International Journal of Scientific & Engineering Research Volume 2, Issue 8, August-2011 1

ISSN 2229-5518

Comparison of Performance Evaluation of Reverse Supply Chain in Information Technology and Telecommunication Equipment Industry

K.ARUN VASANTHA GEETHAN , Dr.S.JOSE, C.SUNILCHANDAR

Abstract- Due to the increasing product variety and shorter life cycles, many electronic products end up in disposal sites. The development in the electronics sector is geared towards growing miniaturization, more complex and compact products, all of which stand in the way of economical and ecological recycling. These factors in addition to the inherent complexity of reverse supply chains (RSC) due to the uncertai nties associated with the quantity, quality, and timing of returns make returns management all the more complicated. Information technology and telecommunication equipment industries have started to realize that RSC can be used to gain competitive advantage. Effective performance management is an important aspect of the RSC initiative, and is the key to recognizing the benefits of efficient supply chain management systems.This research spotlights on this particular problem from a information technology and telecommunication equipment industries perspective, as it poses the greatest challenges in handling returns due to the presence of high clock speed products and greater return volume and variability. In this research, Performance Evaluation Analytic for Reverse Logistics Methodology is developed to facilitate decision making from the perspective of an enterprise engaged in reverse logistics. It explores the various reverse logistics functions and product lifecycle stages. It also devel ops some key business strategies and performance metrics that can be employed to be successful in returns handling. Deployment of this methodology in their organizations not only provides them with a real world assessment of what strategies, reverse logistics functions, product lifecycle stages, or key performance indicators impact the Reverse Logistics Performance Value, thereby allowing them to continuously improve their returns management capabilities but also helps us to compare the efficiencies of the two organisation under study.

Keywords: reverse supply chain, shrinking, performance evaluation analytic, reverse logistics functions, strategies, product lifecycle stages, reverse logistics performance value.

—————————— ——————————

I. INTRODUCTION

everse logistics is defined as “the process of planning, implementing and controlling the efficient, cost effective flow of materials in process inventory, finished goods and related information from the point of conception to the point of origin for the purpose of recycling value or proper disposal [1]. Reverse logistics concentrates on those streams where some value can be recovered. It is the process of managing the flow of returned products and information from the point of consumption to the point of origin. According to a recent study, reverse logistics is one of the twenty one top warehousing trends in the twenty first century (Brockmann,1999). Industries have started to realize that the reverse logistics can be used to gain competitive advantage. An evaluation framework, which incorporates determinants and dimensions of reverse logistics, would be useful in configuring the post activities associated with the EOL computers. There are number of variables affecting the reverse logistics, some of these are interdependent among each other. The objective of the paper is to develop a quantitative methodology for evaluating the reverse supply
chain performance in information technology and telecommunication equipment industry and to compare the same with two case studies. The quantitative methodology was developed with the help of Analytic Network Process. Analytic Network Process (ANP) is a technique that captures the interdependencies between the criteria under consideration, hence allowing for a more systematic analysis [2]. It can allow inclusion of criteria, both tangible and intangible, which has some bearing on making the best decision. Further, many of these factors have some level of interdependency among them, thus making ANP modeling better fit for the problem under study. The ANP model presented in this paper structures the problem related to selection of an alternative for the reverse logistics option for EOL computers in a hierarchical form and links the determinants, dimensions and enablers of reverse logistics with different alternatives.

II. LITERATURE REVIEW

Stock (1992) recognized the field of reverse logistics as being relevant for business and society in general. Kopicki, Berg, Legg, Dasappa, and Maggioni (1993) paid attention to

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the field and pointed out opportunities on reuse and recycling. Fleischmann, Bloemhof-Ruwaard, Dekker, van der Laan, van Nunen, and Van Wassenhove (1997) had given a comprehensive review of literature of the quantitative models in reverse logistics. Reverse logistics programs in addition to the various environmental and the cost benefits can proactively minimize the threat of government regulation and can improve the corporate image of the companies (Carter & Ellram, 1998). Reverse logistics is the process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal (Rogers & Tibben-Lembke, 1998). A reverse logistics defines a supply chain that is redesigned to efficiently manage the flow of products or parts destined for remanufacturing, recycling, or disposal and to effectively utilize resources (Dowlatshahi, 2000).
Thus, the reverse logistics focuses on managing flows of
material, information, and relationships for value addition as 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, & Wassenhove, 1995; van der Laan, Dekker, & Van Wassenhove, 1999) single-use cameras (Toktay, Wein, & Stefanos, 2000), jet engine components (Guide & Srivastava 1998), cellular telephones (Jayaraman, Guide, & Srivastava, 1999), automotive parts (van der Laan,
1997) and refillable containers (Kelle & Silver, 1989). In all
the cases, one of the major concerns is to assess whether or

not the recovery of used products is economically more attractive than the disposal of the products [3]. Reverse logistics are also extensively practiced in the computer hardware industry. IBM and Dell Computer Corporation 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, Johnson, and McDonell (2001) 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 to support and enhance environmental performance. Moyer and Gupta (1997) have conducted a comprehensive survey of previous works related to environmentally conscious manufacturing practices, recycling, and the complexities of disassembly in the electronics industry. Gungor and Gupta (1999) have presented the development of research in environmentally conscious manufacturing and product recovery (ECMPRO) and provided a state-of-the-art survey of the published work in this area. Veerakamolmal and Gupta (1997) have discussed a technique 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, 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 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%. 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 is observed that there is not much work reported till date for multi-criteria decision making in the decision making related to reverse logistics practices in the case of EOL computers.

III. PROBLEM DESCRIPTION

The legislations and the economic benefits of reverse logistics have forced organizations to take a new look at their operations. Due to intense competition and stringent environmental regulations, it is quite difficult to sustain successful business operations just by handling the forward supply chain effectively. Hence, it is imperative that companies begin to effectively manage their reverse supply chains also, thereby developing into a successful closed loop organization. Developing accurate and consistent performance measures is critical because it directly reflects on quality of the system and its effectiveness. The development of accurate and measurable performance metrics represents a major step in adopting a holistic approach to reverse supply chain management. As the information technology and telecommunication equipment industry is more complex than other industries in terms of uncertainty of product returns, this research will concentrate specifically on the information technology and communication equipment industry namely the tronic products such as computers and laptops. tronics is the basic technology for many new products
he industry. Due to the increasing product variety and
rter life cycles, many electronic products end up in posal sites.

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Step 4: Identify the product life-cycle stages

Introduction (IN)

Obsolete (OB)

Life – cycle

Stages

Growth (GR)


Return Rate Variability

Fig.1. Lifecycle- Variability Matrix for different industries

Decline (DE)

Maturity (MA)



IV. METHODOLOGY
The case study approach was selected because it is an ideal method when a holistic, in-depth investigation is needed. This case study approach helps to gather the facts from the real world and explain the linkages between causes and
effects. One such benefit is that the information provided is

Fig.3. Life cycle stages

Step 5: Determination of competitive strategies involved in


RL
usually more concrete and contextual, specifically due to the in depth analysis it offers of the case being studied.

A. Algorithm

Customer

Satisfaction (CS)

New Technology

Implementation (NT)

Step 1: Start

Step 2: Determine the goals and objectives of the organization pertaining to RL

Return Policy (RP) Strategic Alliance

Formation (SA)

Goals

Profit Maximization

Knowledge

Management (KM)

Value Recovery

(VR)


Disposal of Hazardous wastes

Fig.2. Goals


In order to maximize the profit, one has to improve the efficiency of the system which is achieved only by measuring the performance of the system.

Step 3: Drivers of Reverse Logistics are determined

Economic Legislation

Factors

Fig 4. Strategies




Step 6: The various functions involved in RL and their performance metrices are identified

Drivers

Business Customer Service

Strategy Initiatives

Fig 5. Functions and Performance Metrices

Fig.3. Drivers

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Step 7: Form pair-wise matrices with respect to the inter and intra dependencies between the clusters.

Product life-cycle stages

Step 11: Determine the performance values at the measures for each RL function within the organization. This is found out from the Converged Super matrix

GK SS AR TN

RL Strategies RL Functions

RL Performance Metrics

Fig 6. Cluster Relationship diagram for pair wise matrices

GK SS AR TN

Table 1 Converged Super Matrix

Step 12: Calculation of performance metrices – formulae i) Return Value (RV)

Therefore it is clear that pair-wise comparison matrices have to be formed between:
i) The performance metrices with respect to various
Where,

RV = n * N * C

functions
ii) The RL functions with respect to a particular
function
iii) The RL functions with respect to various strategies
iv) The RL strategies with respect to various functions
v) The RL strategies with respect to various product life cycle stages
vi) The product life-cycle stages with respect to
various strategies
Step 8 : . Once the weights are calculated, the next sub step is to determine the Z-Vector value for the reverse logistics process with respect to all the strategies
Step 9: Develop Super matrix from Pair-wise comparison matrices of interdependencies

Life-cycle stages

Strategies

Functions

Life-cycle

stages

Strategies

Functions

Step 10: Converge the Super matrix using WIMS software available at http://wims.unice.fr/wims/wims.cgi. Converging is the process of multiplying the matrix by itself repeatedly till constant results are obtained. It occurs only at an odd (2k+1)th iteration (k is any integer).
n is the Number of Reverse Logistics Locations
N is the Number of returned products
C is the cost of one returned product
Gate keeping Effectiveness (GE)
Gate-keeping effectiveness is a qualitative aggregate measure that helps an organization compare its practices to some of the best practices obtained from academic research and industry.

BEST PRACTICE

Clear and visible return policies to reduce the number of defective products into the RSC

Use of dedicated and skilled labour for return product inspection

Use of latest equipment for checking the reliability of the product

Use of multiple channels such as phone and internet to provide support

Employ programs to reduce idle time of trucks and products at Gate Keeping

Table 2. Checklist for GR

ii) Warehousing Effectiveness (WE)

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Warehousing Effectiveness is an aggregate measure of warehousing performance of an organization in handling returns.

practices in environmental compliance, and ensures that the investments made in compliance initiatives are best leveraged

Table 4. Checklist for EE

Table 3. checklist for WE

iii) Carrying cost Percentage (RC)

Fixed costs + Variable costs

(RC) = -------------------------------------

Average Value of return inventory

Value Recovered

RE = -----------------------------

Resources used

v) Recovery Rate (RR)

RR = 1-(S/N)

Where, S is the number of items scrapped per unit time

vii) Overall Vehicle Effectiveness (VE) It is also a qualitative measure.

Table 5. Checklist for VE

N is the total number of items inducted into the asset recovery process

vi) Environmental Effectiveness (EE)

Environmental conformance effectiveness is an easy to use and implement qualitative measure that combines the best

viii) Return Transit Time (RT)

T

RT = ---------- N

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Where,

T is the total time spent by a product return in transit

N is the number of products entering the reverse supply chain

Step 13: Categorize the performance within the electronics industr

TRANSPORTATION (TN)

VE=5 1.00 40 1.00

VE=4 0.80 50 0.50

VE=3 0.60 60 0.00

VE=2 0.40

VE=1 0.20 ratings at the measures

Table 6. Performance Scale for GK

y in the form of scales to

assign

perfor mance

Table 9. Performance Scale for TN

GATE-KEEPING (GK)

RV (Rs/day)

GE

Value

Rating

Range

Rating

0 1.00 GE=5 1.00

12000 0.50 GE=4 0.80

24000 0.00 GE=3 0.60

GE=2 0.40

GE=1 0.20

Calculate the performance score at the measure

Step 14:

Performance Score at the RL measure: Sm = PR * Wm * Wf

SORTING AND STORING (SS)

PR – Performance rating of the firm

Wm- Metrices weight

Wf – Functions weight

Performance Scale for SS

Tabl e 7.

ASSET RECOVERY

Step 15: Compute the reverse logistics performance value (RLPV) by summing up all the performance scores at the RL measures

Table 10. RLPV

25

1.00

0

1.00

EE=5

1.00

12.5

0.50

0.35

0.50

EE=4

0.80

0

0.00

0.70

0.00

EE=3

0.60

EE=2

0.40

EE=1

0.20

Table 8. Performance Scale for AR

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TN Table 13.Pair-wise comparison matrix to determine the effect of RL functions on each other under Customer Satisfaction

strategy-Company A

Step 16: Stop

V. CASE ILLUSTRATION

The model presented in this paper has been evaluated in two information technology and telecommunication equipment industry A and B, which were interested in the implementation of the reverse logistics practices.

A. Metrices weight-Company A

Table 14.Pair-wise comparison matrix to determine the relative importance of strategies under Gate-keeping function- Company A

GK

RV

GE

Weight

RV

0.10

0.10

0.10

GE

0.90

0.90

0.90

Table.11. Metrices weight for Gate keeping function

B. Functions weight- formation of super matrix- Company A

The pair wise comparison matrices for various strategies as mentioned in methodology is calculated.

Table 12 Pair-wise comparison matrix of relative importance of functions with respect to Gate-keeping function-Company A

Table 15.Pair-wise comparison matrix to determine the relative importance of strategies under Introduction lifecycle stage-Company A

Table 16.Pair-wise comparison matrix to determine the relative importance of lifecycle stages under Customer Satisfaction strategy-Company A

CS

IN

GR

MA

DE

OB

Weight

IN

1

1/5

1/3

7

8

0.18

GR

5

1

3

7

8

0.47

MA

3

1/3

1

6

7

0.25

DE

1/7

1/7

1/6

1

2

0.05

OB

1/8

1/8

1/7

1/2

1

0.04

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Table 17.Z-Vector to determine the total contribution of RL functions with respect to Customer Satisfaction strategy- Company A

0.41 0.35 0.23 0.03 0.04 0

0.15 0.06 0.03 0.03 0.04 0

0.04 0.06 0.08 0.24 0.3 0

0.11 0.09 0.05 0.12 0.06 0

0.26 0.17 0.22 0.13 0.06 0

0.03 0.27 0.38 0.45 0.49 0

* = 0 0 0 0 0 0.21

0 0 0 0 0 0.06

0 0 0 0 0 0.15

0 0 0 0 0 0.09

Z-Vector value for GK function with respect to CS strategy = [(1*0.65) + (0.68*0.07) + (0.67*0.16) + (0.2*0.34)]

Table 18.Super matrix-Company A

Table 20.Converged Super Matrix (M2K+1 = M369) -Company A

IN

GR

MA

DE

OB

CS

IN

GR

MA

DE

OB

CS

NT

RP

SA

KM

VR

GK

SS

AR

TN

Table 19.Column stochastic super matrix (M) -Company A

C. Metrices weight-Company B

GK

RV

GE

Weight

RV

1

9

0.90

GE

1/9

1

0.10

Table.21. Metrices weight for Gate keeping function

D. Functions weight- formation of super matrix- Company B

The pair wise comparison matrices for various strategies as mentioned in methodology is calculated.

Table 22 Pair-wise comparison matrix of relative importance of functions with respect to Gate-keeping function- Company B

GK

SS

AR

TN

Weight

SS

1

4

8

0.67

AR

1/4

1

7

0.27

TN

1/8

1/7

1

0.06

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Table 23.Pair-wise comparison matrix to determine the effect of RL functions on each other under Customer Satisfaction strategy- Company B

Table 27.Z-Vector to determine the total contribution of RL functions with respect to Customer Satisfaction strategy- Company B

* =

Table 24.Pair-wise comparison matrix to determine the relative importance of strategies under Gate-keeping function- Company B

Z-Vector value for GK function with respect to CS strategy = [(1*0.65) + (0.68*0.07) + (0.67*0.16) + (0.2*0.34)]

Table 28.Super matrix- Company B

Table 25.Pair-wise comparison matrix to determine the relative importance of strategies under Introduction lifecycle stage- Company B

IN

CS

NT

RP

SA

KM

VR

Weight

CS

1

3

8

4

8

9

0.44

NT

1/3

1

7

4

7

9

0.29

RP

1/8

1/7

1

1/5

1/2

1

0.04

SA

¼

1/4

5

1

4

6

0.15

KM

1/8

1/7

2

¼

1

2

0.05

VR

1/9

1/9

1

1/6

1/2

1

0.03

Table 26.Pair-wise comparison matrix to determine the relative importance of lifecycle stages under Customer Satisfaction strategy- Company B

IN

GR

MA

DE

OB

CS

IN

GR

MA

DE

OB

CS

NT

RP

SA

KM

VR

GK

SS

AR

TN

Table 29.Column stochastic super matrix (M) - Company B

CS

IN

GR

MA

DE

OB

Weight

IN

1

1/3

7

9

9

0.33

GR

3

1

8

9

9

0.51

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0 0 0 0 0 0.03

0 0 0 0 0 0.02

0.44 0.31 0.48 0.11 0.04 0

0.29 0.05 0.22 0.2 0.47 0

0.04 0.03 0.05 0.05 0.03 0

0.15 0.43 0.07 0.22 0.23 0

0.05 0.13 0.04 0.04 0.15 0

0.03 0.04 0.15 0.37 0.07 0

0 0 0 0 0 0.21

0 0 0 0 0 0.16

0 0 0 0 0 0.12

0 0 0 0 0 0.03

Table 30.Converged Super Matrix (M2K+1 = M49) - Company B

n is the number of reverse logistic centre = 10

N is the number of return products in Gate-keeping per unit day = 13

C is the cost of a single returned product = Rs.150

Therefore, RV = 10 * 13 * 150

RV = Rs.19500/ day

Table 31.Gate keeping Effectiveness (GE)

GK

SS

AR

TN

GK

0.40

0.40

0.40

0.40

SS

0.31

0.31

0.31

0.31

AR

0.24

0.24

0.24

0.24

TN

0.09

0.09

0.09

0.09

E. Performance value of the firm

Return Value

For -Company A, RV = n * N * C Where,

n is the number of reverse logistic centre = 8

N is the number of return products in Gate-keeping per unit day = 10

C is the cost of a single returned product = Rs.150

Therefore, RV = 8 * 10 * 150

RV = Rs.12000/ day

For Company B, RV = n * N * C Where,

Checklist for evaluating performance rating of Gate-keeping

Effectiveness

We have rated each parameter in the check list as 0.2

according to likert’s scale.

Therefore, performance rating of GE in Company A = 4 * 0.2

= 0.8

Similarly performance rating of GE in Company B = 1.

F. Calculation of RLPV

Table 32. RLPV-Company A

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survey sample size is necessary.

VII. REFERENCES

Table 33. RLPV-Company B

[1] Ahmed, A., H. Abdalla. 2002. An intelligent system for performance measurement selection. In Proceedings of the institution of mechanical engineers591-606.

[2] de Brito, M. P., R. Dekker. 2003. A framework for reverse logistics. In ERIM report series research in management.

[3] Fleischmann, M., H. R. Krikke, R. Dekker, and S. D. Flapper.

2000. A characterization of logistics networks for product recovery. Omega 28: 653-666.

[4] Hillegersberg, J., R. Zuidwijk, J. van Nunen, and D. van Eijk.

2001. Supporting return flows in the supply chain. Communications of the ACM 44, no. 6: 74-79.

[5] Krikke, H., A. van Harten, and P. Schuur. 1999. Business

case oce: Reverse logistic network re-design for copiers. OR Spektrum 34, no. 3: 381-409.

[6] Saaty, T. L. 1999. Fundamentals of the analytic network process. In ISAHP.

[7] V.Ravi, Ravi Shankar & M.K.Tiwari, Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach, Computers & Industrial Engineering 48,(2005),pp 327-356.

VI. CONCLUSION

The function weights along with the measure weights from the pairwise matrices and the performance ratings developed for each of the two case studies have been illustrated in table 32 and table 33. In addition, the actual performance metric values obtained from the interview process have also been included. Based on the formulations developed, the performance score of Company A was obtained to be 0.6744 or 67.44% and that of Company B to be 0.5836 or 58.36 % of the industry average standards. In the case of this research, it is important to note here that the data used is skewed and that these figures do not accurately represent the information technology and telecommunication equipment industry standards due to the fact that only two companies were used for data collection. Ideally, in order to validate the results, a bigger

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