Expert answer:Team Management
Expert answer:Read the attached articles. Read the article associated with this week’s forum and watch this video about Team Management. Link: http://youtu.be/Gp39lhald4kDo you agree or disagree with the McKinsey survey used by the authors that supply chain, sales, and marketing managers do not understand the impact they have on one another and therefore, are the most common barrier to collaboration for resolving the major supply chain trade-offs? Explain and discuss.The post should be at least 250 word
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is_your_top_team_undermining_your_supply_chain.pdf
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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 endof-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: ravi1@dms.iitd.ernet.in
International Journal of Production Research
ISSN 0020–7543 print/ISSN 1366–588X online ß 2007 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/00207540601115989
2
V. Ravi et al.
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 environment. 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).
Selection of a reverse logistics project for end-of-life computers
3
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
4
V. Ravi et al.
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
Selection of a reverse logistics project for end-of-life computers
5
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 interdependencies 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.
6
V. Ravi et al.
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 Z ¼ Pk (wj diþ , wj di )
subject to
aij xj þ di diþ bi
xj þ di ¼ 1
xj ¼ 0 or 1
for i ¼ 1, 2, . . . , m, j ¼ 1, 2, . . . , n
for i ¼ m þ 1, m þ 2, . . . , m þ n, j ¼ 1, 2, . . . , n
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 j ¼ 1, 2, . . . , n reverse logistics
projects.
Pk Some K pre-emptive priority (P14P24, . . . , Pk), for i ¼ 1, 2, . . . , m
reverse logistics project goals.
di Deviation variables for goals.
diþ , di The ith positive and negative deviation variables for i ¼ 1, 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 di .
7
Selection of a reverse logistics project for end-of-life computers
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 …
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