RLMT620 Week 7 Paper

International Journal of Production Research, 2007, 1–22, iFirst

Selection of a reverse logistics project for end-of-life computers: ANP

and goal programing approach

V. RAVIy, RAVI SHANKAR*y and M. K. TIWARIz

yDepartment of Management Studies, Indian Institute of Technology Delhi,

Hauz Khas, New Delhi 110 016, India

zDepartment of Manufacturing Engineering, National Institute of Foundry and Forge

Technology, Jharkhand State, Ranchi 834003, India

(Revision received September 2006)

Considering the key issues involved in environmental-friendly disposal of end- of-life (EOL) computer, its supply chain should be designed to incorporate the key dimensions of reverse logistics. An important managerial decision-making activity undertaken by reverse logistics managers is selection of feasible projects that could be completed according to the resources available. The reverse logistics project selection is a multi-criteria decision-making (MCDM) problem. While the experience and expertise of reverse logistics managers could work out for small sized projects, it might not be fruitful for multiple-criteria large sized reverse logistics in arriving at a proper decision related to selection of projects. The reverse logistics projects involve interdependencies among the criteria and the candidate reverse logistics projects. In this paper, a combination of analytical network process (ANP) and zero one goal programing (ZOGP) is used as solution methodologies to deal with the above problem. The ANP is used to determine the degree of interdependence among the criteria and candidate reverse logistics projects, while ZOGP permits the consideration of resource limitations and other constraints in arriving at the solution. The hybrid approach using ANP and ZOGP provides a realistic representation of the problem related to the selection of feasible reverse logistics for EOL computers.

Keywords: Reverse logistics; Analytical network process; Zero-one goal programming; Multi-criteria decision-making; Computer hardware industry

1. Introduction

The computer industry is growing at an exponential rate with new technologies and upgrades reaching the market in a very short span of time. Accordingly, as the technology changes and improves, the products become technically obsolete (Grenchus et al. 2001). Some 500 million computers will be rendered obsolete by 2007 in the USA alone (Hamilton 2001). The product life cycle of computers has drastically reduced and the useful life of a personal computer is now in the sub-three year range (Greene 2000, Pescovitz 2000). Thus, shrinking of the useful life of computers has resulted in an ever-increasing amount of end-of-life (EOL) computers

*Corresponding author. Email: [email protected]

International Journal of Production Research

ISSN 0020–7543 print/ISSN 1366–588X online � 2007 Taylor & Francis http://www.tandf.co.uk/journals

DOI: 10.1080/00207540601115989

being disposed of. While customers have benefited from greater product variety and

enhanced performance, it has also resulted in an increase in unsold products,

packaging materials and waste (Van Hoek 1999). Thus, electronic waste (e-waste) is

a growing concern in computer hardware supply chains. Discarded computers

contain hazardous wastes, which if directly dumped into landfills or improperly

recycled could pose serious hazard to human health and the environment.

For example, computer monitors contain significant quantities of lead and

polybrominated flame retardants, which are potentially hazardous to the environ-

ment. Thus, the e-waste from the computers needs to be disposed of in an

environmental friendly manner. Regulations, corporate and consumer awareness,

competition and marketing motives, and economic motives have resulted in

computer companies initiating reverse logistics activities in their organization. Some of the alternatives for handling EOL computers include temporary storage,

passing it on to someone else for charity, recycling, refurbishing or land filling.

However, with the alarming volume of computers entering the return stream, landfill

may not be a feasible solution to the problem. For example, states like

Massachusetts, Minnesota and Wisconsin have either banned, or are considering

banning, the dumping of computer-related equipment in their landfills (Stough and

Benson, 2000). Thus, if we are to offset the increasing demand for landfills, enhanced

efforts for recycling are needed and this requires reverse logistics activities (Barnes

1982). Murphy and Poist (2000) have reported that recycling of materials, reducing

consumption and reusing materials are the three most commonly utilized green

logistics strategies. Recycling is collecting and totally de-manufacturing/dismantling

EOL computers in order to recover the basic commodities which make up the

computer (Knemeyer et al. 2002). For computers, these commodities fall into three

categories—glass, metal and plastics. Design for recycling (DFR) has become an

important dimension for some computer manufacturers in the recent times (Masanet

et al. 2002). Recycling of computer components when properly implemented

represents the safest and most cost-effective strategy for addressing the problem

posed by EOL computer products (Silicon Valley Toxics Coalition 2004). Designing

the computers to be recyclable not only leads to environmental benefits but also has

become a strategic requirement for computer original equipment manufacturers

(OEMs). Only 10% of the computers taken out of service in the USA each year are

recycled (Platt and Hyde 1997). The lure of export markets for e-waste has had

a profound effect on the US electronics industry (Roman and Puckett 2002). One of

the reports reveals that extremely hazardous and dangerous e-waste recycling

operations of computer recycling activities have polluted the air, water and soil of

Asian countries and endangered the living of residents (Basal Action Network 2002). Supply chains have undergone a radical transformation during the last decade.

Reverse logistics is becoming an important part of supply chain management (Carter

and Ellram 1998). The EOL computers pose several issues regarding proper disposal

of products and thus reverse logistics activities assumes prime importance in

computer hardware supply chains. In addition to environmental and cost benefits,

reverse logistics programs can pro-actively minimize the threat of governmental

regulation, thus improving the corporate image of companies (Carter and Ellram

1998).

2 V. Ravi et al.

Santhanam and Kyparisis (1996) in their research work have classified the interdependence among information system (IS) projects into three main types. These are:

(1) Resource interdependences. (2) Benefit interdependencies. (3) Technical interdependencies.

The same holds good for reverse logistics projects. Resource interdependencies may arise because of the sharing of hardware and software resources among various reverse logistics projects such that the implementation of two or more related projects will require less resources than if they were to be implemented separately. For example, if a recycling strategy developed for one reverse logistics project is used for the second reverse logistics project, then the amount of research and development (R&D) required for developing the recycling strategy for the second reverse logistics project are accordingly reduced.

Benefit interdependencies occur when the total benefits to the organization derived from implementing two related reverse logistics projects increase due to their synergistic effect. Turner et al. (1994) have emphasized the concept of symbiotic logistics to solve the problems of reverse logistics networks. The firms have realized the potential of the mutual benefits arising from working in concert rather than independently by pooling resources (Lambert and Stock 1993). Technical interdependencies arise when an initiation of a new reverse logistics project necessitates understanding of its technical linkages with existing reverse logistics projects.

The shorter product life cycles in the computer industry have increased the volume of product returns and waste entering the reverse logistics channel and the cost of managing them. A computer hardware company may have many reverse logistics projects running at a time. Thus, prioritising these reverse logistics projects on the basis of multiple-criteria having interdependence property could be of great value to the top management in arriving at a strategic decision for efficient running of reverse logistics programs. This research exactly addresses this issue. Analytical network process (ANP) and zero-one goal programming (ZOGP) has been used as the solving methodologies in this research. Reverse logistics projects have some amount of interdependence. ANP can be effectively used to capture the interdependencies among the projects. Also in developing a reverse logistics project selection model, it is important that no fractional solutions would be acceptable since the projects are either selected or not. Hence, a combination of the ANP and ZOGP approach is adopted in this paper.

The electronics industry must take responsibility for their products after the end of their useful life. This responsibility forms the basis for take-back legislation, which has been implemented in the European Union under the Waste Electrical and Electronic Equipment (WEEE) Directive, since August 2005. This directive encourages the design and production of electronics equipment to take into account and facilitate dismantling and recovery, in particular the re-use and recycling of electronics equipment, components, and materials necessary to protect human health and the environment. In 2003, the European Union (EU) enacted the Restriction on Hazardous Substances (RoHS) Directive that banned the use of lead, mercury, cadmium, hexavalent chromium, and certain brominated flame retardants in

Selection of a reverse logistics project for end-of-life computers 3

electronics products sold in the EU market from 1 July 2006 onwards. Since computer products contain significant amount of these materials, this directive may result in a significant change in the way computers are designed.

This paper is further organized as follows. The next section gives a brief literature review on reverse logistics, which is followed by the discussion of ANP and ZOGP methodologies. An illustrative application of a proposed model by an actual computer hardware company in dealing with their reverse logistics projects is discussed. Finally, the results and managerial implications of this research are presented, which is followed by discussion and conclusion.

2. Literature review

Reverse logistics has grown leaps and bounds as a research field since the last decade. It has been conceptualized in a variety of ways by many researchers working in this field. Murphy (1986) was one of the first authors who used the term ‘reverse logistics’ as such. He defined reverse distribution as the ‘movement of goods from a consumer towards a producer in a channel of distribution.’ Stock (1992) recognized the field of reverse logistics as being relevant for business and for society in general. Kopicki et al., (1993) paid attention to the discipline and practice of reverse logistics and pointed out opportunities in the area of re-use and recycling. Carter and Ellram (1998) describe reverse distribution as the return, upstream movement of material resulting from re-use, recycling, or disposal. They define reverse logistics as the sum of reverse distribution and source reduction. Reverse logistics is the process of planning, implementing and controlling backward flows of raw materials, in process inventory, packaging and finished goods, from a manufacturing, distribution or use point, to a point of recovery or point of proper disposal (RevLog 1998). Reverse logistics is the process of moving goods from the point of consumption to another point for the purpose of recapturing the remaining value, or for the eventual proper disposal of the product (Rogers and Tibben-Lembke 1999). Dowlatshahi (2000) defines reverse logistics as the process in which a manufacturer systematically accepts previously shipped products or parts from the point for consumption for possible recycling, remanufacturing, or disposal. Blumberg (2005) defines reverse logistics as the subset of closed loop supply chain which includes full co-ordination and control, physical pickup and delivery of the material, parts, and products from the field of processing and recycling or disposition, and subsequent returns back to the field where appropriate.

Reverse logistics activities are being practiced in many industries and computer hardware industries are no exception. Computer giants like IBM and Dell have embraced reverse logistics by taking steps to streamline the way they deploy old systems and in the process make it easier for the customers to refurbish existing computers or buy new parts (Ferguson 2000). Grenchus et al. (2001) have reported that the Global Asset Recovery Services (GARS) organization of IBM’s Global Financing division has integrated some of the key components of its reverse logistics network in order to enhance environmental performance. Moyer and Gupta (1997) have conducted an exhaustive survey of previous works related to environmentally conscious manufacturing practices, recycling, and the complexities of disassembly processes in the electronics industry. Veerakamolmal and Gupta (1999) have

4 V. Ravi et al.

discussed a technique for analyzing the design efficiency of electronic products in order to study the effect of EOL disassembly and disposal on environment. Krikke et al. (1999) have discussed a case of recycling personal computer monitors as a part of a broader pilot project at Roteb (The Netherlands) where, by using the model developed, a reduction in recycling costs of about 25% was achieved. Boon et al. (2002) have investigated the critical factors influencing the profitability of EOL processing of PCs. They have also suggested suitable policies for both PC manufacturers and legislators to ensure that there is a viable PC recycling infrastructure. Knemeyer et al. (2002) have utilized qualitative methodology to examine the feasibility of designing a reverse logistics system to recycle and/or refurbish EOL computers that are deemed no longer useful by their owners. Tan et al. (2003) have conducted a study on a leading US-based computer maker to examine its reverse logistics operations in the Asia–Pacific region. Ravi et al. (2005) have utilized the ANP and balanced scorecard approach to analyze the alternatives in reverse logistics for EOL computers. Kongar and Gupta (2000) opine that in an environmentally conscious environment it is no longer realistic to use a single objective function since the introduction of restrictive regulations make the decision procedure more complicated and mostly multi-objective. They also suggest that a goal programing approach is especially appropriate for taking decision in these cases. From the literature review, it is observed that there is not much work reported thus far for selection of reverse logistics projects on the basis of multi-criteria having interdependence property in the case of EOL computers. This research is an attempt to fill this gap in the literature.

3. Goal programming using the ANP approach for reverse logistics project selection

The ANP is a comprehensive technique that allows for the inclusion of all the relevant criteria; tangible as well as intangible, which have some bearing on the decision-making process (Saaty 1996). In the decision-making problems, the interdependent relationship among criteria needs to be considered because of the inherent nature of interdependence that exists in real life problems. The ANP methodology allows for the consideration of interdependencies among and between the levels of criteria. The reverse logistics variables for end-of-life computers are interdependent in nature and ANP can be used to effectively capture interdepen- dencies among them (Ravi et al. 2005).

In order to consider the interdependence among the reverse logistics projects, the first step is to identify the multiple criteria that merit consideration. Then we draw a relationship between the criteria that show the degree of interdependence among the criteria (Saaty and Takisawa 1986, Lee and Kim 2000). Subsequently, the degree of impact or influence between the criteria is determined. One of the illustrative questions asked of the decision maker is: ‘In comparing reverse logistics 1 and 2, on the basis of cost reduction, which project would be preferred?’ When there is interdependence, the illustrative question could be: ‘Out of the given set of alternatives and attributes, which of the alternatives influences more with respect to an attribute and how much more?’ For making comparisons between the criteria, a scale of 1 to 9 is used (Saaty 1980). The final step is to determine the overall prioritisation of the reverse logistics projects.

Selection of a reverse logistics project for end-of-life computers 5

In the second phase, the result obtained from the ANP is used to formulate a zero-one goal programming (ZOGP) model as a weight. The solution from the ZOGP will determine the resources that will be allocated among the different reverse logistics projects. ZOGP permits the consideration of resource limitations and other selection constraints which must be taken in to care in the selection of reverse logistics projects.

3.1 Assumptions made in the model

(1) Return rates of EOL computers for each of the models are deterministic and constant.

(2) The quality of the returned EOL computers for all models is assumed to be the same.

(3) Reverse logistics operating costs like the sorting and disassembly costs, recycling costs and disposal costs remain constant and do not change during the period under study.

(4) The period under study is taken as one year.

The ZOGP model for reverse logistics project selection can be stated as follows:

Minimize Z¼Pk (wj d þ i , wj d

� i )

subject to

aijxj þd � i �d

þ i � bi for i ¼ 1, 2, . . . , m, j ¼ 1, 2, . . . , n

xj þdi ¼ 1 for i ¼ mþ1, mþ2, . . . , mþn, j ¼ 1, 2, . . . , n xj ¼ 0 or 1 for 8j,

ð1Þ

where

m The number of reverse logistics project goals considered in the model. n The pool of reverse logistics projects from which optimal projects would

be selected. wj The ANP mathematical weight on the j¼1, 2, . . . , n reverse logistics

projects. Pk Some K pre-emptive priority (P14P24, . . . , Pk), for i¼1, 2, . . . , m

reverse logistics project goals. di Deviation variables for goals.

dþi , d � i The ith positive and negative deviation variables for i¼1, 2, . . . , m

reverse logistics project goals. aij The jth reverse logistics project usage parameter of the ith resources. bi The ith available resource or limitation factors that must be considered

in the selection decision.

xj 1, select the jth reverse logistics project: 0, do not select the jth reverse logistics project:

�

The ZOGP model bases the selection of the reverse logistics xj on the determined weights of ANP wj for corresponding d

� i .

6 V. Ravi et al.

4. An illustrative application of the proposed model

in a computer hardware company

The model that is presented in this research has evaluated in an actual computer manufacturing company engaged in reverse logistics activities. The reverse logistics framework for the case computer company is illustrated in figure 1. It is seen from this figure that the supply chain of the company has integrated activities not only concerned with supply alone, but also activities concerning service and product recovery. Defective products may be detected after they have entered the supply chain resulting in product returns due to warranty claims. After the useful life of computers, they are collected for the reverse flow. Then sorting and dismantling of computer components is carried out and potentially suitable components are chosen for recycling. The recycled components are then sold in secondary markets. Those components which cannot be recycled are properly disposed of so that there are no harmful effects on the environment.

The company is engaged in producing six brands of computers catering to the various requirements of its customers, which amounts to the management of six reverse logistics projects. The top management is interested in prioritising these reverse logistics projects on the basis of multiple criteria. The criteria are:

(1) Increased use of resource reduction (RR). (2) Increase of eco-efficiency (EE).

Collection for reverse flow

Sorting and disassembly

Customer

Computer part supplier

Distribution network Warranty returns

Warranty returns

End-of-life

Manufacture and assembly

Recycling and processing

Disposal

Sale in secondary markets

Figure 1. Reverse logistics framework for the computer company.

Selection of a reverse logistics project for end-of-life computers 7

(3) Development of green products (GP). (4) Cost of implementation of reverse logistics programs (IC).

The importance of these four criteria in reverse logistics projects follows the next part of this section.

4.1 Resource reduction

Resource reduction should be the ultimate goal of reverse logistics programes (Carter and Ellram, 1998) as shown in figure 2. Resource reduction refers to the minimisation of materials used in a product and the minimization of waste and energy achieved through the design of more environmentally efficient products. It is evident from this figure, that once the resource reduction option has been exhausted, the firm should attempt to maximize re-use, recycling, and so on. Disposal should be last option where the firm can either dispose of a product through incineration, whereby some form of energy recovery may be possible, or through disposal at a landfill.

4.2 Eco-efficiency

The World Business Council for Sustainable Development (WBCSD) defines eco-efficiency as being achieved by the delivery of competitively priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life cycle, to a level at least in line with Earth’s estimated carrying capacity (WBCSD 2000). Eco-efficiency is used to describe those companies which add the most value with

Resource reduction

Re-use

Re-cycling

Disposal with energy recovery

Disposal in landfill

Figure 2. The reverse logistics hierarchy (Carter and Ellram 1998).

8 V. Ravi et al.

least resources and the least pollution (Schmidheiny 1992). Eco-efficiency is defined as a combination of economic and environmental (ecological) efficiencies, expressed by the ratio:

Eco-efficiency ¼ Economic value ðaddedÞ

Environmental impact ðaddedÞ ð2Þ

According to equation (2), eco-efficiency is improved by reducing the environmental impact added while increasing the value of the output produced. Eco-efficiency is critical for organizations that seek to be both environmentally conscious and profitable (Sarkis and Talluri 2004). Reverse logistics is a process whereby companies can become environmentally efficient through recycling, reusing, and reducing the amount of materials used (Carter and Ellram 1998). WBCSD defines seven components of eco-efficiency as: reduced materials intensity, reduced energy intensity, reduced dispersion of toxic substances, enhanced recyclability, maximized use of renewables, extended product life, increased service intensity. Thus, the increase of eco-efficiency leads to reverse logistics activities in the firm. The outcomes of the eco-efficiency calculations will help authorities in formulating criteria for collection of disposed products and in monitoring end-of-life performance of take- back systems (Huisman et al. 2002).

4.3 Green products

In the present industrial scenario, the ‘green’ image of the companies has become an important marketing element (Fleishchmann et al. 1997). Green products are emerging from the demand-pull of customers with new attitudes toward environ- mental values (Simon 1992). Present day customers expect products offered for sale to be free of harmful ingredients and materials that cause environmental pollution or that endanger the well-being of users (Vandermerve and Oliff 1990). Green product development, which addresses environmental issues through product design and innovation as opposed to the traditional end-of-pipe-control approach, is receiving significant attention from customers, industries, and governments around the world (Chen 2001). Market research in the USA has found that green products account for 9.5% of all new product introductions in the economy (Ottman 1998). Analysts have identified the growth and opportunities in green markets as ‘the next big thing’ for small business (Murphy 2003).

4.4 Cost of implementation of reverse logistics projects

For the business to remain sustainable, it should be profitable. Reverse logistics programmes may cost a company millions of dollars. Financial constraints are one of the key barriers to good reverse logistics programs (Rogers and Tibben-Lembke 1999, Ravi and Shankar 2005). Finance is needed to support the infrastructure and manpower requirements of reverse logistics processes. Information support is one of the ways to develop linkages in achieving efficient reverse logistics operations (Daugherty et al. 2002). Efficient information and technological systems are needed to for tracing and tracking of the potential products that would return for reverse logistics operations. Training of staff related to the reverse logistics is also essential

Selection of a reverse logistics project for end-of-life computers 9

for efficient management of reverse logistics operations. All of these reverse logistics activities requires financial support.

4.5 Relationship among the four criteria

A quick look at these four criteria reveals that there is an interdependence relationship among them in reverse logistics project selection problems. For example, resource reduction strategies would result in increase of eco-efficiency and also decrease the cost of implementation of reverse logistics projects. Likewise, for development of green products an increase of eco-efficiency and an increase in cost of implementation are needed. Also, for increasing the eco-efficiency, initiation of resource reduction strategies and an increase of cost of implementation are needed. Thus, it is evident from above that there is interdependency among the criteria; the attribute of criteria RR affecting EE and IC, the attribute of criteria EE affecting GP, and criteria IC influencing criteria EE and GP. In this research for identifying the relationship and degree of interdependency among the criteria, two experts, one from computer hardware industry and another from academia were consulted for the same. These experts from the industry and academia were well conversant with reverse logistics practices in computer hardware industry having an experience of over ten years. The relationship having interdependence among the four criteria taken in this research is illustrated in figure 3.

In order to find the weight of degree of influence among the selected criteria, we use the procedure using the matrix manipulation based on Saaty and Takisawa (1986) and Lee and Kim (2000). The proposed ANP-ZOGP model is illustrated in the following seven steps:

Step 1: For carrying out the comparison among the criteria, the sample question asked of the experts was: ‘Out of the all the criteria, which one should be emphasized

+

+

+

+

+

RR EE

EE

EE

Figure 3. Interdependent relationship among the criteria. RR, resource reduction; EE, eco-efficiency; IC, cost of implementation, GP, green products.

10 V. Ravi et al.

more in reverse logistics projects for EOL computers, and how much more’?

By comparing all pairs with respect to the six brands of computers, we obtain

the following data like (RR, EE, IC, GP)¼ (5, 7, 9, 4)ffi (5/25, 7/25, 9/25, 4/25)¼ (0.2,

0.28, 0.36, 0.16) assuming that there is no interdependence among them. This data

means only relative weight without considering independence among the criteria.

Step 2: In this step, by assuming that there is no interdependence among the six

reverse logistics projects (p1, p2, p3, p4, p5, p6) comparison is done with respect to each

criterion yielding the column normalized weight with respect to each criterion, as

shown in table 1.

Step 3: In this step, the interdependence among the criteria is considered. When we

select the reverse logistics project, we cannot do it based on only on one criterion, but

also must consider other criteria with it. In table 2, we obtain the four sets of weight

through discussions with the experts. The data in this table mean four criteria’s

degree of relative impact for each four criteria. For example, the RR’s degree of

relative impact for EE is 0.4, and the EE’s degree of relative impact for GP is 0.3.

The interdependence weight matrix of criteria is defined as:

W3 ¼

1:0 0:4 0:5 0:0 0:0 0:5 0 0:3 0:0 0:1 0:5 0:2 0:0 0:0 0:0 0:5

0 BB@

1 CCA

Step 4: In this step, the interdependence among the alternatives with respect to each

criterion is done. An illustrative question asked to the decision maker

is: ‘With respect to the satisfaction of criteria 1 (RR), with reverse logistics

project, which of the project contributes more to the action of project 1 to

criteria 1 and how much more’? The data from such a comparison made is shown in

tables 3 to 6.

Table 1. Data of reverse logistics projects to four criteria.

Reverse logistics projects RR EE IC GP

p1 6 5 4 5 p2 5 7 8 4 p3 9 4 6 7 p4 8 6 7 4 p5 5 7 5 7 p6 5 8 9 4

p1 0.158 0.135 0.103 0.161 p2 0.132 0.189 0.205 0.129 p3 0.237 0.108 0.154 0.226 p4 0.211 0.162 0.179 0.129 p5 0.132 0.189 0.128 0.226 p6 0.132 0.216 0.231 0.129

w21 w22 w23 w24

RR, resource reduction; EE, eco-efficiency; IC, implementation cost, GP, green products.

Selection of a reverse logistics project for end-of-life computers 11

The project interdependence weight matrix for criteria RR (w41) is defined as:

w41¼

0:184 0:012 0:009 0:009 0:011 0:021

0:079 0:176 0:011 0:008 0:012 0:042

0:132 0:141 0:223 0:014 0:013 0:028

0:158 0:247 0:178 0:285 0:016 0:028

0:237 0:282 0:312 0:342 0:474 0:034

0:211 0:141 0:267 0:342 0:474 0:846

2 666666666664

3 777777777775

Step 5: The interdependence priorities of the criteria by synthesizing the results

from steps 1 to 3 is as follows:

Wc ¼ W3 �W1 ¼

1:0 0:4 0:5 0:0

0:0 0:5 0:0 0:3

0:0 0:1 0:5 0:2

0:0 0:0 0:0 0:5

2 666664

3 777775 �

0:2

0:28

0:36

0:16

2 666664

3 777775 ¼

0:492

0:188

0:240

0:080

2 666664

3 777775

Thus, we have Wc¼ (RR, EE, IC, GP)¼(0.492, 0.188, 0.240, 0.080)

Table 3. Data among reverse logistics projects for criteria 1 (RR).

w41 p1 p2 p3 p4 p5 p6

p1 7 1/3 1/5 1/6 1/9 1/8 p2 3 5 1/4 1/7 1/8 1/4 p3 5 4 5 1/4 1/7 1/6 p4 6 7 4 5 1/6 1/6 p5 9 8 7 6 5 1/5 p6 8 4 6 6 5 5

p1 0.184 0.012 0.009 0.009 0.011 0.021 p2 0.079 0.176 0.011 0.008 0.012 0.042 p3 0.132 0.141 0.223 0.014 0.013 0.028 p4 0.158 0.247 0.178 0.285 0.016 0.028 p5 0.237 0.282 0.312 0.342 0.474 0.034 p6 0.211 0.141 0.267 0.342 0.474 0.846

Table 2. Data among four criteria.

W3 RR EE IC GP

RR 1 0.4 0.5 0.0 EE 0 0.5 0.0 0.3 IC 0 0.1 0.5 0.2 GP 0 0.0 0.0 0.5

RR, resource reduction; EE, eco-efficiency; IC, implementation cost, GP, green products.

12 V. Ravi et al.

Table 4. Data among reverse logistics projects for criteria 2 (EE).

w42 p1 p2 p3 p4 p5 p6

p1 5 1/7 1/7 1/5 1/4 1/7 p2 7 7 1/6 1/6 1/7 1/5 p3 7 6 5 1/3 1/7 1/7 p4 5 6 3 7 1/5 1/4 p5 4 7 7 5 5 1/7 p6 7 5 7 4 7 7

p1 0.143 0.005 0.006 0.012 0.020 0.018 p2 0.200 0.225 0.007 0.010 0.011 0.025 p3 0.200 0.193 0.224 0.020 0.011 0.018 p4 0.143 0.193 0.134 0.419 0.016 0.032 p5 0.114 0.225 0.314 0.299 0.393 0.018 p6 0.20 0.161 0.314 0.240 0.550 0.889

EE, eco-efficiency.

Table 5. Data among reverse logistics projects for criteria 3 (IC).

w43 p1 p2 p3 p4 p5 p6

p1 7 1/4 1/5 1/7 1/5 1/3 p2 4 5 1/7 1/5 1/4 1/7 p3 5 7 7 1/4 1/3 1/5 p4 7 5 4 5 1/7 1/7 p5 5 4 3 7 8 1/7 p6 3 7 5 5 7 5

p1 0.226 0.009 0.010 0.008 0.013 0.056 p2 0.129 0.177 0.007 0.011 0.016 0.024 p3 0.161 0.248 0.362 0.014 0.021 0.034 p4 0.226 0.177 0.207 0.284 0.009 0.024 p5 0.161 0.142 0.155 0.398 0.502 0.024 p6 0.097 0.248 0.259 0.284 0.440 0.839

IC, implementation cost.

Table 6. Data among reverse logistics projects for criteria 4 (GP).

w44 p1 p2 p3 p4 p5 p6

p1 5 1/7 1/6 1/7 1/7 1/9 p2 7 7 1/4 1/5 1/7 1/5 p3 6 4 5 1/7 1/5 1/7 p4 7 5 7 5 1/7 1/9 p5 7 7 5 7 7 1/5 p6 9 5 7 9 5 5

p1 0.122 0.005 0.007 0.007 0.011 0.019 p2 0.171 0.249 0.010 0.009 0.011 0.035 p3 0.146 0.142 0.205 0.007 0.016 0.025 p4 0.171 0.178 0.287 0.233 0.011 0.019 p5 0.171 0.249 0.205 0.326 0.554 0.035 p6 0.220 0.178 0.287 0.419 0.396 0.867

GP, green products.

Selection of a reverse logistics project for end-of-life computers 13

Step 6: The priorities of the projects Wp with respect to each of the

four criteria are given by synthesizing the results from steps 2 and 4 is done as

follows:

Wp1 ¼ W41 �W21 ¼

0:0389

0:0471

0:1007

0:1657

0:2878

0:3616

2 66666664

3 77777775 , Wp2 ¼ W42 �W22 ¼

0:0305

0:0794

0:0969

0:1481

0:2184

0:4262

2 66666664

3 77777775

Wp3 ¼ W43 �W23 ¼

0:0423

0:0602

0:1362

0:1490

0:2106

0:4017

2 66666664

3 77777775 , Wp4 ¼ W44 �W24 ¼

0:0277

0:0701

0:0959

0:1503

0:2778

0:3786

2 66666664

3 77777775

The matrix Wp is derived by grouping the above four columns:

Wp ¼ ðWp1, Wp2, Wp3, Wp4Þ

Step 7: The overall priorities for the reverse logistics projects are calculated by

multiplying Wp by Wc.

We have Wp �Wc

0:037

0:058

0:108

0:157

0:255

0:385

2 66666664

3 77777775

The final results in the ANP phase are (p1, p2, p3, p4, p5, p6)¼ (0.037, 0.058,

0.108, 0.157, 0.255, 0.385). From these results, it can be inferred that the highest

weight of criteria in this reverse logistics project selection is p6 followed by p5 and so

on. These weights so obtained from the ANP phase are used as priorities in goal

programing formulation. That is (p1, p2, p3, p4, p5, p6)¼ (w1, w2, w3, w4, w5,

w6)¼ (0.037, 0.058, 0.108, 0.157, 0.255, 0.385), as wj values of the six reverse logistics

projects. In order to formulate the ZOGP model, we have taken a typical situation faced

by a case computer company dealing with the reverse logistics projects for EOL

computers in their organization. Silicon Valley Toxics Coalition (2004) estimates the

cost of recycling of computers ranges from $10 to $60 per unit, and $25 to $50 per

unit for safe disposal of hazardous waste from computers. This was used as the

reference for the formulation of reverse logistics data for the case company dealt

in this paper. The case company had certain obligatory as well as flexible goals for

14 V. Ravi et al.

the six reverse logistics projects that were under consideration. There are four obligatory goals for the case company:

(1) Total amount of e-waste should not exceed 1750 tonnes from list of reverse logistics projects selected for the period in one year.

(2) A total maximum budget of $25 000 000 is available as sorting and disassembly costs to complete all of the reverse logistics projects selected.

(3) A total maximum budget of $15 000 000 is available as recycling costs to complete all of reverse logistics projects selected.

(4) Reverse logistics project 3 consists of a brand of computer which contributes to more than one-third of the total production of the company and therefore is a mandated project that must be one of the reverse logistics projects selected.

In addition to the goal of selecting the reverse logistics projects, there are two other flexible goals, stated in order of their importance:

(1) An initial allocation of budgeted dollars as recycling costs is set as $12 000 000 but can vary up to but not beyond the total maximum value of $15 000 000.

(2) An initial allocation goal of proper disposal costs is set at $20 000 000 but deviation from this allocation is possible.

Table 7 shows the resource usage information for the six reverse logistics projects during the period under consideration.

Based on these data and the previously computed ANP value, the goal constraints for the hypothetical problem are presented in table 8.

This ZOGP was solved using LINDO software. The results obtained are as follows:

d�1 ¼ 0, d þ 1 ¼ 0, d

� 2 ¼ 325, d

þ 2 ¼ 0, d

� 3 ¼ 0, d

þ 3 ¼ 0, d

� 4 ¼ 0, d

� 5 ¼ 1, d

� 6 ¼ 1,

d�7 ¼ 0, d � 8 ¼ 0, d

� 9 ¼ 0, d

� 10 ¼ 0, d

� 11 ¼ 0, d

þ 11 ¼ 300, d

� 12 ¼ 0, d

þ 12 ¼ 145:

Projects 3, 4, 5, and 6 were chosen which results in a total amount of e-waste of 1750 tonnes. When these projects are selected, the sorting and disassembly cost is reduced by $325 000. An extra amount of $145 000 is to be allocated as disposal costs.

In order to assess the results of the ANP-ZOGP model, the same problem presented in table 7 has been solved by using a zero-one integer programing approach for the selection of the feasible reverse logistics projects. In the zero-one integer programming model, there are both hard and soft constraints. The hard constraints, which must be exactly satisfied, correspond to the obligatory goals of the

Table 7. Yearly resources usage for all the reverse logistics projects.

Reverse logistics project yearly resources data

x1 x2 x3 x4 x5 x6 bi

Amount of e-waste (in tonnes) 200 400 600 300 500 350 1750 Sorting and disassembly costs (000) 300 550 700 475 600 400 2500 Recycling costs (000) 325 300 400 350 400 350 1500 Disposal costs (000) 250 475 600 400 620 525 2000

Selection of a reverse logistics project for end-of-life computers 15

T a b le

8 .

Z O G P m o d el

fo rm

u la ti o n .

Z O G P m o d el

fo rm

u la ti o n

G o a ls

M in im

iz e Z ¼ p l 1 (d þ 1 þ d þ 2 þ d þ 3 þ d � 4 )

S a ti sf y a ll o b li g a to ry

g o a ls

p l 2 (0 .0 3 7 d � 5 þ 0 .0 5 8 d � 6 þ 0 .1 0 8 d � 7 þ 0 .1 5 7 d � 8 þ 0 .2 5 5 d � 9 þ 0 .3 8 5 d � 1 0 )

S el ec t h ig h es t A N P w ei g h te d re v er se

lo g is ti cs

p ro je ct s

p l 3 (d � 1 1 þ d þ 1 1 )

U se

$ 1 2 0 0 0 0 0 a s re cy cl in g co st

fo r a ll th e re v er se

lo g is ti cs

p ro je ct s se le ct ed

p l 4 (d � 1 2 þ d þ 1 2 )

U se

$ 2 0 0 0 0 0 0 a s d is p o sa l co st

fo r a ll th e re v er se

lo g is ti cs

p ro je ct s se le ct ed

S u b je ct

to 2 0 0 x 1 þ 4 0 0 x 2 þ 6 0 0 x 3 þ 3 0 0 x 4 þ 5 0 0 x 5 þ 3 5 0 x 6 þ d � 1 � d þ 1 ¼ 7 5 0

A v o id

o v er

u ti li zi n g m a x im

u m

a m o u n t o f e- w a st e

3 0 0 x 1 þ 5 5 0 x 2 þ 7 0 0 x 3 þ 4 7 5 x 4 þ 6 0 0 x 5 þ 4 0 0 x 6 þ d � 2 � d þ 2 ¼ 2 5 0 0

A v o id

o v er

u ti li zi n g m a x im

u m

b u d g et ed

d o ll a rs

fo r so rt in g a n d

d is a ss em

b ly

co st s

3 2 5 x 1 þ 3 0 0 x 2 þ 4 0 0 x 3 þ 3 5 0 x 4 þ 4 0 0 x 5 þ 3 5 0 x 6 þ d � 3 � d þ 3 ¼ 1 5 0 0

A v o id

o v er

u ti li zi n g m a x im

u m

b u d g et ed

d o ll a rs

a s re cy cl in g co st s

x 3 þ d � 4 ¼ 1

S el ec t o b li g a to ry

re v er se

lo g is ti cs

p ro je ct

3 x 1 þ d � 5 ¼ 1

S el ec t p ro je ct

1 x 2 þ d � 6 ¼ 1

S el ec t p ro je ct

2 x 3 þ d � 7 ¼ 1

S el ec t p ro je ct

3 x 4 þ d � 8 ¼ 1

S el ec t p ro je ct

4 x 5 þ d � 9 ¼ 1

S el ec t p ro je ct

5 x 6 þ d � 1 0 ¼ 1

S el ec t p ro je ct

6 3 2 5 x 1 þ 3 0 0 x 2 þ 4 0 0 x 3 þ 3 5 0 x 4 þ 4 0 0 x 5 þ 3 5 0 x 6 þ d � 1 1 � d þ 1 1 ¼ 1 2 0 0

A v o id

o v er

o r u n d er

u ti li zi n g th e b u d g et ed

d o ll a rs

a s re cy cl in g co st s

2 5 0 x 1 þ 4 7 5 x 2 þ 6 0 0 x 3 þ 4 0 0 x 4 þ 6 2 0 x 5 þ 5 2 5 x 6 þ d � 1 2 � d þ 1 2 ¼ 2 0 0 0

x j ¼ 0 o r 1 j ¼ 1 ,2 ,.

. . ,n

A v o id

o v er

o r u n d er

u ti li zi n g th e b u d g et ed

d o ll a rs

a s d is p o sa l co st s

Z O G P , ze ro

o n e g o a l p ro g ra m in g .

16 V. Ravi et al.

proposed ANP-ZOGP model. The soft constraints that can be relaxed correspond to

the flexible goals of the proposed ANP-ZOGP model. This model is similar to ZOGP

model (table 8), except that the objective function is to maximize

X6 i¼1

xi

and there are no goals and related deviational variables (dþi and d � i ). In this project 3

is binding in selection while other constraints are of less than and equal to type.

Table 9 presents a comparison of the results using zero-one integer programming and

ANP-ZOGP models. The zero-one integer programming approach resulted in the selection of reverse

logistics projects 1, 3 and 4, thus resulting in large amount of unused resources for

the case company. On the other hand, ANP-ZOGP model results in more number of

reverse logistics projects being completed with less unused resources. Although, the

ANP-ZOGP model results in extra amount of $145 000 as disposal costs, this is good

for the cause of environment and also for increasing the corporate image of the

company in the long run. Thus, it could be seen that ANP-ZOGP model gives better

solution with inclusion of obligatory and flexible goals and enhances the clarity of

the decision makers in selection of feasible reverse logistics projects by making better

utilisation of the available resources.

5. Sensitivity analyzis

Sensitivity analyzis is an important concept for the effective use of any quantitative

decision model (Poh and Ang 1999). As stated earlier, the case company under

present study has four obligatory goals and two flexible goals for the six reverse

logistics projects. The two flexible goals in the order of their importance are:

(1) An initial allocation of budgeted dollars as recycling costs is set as $12 00 000

but can vary up to but not beyond a total maximum value of $15 000 000. (2) An initial allocation goal of proper disposal costs is set at $20 000 000, though

some deviation from this allocation is possible.

Table 9. Comparison of zero-one integer programing and ANP-ZOGP solution.

Selected reverse

Resulting unused resources

Method logistics projects

E-waste (in tonnes)

Sorting and disassembly costs (000)

Recycling costs (000)

Disposal costs (000)

Zero-one integer programming model

1, 3, 4 650 1025 425 750

ANP-ZOGP model 3, 4, 5, 6 0 325 0* 0*

*Extra amount is allocated for recycling and disposal costs. ANP-ZOGP, analytical network process-zero one goal programing.

Selection of a reverse logistics project for end-of-life computers 17

In the present research, sensitivity analyzis has been conducted to judge how the solution would change when there is change in flexible goals. These results are presented in table 10.

Table 10 presents the results of sensitivity analyzis for the flexible goals of the ANP-ZOGP model. In this table, the first and second columns represent the different values of initial allocation of recycling costs and disposal costs. LINDO software was used to find solutions to the ZOGP. The results indicate that in all the cases, projects 3, 4, 5, and 6 were the possible reverse logistics projects selected. In other words, sensitivity analyzis indicates the fact that changing the flexible goals do not result in the selection of different reverse logistics projects.

6. Results and managerial applications

In this section, we first discuss the results of the ANP-ZOGP model followed by a discussion on the managerial implications of this model.

Reverse logistics project selection are multi-criteria decision-making (MCDM) problems. The major contribution of this research is the development of an ANP- ZOGP model that takes into account the interdependencies among criteria and candidate reverse logistics projects such that feasible reverse logistics projects are selected. Most of the real world projects have the property of interdependence. ANP can be used to determine the degree of interdependence among the criteria and projects and the inner dependence among them. But one of the shortfalls of ANP is that it lacks taking into account resource limitations required in the course of selection of reverse logistics projects. The ZOGP allows for including the constraints by which it can provide a feasible solution that best satisfies the priority goals of the decision maker. Thus, taking into account all these factors, we have used both

Table 10. Sensitivity analysis conducted on changing the flexible goals for the proposed ANP-ZOGP model.

Result from ANP-ZOGP Solution

Initial budgeted allocation of recycling costs (000)

Initial budgeted

allocation of disposal costs

(000)

Selected reverse logistics projects

E-waste unused (tonnes)

Decrease in sorting and disassembly costs (d�2 )

(000)

Increase in recycling

costs (dþ11) (000)

Increase in disposal costs (dþ12)

(000)

0 0 3, 4, 5, 6 0 325 1500 2145 250 250 3, 4, 5, 6 0 325 1250 1895 500 500 3, 4, 5, 6 0 325 1000 1645 750 750 3, 4, 5, 6 0 325 750 1395 1000 1000 3, 4, 5, 6 0 325 500 1145 1250 1250 3, 4, 5, 6 0 325 250 895 1500 1500 3, 4, 5, 6 0 325 0 645 1500 1750 3, 4, 5, 6 0 325 0 395 1500 2000 3, 4, 5, 6 0 325 0 145 1500 2145 3, 4, 5, 6 0 325 0 0

ANP-ZOGP, analytical network process-zero one goal programing.

18 V. Ravi et al.

ANP and ZOGP as solving methodologies to arrive at an optimum solution to the stated problem.

For the case undertaken in this study, the results indicate that out of the pool of six reverse logistics projects from which selection was made, the ZOGP model selected all of the projects except 1 and 2. Such a selection resulted in the sorting and disassembly costs to be reduced by $325 000. Also an extra amount of $145 000 is needed as disposal costs. This assumes importance as properly disposing of unwanted products is becoming a more closely monitored activity (Rogers and Tibben-Lembke 1999) and a green image has become an important part of the marketing element for the companies. The proposed model provides some vital managerial implications for the companies, which are now discussed.

The proposed model can act as a guide to the top management in the selection of reverse logistics projects such that there is no undue allocation of resources to reverse logistics projects that is not profitable. It gives better insights to the top management so that they can proactively deal with the redesign of the computers for better disassembly and recycling. For the case company to be successful in implementing the reverse logistics projects, gatekeeping on return operations should be enforced rigorously. They need to speed up the integration of legacy systems to simpler and web-based applications to facilitate the tracking and tracing of returns, as well as the overall integration of all reverse logistics activities. The management can reason out why some of the projects were not selected out of the given set of conditions of goals and constraints. Thierry et al. (1995) opine that most of the cases of product recovery management require redesign of the products. Thus, one of the reasons for some of the projects being not selected could be the poor design of these models of computer. Thus the company should explicitly consider the requirements for product re-use and product return channels during the design phase for the development for these computers. The model does not recommend reverse logistics projects 1 and 2 according to the resource constraints of the case computer company. Thus, they can explore the possibility of some third party reverse logistics solution providers specializing in this area for dealing with these projects.

7. Discussion and conclusion

Reverse logistics programmes are becoming an integral part of the computer hardware supply chains. The importance of reverse logistics to the profitability of the company depends on the capability to recover as much economic value as possible out of the used products. Also, reducing the environmental impact of used products increases the corporate image of companies. The EOL computers contain hazardous metals, which need to be disposed of in an environmentally friendly manner. With legislative measures being tightened up, there are not many options left for companies but to embrace reverse logistics operations into their production plans, both from economic as well as environmental point of view. The implementation of reverse logistics projects could be a risky endeavour for the top management as it involves financial and operational aspects which ultimately determine the performance of the company. Reverse logistics projects involve staff, machine and financial resources that could prohibit the selection of some of the projects.

Selection of a reverse logistics project for end-of-life computers 19

Thus, reverse logistics managers would definitely like to avoid selecting those projects that cannot be completed in time because of limited resources available. The proposed model addresses this issue and thus it may be useful to policy makers and decision makers dealing with the strategic management of reverse logistics projects for EOL computers. One of the limitations of the proposed model is that we have assumed that the return rates for EOL computers are known with certainty and therefore a deterministic model is applicable. Future research may be conducted to model this aspect as a stochastic problem.

An annual or quarterly managerial decision-making activity that reverse logistics managers have to perform is the selection of the projects that would be feasible. Moreover these reverse logistics projects may have an amount of interdependence which need to be taken into account for arriving at a proper decision. In a real life situation, the reverse logistics projects are subject to a great deal of uncertainty in terms of quality, quantity and timing of return of the products. A commonly used strategy to select these projects is based on the expertise and experience of reverse logistics manager. While these strategies might work out for single criteria small sized reverse logistics projects, it might not be fruitful in the case of multi-criteria large sized reverse logistics projects. Thus, the proposed ANP-ZOGP model provides a solution to this problem.

Finally, we examine the scope of future research. Future research could include more reverse logistics projects and criteria in the proposed ANP-ZOGP model. Also, a user-friendly computer package could be developed on the basis of the proposed model.

Acknowledgement

The author would like to thank the learned reviewers for their constructive comments and valuable suggestions for the improvement of the paper over its earlier version.

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