on environment. Nagel and Meyer (1999) discuss a
novel method for systematically modeling end-of-life
networks and show ways of improving the existing and new
systems with ecological and economical concerns. Boon,                                              DETERMINANTS
Isaacs, and Gupta (2002) have investigated the critical factors
influencing the profitability of end-of-life processing of PCs.
They also suggested suitable policies for both PC
manufacturers and legislators to ensure that there is a viable
PC recycling infrastructure. Lambert (2003) presented a state-
of-the-art survey of recently available literature on                                                 DIMENSIONS
disassembly sequencing and the papers closely related to this
topic. Krikke, van Harten, and Schuur (1999b) have discussed
a case of the recycling PC-monitors as a part of a broader pilot
project at Roteb (the municipal waste company of Rotterdam,
The Netherlands) where by using the model developed, it
achieved a reduction of recycling costs by about 25%.                                                  ENABLERS
Ferguson and Browne (2001) discussed the issues in EOL
product recovery and reverse logistics. Knemeyer, Ponzurick,
and Logar (2002) utilized a qualitative methodology to
examine the feasibility of designing a reverse logistics system
to recycle or refurbish EOL computers that are deemed no
longer useful by their owners [7]. From the literature review, it                                    ALTERNATIVES
is observed that there is not much work reported till date for
                                                                                          Fig.1.ANP model for EOL Computers




                                                                    158                              http://sites.google.com/site/ijcsis/
                                                                                                     ISSN 1947-5500
                                                      (IJCSIS) International Journal of Computer Science and Information Security,
                                                      Vol. 9, No. 4, April 2011


                                                                       Step 5: Pair-wise comparison matrices between component
                      IV. METHODOLOGY                                  /enablers levels
The case study approach was selected because it is an ideal
method when a holistic, in-depth investigation is needed. This                D1                      D2                      D3                  D4
case study approach helps to gather the facts from the real                                                                                  Waste
                                                                         Uncertainity            Demand                Convenience(CON)
world and explain the linkages between causes and effects.                                                                                  Reduction
                                                                        Modeling(UM)         Forecasting(DF)
One such benefit is that the information provided is usually                                                                                  (WR)
more concrete and contextual, specifically due to the in depth          Management of                                                           Cost
                                                                                               Forecasting                  Green
analysis it offers of the case being studied.                            Collection
                                                                                                cost(FS)                 Products(GP)
                                                                                                                                              Savings
                                                                        Centers(MCC)                                                           (CSA)

                                                                                                                                           Recapturing
    A. Algorithm                                                           Impact of        Remanufacturing                Customer
                                                                                                                                             Value
                                                                       Transportation(IT)      cost(RC)                Satisfaction(CSF)
                                                                                                                                             (REV)
Step 1: Start
                                                                                     Table .1. Enablers with respect to the Dimensions
Step 2: Model development and problem formulation                      Two components would be compared at a time with respect to
                                                                       an upper level control criterion. The pair-wise comparisons of
Step 3: Pair-wise comparison of determinants                           the elements at each level are conducted with respect to their
                                                                       relative influence towards their control criterion. For a
                                                                       Determinant, Pair-wise comparison is done between the
                                                                       applicable enablers within a given dimension cluster.
           Economic                Legislation
           Factors
                                                                       Step 6: Pair-wise comparison matrices of interdependencies
                                                                       among the enablers
                      Determinants
                                                                       Step 7: Evaluation of alternatives
           Business            Customer Service
           Strategy            Initiatives

                                                                                                           Self
                                                                                                           Support
                                                                                                           Logistics
                        Fig.2. Determinants                                                                (SSL)


                                                                                   Third                                      Virtual
Step 4: Pair-wise comparison of dimensions for each                                Party                                      Reverse
determinants                                                                       Reverse                                    Logistics
                                                                                   Logistics (3PRL)                           Network (VRL)
                                                       D2
    D1
                 Design
                 Remanufacturing                                                                      Fig.4.Alternatives

                            Dimensions                                 Step 8: Develop Super matrix from Pair-wise comparison
                                                                       matrices of interdependencies

                                                                       Step 9: Selection of the best alternative for a determinant
     D3          Customer               Financial      D4
                                                                       Step 10: Calculation of reverse logistics overall weighted
                                                                       index (RLOWI)

                        Fig.3. Dimensions                              Step 11: Stop




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                                                                                                           ISSN 1947-5500
                                                       (IJCSIS) International Journal of Computer Science and Information Security,
                                                       Vol. 9, No. 4, April 2011


                   V. CASE ILLUSTRATION                                       B. Super Matrix

The model presented in this paper has been evaluated in an                      UM      MCC    IT       DF      FS     RC      CON    GP      CSF    WR     CSA    REV
actual computer manufacturing company, which was                        UM        0     0.14   0.14
interested in the implementation of the reverse logistics               MCC     0.86      0    0.86
                                                                         IT     0.14    0.86    0
practices.                                                               DF                              0      0.83   0.8
                                                                         FS                             0.2       0    0.2
                                                                         RC                             0.8     0.17    0
    A. Main function of the ANP implementation program                  CON                                                      0    0.86    0.83
                                                                         GP                                                    0.83     0     0.17
                                                                        CSF                                                    0.17   0.14     0
void main()                                                             WR                                                                             0    0.86   0.2
                                                                        CSA                                                                          0.86     0    0.8
{ int i,k,n=0,j;                                                        REV                                                                          0.14   0.14    0
 float detevect[20]={0},rlowii[20]={0};
 float rlowic=0, rlowin[10]={0};                                                                        Table.2. Super matrix
 float
det[4][4]={1,6,5,3,0.1667,1,3,2,0.2,0.333,1,2,0.333,0.5,0.5,1};               C. RLOWI calculation for alternatives
 cout<<"\n\t\t DETERMINANTS\n\n";
float * evectordet=evectformation(det);
   for( k=0;k<3;k++)                                                     Alternatives                 3PRL             SSL                    VRL
 {         detevect[k]=*(evectordet+n);                                   Weights
             n++;                           }
   float * edi=economicfactors();
 for( k=0;k<3;k++)                                                       Eco                             0.18                0.1             0.06
 {          rlowi[k][0]=*(edi+n);                                        0.56
             n++;                    }
float * ldi=legislation();                                               Legi                            0.4             0.21                0.13
  for( k=0;k<3;k++)                                                      0.2
 {          rlowi[k][1]=*(ldi+n);
             n++;                         }
                                                                                                         0.51            0.38                0.18
 float * bdi=businessstrategy();                                         BS
 for( k=0;k<3;k++)                                                       0.13
 {          rlowi[k][2]=*(bdi+n);
             n++;                             }                                                          0.65            0.53                0.23
                                                                         CSI
 float * cdi=customerserviceini();                                       0.11
 for( k=0;k<3;k++)
 {          rlowi[k][3]=*(cdi+n);                                                                                RLOWI Calculation
             n++;                               }                                                                RLOWIi = ∑ Dia Ca
                                                                                                       Where
for(i=0;i<3;i++)                                                                                              Dia is the summation of the
 { for(k=0;k<4;k++)                                                                                    products of the desirability indices
       { cout<<rlowi[i][k]<<" ";                  }                                                           Ca is the relative importance
      cout<<"\n";                                  }                                                   weights of the determinants
for(i=0;i<3;i++)
 {        rlowii[i]=0.0;
            for(k=0;k<4;k++)
          { rlowii[i]+= detevect[k]* rlowi[i][k];    }}
cout<<"\n\t\t REVERSE LOGISTICS OVERALL                                                RLOWI            0.245025        0.148481         0.0868695
WEIGHTED INDEX \n\n";
for( i=0;i<3;i++)
  {          cout<<rlowii[i]<<"\n";                     }
  for(i=0;i<3;i++)                                                                                Fig.5. RLOWI calculation for alternatives
 {            rlowic+=rlowii[i];                     }
 for(j=0;j<3;j++)
{           rlowin[j]=(rlowii[j]/rlowic);               }
cout<<"\n\t\t NORMALISED VALUE OF REVERSE
LOGISTICS OVERALL WEIGHTED INDEX \n\n";
for(i=0;i<3;i++)
  { cout<<rlowin[i]<<"\n";                             } }




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                                                                                                               ISSN 1947-5500
                                                          (IJCSIS) International Journal of Computer Science and Information Security,
                                                          Vol. 9, No. 4, April 2011


                                                                                                     VII. REFERENCES
                                                                            [1]   He, B., Yang, C., Ren, M. M. A Fuzzy Multi-objective
                                                                                  programming for optimization of reverse logistics for solid waste
                                                                                  through genetic algorithms. In Fourth international conference on
                                                                                  fuzzy systems and knowledge discovery. China 2007, pp. 416-420.

                                                                            [2]    Kärkkäinen, M. Increasing efficiency in the supply chain for short
                                                                                  shelf life goods using RFID tagging. International Journal of
                                                                                  Retail and Distribution Management, 31(10), 2003, pp 526-536.

                                                                            [3]   Lee, D. H., & Dong, M. A heuristic approach to logistics network
                                                                                  design for end-of-lease computer products recovery.
                                                                                  Transportation Research Part E: Logistics and Transportation
                                                                                  Review, 44(3), 2008, pp 455-474.


                                                                            [4]   Caruso, C., Colorni, A., & Paruccini, M. . The regional urban
                                                                                  solid waste management system: A modeling approach. European
                                                                                  Journal of Operational Research, 70(1), 1993, pp 16-30.
                 Fig.6. Normalised Values for RLOWI
                                                                            [5]   Aras, H., Erdogmus, S., & Koc, E. Multi-criteria selection for a
                                                                                  wind observation station location using analytic hierarchy process.
                                                                                  Renewable Energy, 29, (2004) pp 1383-1392.
                        VI. CONCLUSION
                                                                            [6]   Bian, W., & Yu, M.. Location analysis of reverse logistics
The reverse logistics practices may cost in millions of dollars                   operations for an international electrical manufacturer in Asia
for company. The implementation of these may be a risky                           Pacific using the analytic hierarchy process. International Journal
endeavor for the top management as it involves financial and                      of Services Operations and informatics, 1 (1/2), 187-201.
operational aspects, which can determine the performance of                       Management and Decision Making, 9 (4), (2006), 350-365.
the company in the long run. However, with the legislative
measures tightening up, there are not many options. The                     [7]    V.Ravi, Ravi Shankar & M.K.Tiwari, Analyzing alternatives in
question now is not whether to go for it or not but which                         reverse logistics for end-of-life computers: ANP and balanced
framework to pick up.                                                             scorecard approach, Computers & Industrial Engineering
                                                                                  48,(2005),pp 327-356.

For the case undertaken in this study, the results indicate that
Third party reverse logistics (3PRL) is the first choice of the
case company which is followed by self support logistics
(SSL) and Virtual reverse logistics network (VRL).

Growing complexity of the logistics function and increased
impetus on core competence has led the company under study
to outsource logistics activities to third party reverse logistics
providers as the value of this alternative is higher( 0.510107)
than the other two alternatives. Another important factor
influencing the adoption of 3PRL is globalization.

The major contribution of this paper lies in the development of
a comprehensive model, which incorporates diversified issues
for conducting reverse logistics operations for EOL
computers. The C++ program developed for the execution of
the above ANP problem is more flexible, that it can be used
for any other type of industry under study.




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