Methodology of thesis 'research barriers in the implementation of reverse logistics'

Critical barriers in Implementing Reverse Logistics in Manufacturing Sector: evidence from Pakistan

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Methodology


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3.1 Research Approach

There are two primary approaches to the conduct a research project and generate

knowledge. They are quantitative and qualitative methods. Each of these has it strengths and

weaknesses. Multiple research method bridges the gap and makes use of the strengths of each

method.

It is very important to distinguish between the qualitative and the quantitative approach to

help in identifying the design of the research and how it can be carried out. A qualitative method

is based on the interpretations of researcher and often depends on words and descriptions to

create a deeper understanding of a specific area. Interviews and observations are examples of

qualitative analysis. While the quantitative method is based on numerical and statistical data, it is

a convenient approach to manage a large amount of data which can easily be presented in figures

and tables. Since not everything can be measured in a numerical way, the qualitative approach

sometimes needs to be applied. The goal of quantitative approach is to answer research questions

or test hypotheses (Hopkins, 2000). It typically tends to learn ‘what’, ‘how much’ and ‘how

many’ (Pinsonneault & Kraemer, 1993).In our research we have adopted the approach of

quantitative investigation (Abdulrahman, Gunasekaran and Subramanian, 2012; Daugherty,

Richey, Genchev and Chen 2004) for deriving the results and checking the consistency and

assessment of independent variables management barriers, financial barriers, policy barriers and

infrastructure barriers on reverse logistics. Quantitative examination is the systematic

examination of consider variables by means of factual, scientific or computational systems.

3.2 Research Purpose

Our study is conducted on explanatory purpose (Torre, Alvarez, Sarkis and Diaz, 2010)

for finding out the impact and influence of critical barriers in the implementation of reverse


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logistics in Pakistan. Explanatory investigation is conducted for clarifications of specific

connections. Hypothesis examination gives a comprehension of the connections that exist

between variables. This study will help the reverse logistics decision-making and practice as well

as for better understanding of reverse logistics practices.

3.3 Research Design

Correlational technique (Abdulrahman, Gunasekaran and Subramanian, 2012) will be

used for checking the relationship of most impact and influence obstacle with regard of reverse

logistics implementation barriers in Pakistan.

3.4 Data Source

This research conducted by utilizing primary data and conducted by sending the

questionnaire to the manufacturing sector of Karachi. According to Dillman (2000), self-

administered questionnaires are an accepted social science research instrument. They are one of

the oldest methods in the researcher’s repertoire, and the method with which the general public is

most familiar (Dane 1990). Often they are the only feasible way to reach a number of reviewers

large enough to allow statistically analysis of the results. Sufian (1990) classifies questionnaires

into two types: structured and unstructured questionnaires. A completely structured questionnaire

is one in which all respondents are asked the same set of predetermined questions with fixed

wording and sequence. Unstructured questionnaires on the other hand, although seeking

standardization in the sense of obtaining true variations among the respondents through their

responses, are based on the assumption that it is not possible to frame the same set of

predetermined questions that have identical meanings for extremely heterogeneous respondents.


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3.5 Sample Size

As this study is concerned, a small sample group manufacturing concerns in Karachi

were selected for the careful review of the instrument. The mailing list was randomly selected

from the full mailing list. The examinations of this experiment require the ample sample size for

this investigation. Due to time limitation and budgetary constrain the sample size of 50 were

considered sufficient for this data analysis. The survey covered certified manufacturing firms in

Karachi, Pakistan. The term ‘firm’ here refers to companies as well as individual units or sites

within companies.

3.6 Data collection Tool

In the process of solving any research problem, collecting the proper data is a must. For

data collection structured questionnaire was used. Sufian (1990) classifies questionnaires into

two types: structured and unstructured questionnaires. A completely structured questionnaire is

one in which all respondents are asked the same set of predetermined questions with fixed

wording and sequence. The constructed survey was selected towards answering the problem

statement of this research. Questions under each construct were worded to cover certain

construct comprehensively.

We used the questionnaire developed with the questions regarding the most influence and

determining obstacles for the implementation on reverse logistics and it will be based on Likert

scale 1= Strongly Disagree 2= Disagree 3=Unsure 4= Agree 5= strongly Agree.


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3.7 Sample Technique

The survey covered certified manufacturing firms in Karachi, Pakistan. The term ‘firm’

here refers to companies as well as individual units or sites within companies. The execution of

survey research method included the following steps:

1) Selection of study area was done through simple random sampling

2) Respondents were selected by using Convenient Sampling.

3) Data collection was carried out by email method through well- structured questionnaire.

4) The ambiguity or uncertainty with the question wording, structure, design, comprehension,

layout and/ or sequence was reduced by pilot survey.

5) Reliability of data and response rate were increased through continuous follow. Convenience

sampling is utilized within this study to conduct survey at his/her own comfort of the respondent

for safe time. Those who were not available at offline survey were sent them emails and by

Facebook and LinkedIn.

3.8 Statistical Technique/Tools

We will apply Factor and Regression analysis for obtaining the results. Factor analysis

reduces the data and prepares it for its proposition of regression model. Reliability test is used to

ensure internal uniformity of data through Cronbach’s alpha. Factors will be made to further

evaluate with the regression analysis. To analyze the empirical data, several statistical methods

were employed. First, Cronbach’s Alpha and Corrected Item-to-Total Correlations were used in

assessing the internal consistency of each construct. The mean was used to find out the trend of

each attribute under each construct. Cronbach's α (Alpha) is a coefficient of reliability. It

estimates the consistency (or repeatability) of the survey instrument measurement for a given

concept. It is an indication how well a set of items measures the same concept. Theoretically,


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alpha varies from zero to 1, including negative values. Higher values of alpha are more desirable.

As a rule of thumb a reliability of 0.70 or higher is required.

Below, for conceptual purposes, is the formula for the standardized Cronbach's Alpha:

Where:

N: is equal to the number of items,

c-bar: is the average inter-item covariance among the items and

v-bar: equals the average variance.

3.9 Model Framework

The concept of reverse logistic can be examined within the framework of De Brito and Dekker

(2003) who identified five dimensions that includes:

• The return reasons (why-returning).

• Driving forces (why-receiving).

• The type of products and their characteristics (what)

• The recovery processes and recovery options (how)

• The actors involved and their roles (who).Popper (1994) defines a framework as a set of basic

fundamental principles, which can help to promote discussions and actions. The authors have

defined framework as a set of simplified theoretical principles and practical guidelines for

implementation and adoption, which can enhance the chance of success that are easy to

understand.


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RL = α + β1 (MB) + 2 (FB) + 3 (PB) + (IFB) + ε

RL= Reverse Logistics

MB= Management Barriers

FB= Financial Barriers

PB= Policy Barriers

IFB= Infrastructure Barriers

ε = Error term

3.10 Variable Descriptions

Based on the literature review, this study investigates the real barriers relying upon the

manufacturers’ point of view of the major problems which hinder the implementation of reverse

logistics in Karachi and or Pakistan at large. These barriers are classified and discussed below:

The variables are presented in a gist here in the following table-1.

Sr. No. Barriers1 Policy2 Management3 Financial4 Infrastructure

Policy Barriers

This contains all those barriers which are related to either the government laws or the

laws enforced by the international organizations. It contains legal issues, a company's RL

practices, transparency etc. (Table-4). Government Rules & legislation should be a major driver

and not become a barrier for company’s Regulations, because RL is a complicated and

sophisticated system, since it involves environmental, economic and social aspects Furthermore,

a system that is not economically justifiable will not be successful in the long run.


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Management Barriers

These are the barriers in the implementation of reverse logistics due to lack of interest

and knowledge of top decision makers of the organizations. Besides when an entity's

organizational capabilities are strong, it can progress smoothly, but when they are weak it can

find it difficult to get the job done, making errors due to underestimating the problems. An

organization's capability is its ability which can win over the barriers in the implementation of

RL practices. This consists of the strategy of a company, its strategic plans, its commitment,

employees' hiring and skills development, a working system of performance appraisal and

supporting programs. The following table shows a gist of various types of management barriers

(Table-2)

Financial Barriers

All such activities which support Reverse logistics, like human resource management and

training, tracking system and adherence to government policies are included in financial barriers

and are very critical for a firm to obtain benefits of RL. The barriers as reported in various

literature are given under (Table-3).Barriers due to inappropriate financial planning or situation

of organizations. Besides this, shareholders have invested money in a company, they have a

vested interest in its performance and can be a powerful influence on company policy;

sometimes these influences turn into barriers in the implementation of RL in a company.

Infrastructure Barriers

Reverse logistics cannot be implemented without proper infrastructure. Like proper

warehousing, recycling facilities, coordination of all departments, smooth transportation and

logistics. Smooth adoption of reverse logistics required an internal structure where it can be

adopted and functions smoothly. The internal hurdles or structural obstacle are referred to as


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infrastructural barriers. A careful design and control of infrastructure and adequate transportation

systems is crucial in reverse logistics. In a broader perspective, the above considerations point at

distribution management issues in reverse logistics. In more traditional contexts distribution

logistics has been structured in many ways, including internal versus, external and inbound

versus outbound transportation. Sometimes all the barriers are not evident enough to be noted

such barriers have not been either described in most of the earlier research papers nor have been

mentioned here in this paper.

Table -1

Management Barriers in the RL implementation

Source Type of Barriers

Rogers and Tibben-Lembke (2001),

Pricewaterhouse Coopers’ report (2008),

Zhou et al. (2007),

Ravi and Shankar (2005),

Chung and Zhang (2011)

Importance of reverse logistics relative to other issuesCompany policesCompetitive issuesManagement commitment/little senior management attentionPersonnel resources (Training, poor level of technical knowledge)Difficulties in extended producer responsibility across countriesLack of appropriate performance management systemLack of shared understanding of best practicesLack of strategic planning and structure for reverse logistics


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Table -2

Financial Barriers in the RL implementation



Zhou et al. (2007),

Ravi and Shankar (2005),

Lau and Wang (2009)

Table -3

Policy Barriers in the RL implementation


Rogers and Tibben-Lembke (2001), Ravi

and Shankar (2005), Zhou et al.

(2007), Lau and Wang (2009), Chung

and Zhang (2011); Miao et al. (2011),

Rahman and Subramanian (2012),

Chaabane et al. (2012); Koh et al.

(2012) (2009)

Financial resources/constraints/ funds

for training/return monitoring

system/storage and handling

Preferential tax policies

Legal issues/lack of supportive policies

Loop holes in government regulations

Lack of enforceable law

Lack of RL management practices

Lack of directives to motivate manufacturers

Lack of awareness in environmental regulations

Customers not informed of take back channels


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Table -4

Infrastructure Barriers in the RL implementation



Ravi and Shankar (2005), Zhou et al.

(2007);

PricewaterhouseCoopers’ report (2008),

Chung and Zhang (2011), Lau and Wang

(2009),

Rahman and Subramanian (2012)

Karachi was the focus of this study because it has a very well communication network and

infrastructure, including logistics facilities as compared to other cities of Pakistan. The selection

of manufacturers included from a variety of manufacturing units of diverse nature in Pakistan's

industrial sectors.

According to this research the following table 5 depicts the nature of barriers.

Lack of systems/EDI standards/

Underdevelopment of recycling technologies

Coordination and support/collaboration or

reluctance of support from members

Limited forecasting and planning/

Lack of In-house facilities


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Table-5


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Chapter # 4

Research Analysis and

Results


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4. Data Analysis and Results

4.1 Introduction

This Chapter comprises in depth analyses of the conducted research about “Critical barriers in

Implementing Reserve Logistics in manufacturing sector evidence from Pakistan. It is concerned

with the data analysis and interpretation. Following the data collection, the data preparation

process suggested by Malhotra (1993) and Churchill (1999) was implemented to ensure data was

cleaned before performing further statistical analysis. In this research work, the researcher used

SPSS statistical package software for the purpose of data analysis. It is general statistical

software tailored to the needs of social scientists and the general public. It provides over 50

statistical processes, including regression analysis, correlation and analysis of variance.

Compared toother software, it is more intuitive and easier to learn; the trade-off is less flexibility

and fewer options in advanced statistics than some other statistical software like SPlus, R and

SAS. The psychometric multi item scale, i.e. Likert Scale was chosen for answering the

questions. The questionnaire was sent via. Electronic mail and to the sample group. The

questionnaire was developed based on 10 most relevant constraints taken from research papers.

The questionnaire consists of formalized and pre-specified set of questions designed to obtain

responses from the potential respondents. The psychometric multi item scale, i.e. Likert Scale

was chosen for answering the questions.


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4.2 Reliability and Validity Assessment

Measurement, by definition, is simply the assignment of numbers to events, objects or

individuals, according to specified rules. Whether the attribute being measured is physical or

psychological, “hard” or “soft”, the focus of measurement is necessarily on the “something” that

is measured. The goodness of the measurement or the truthful of the measurement results is a

corner stone in the quality of measurement process and therefore to the conclusion made. The

assessment of the measurements can be achieved by considering the reliability and validity of the

data under research. These two concepts of assessment are discussed in the following section in

more detail.

The goodness of the measurement or the truthful of the measurement results is a corner

stone in the quality of measurement process and therefore to the conclusion made. The

assessment of the measurements can be achieved by considering the reliability and validity of the

data under research. These two concepts of assessment are discussed in the following section in

more detail.

The term ‘Reliability’ is a concept used for testing or evaluating all kinds of research

methods; quantitative, qualitative or others. Reliability is a kind of assessment concept that has

issues of consistency of measures (Bernard, 2000). In other word, it is concerned with

minimizing the errors and biases in the study so that if another researcher duplicated the same

procedures using the same case study, then the results and conclusions would ideally be the

same.


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As in most of empirical studies, Cronbach’s Alpha (Terence Jackson, 2001) was found to

be the most acceptable statistical technique to measure the reliability of a given construct. The

Crobach’s Alpha coefficient (Cronbach, 1951; Nunnally, 1967) varies between 0 (no correlation

and therefore no internal consistency) and 1 (perfect correlation. Typically, reliability

coefficients of 0.70 or higher were found to be the most acceptable cut off value (Nunnally,

1978). The reliability is affected by number of items in a scale and sample size (Hayes, 1992).

Therefore, permissible alpha values can be slightly lower (0.60) for newer scales Nunnally

(1978). The Crobach’s Alpha coefficient with a value of (0.7) is considered in data analysis as

accepted cut off value in this study.

Validity concerns the crucial relationship between concept and indicator. Unlike

reliability that focuses on the performance of empirical measures, validity is usually more of a

theoretically-oriented issue because it inevitably raises the question, “valid for what purpose?”

Validity is crucial to an instrument’s credibility; it is an indication that the instrument is indeed

measuring what it was designed to measure and that it is measuring it accurately.

4.3 Factor Analysis

Factor analysis is frequently used to develop questionnaires: after all if it is wanted to

measure an ability or trait, the need is to ensure that the questions asked relate to the construct

that is intended to be measured. KMO and Bartlett’s test of sphericity produces the Kaiser-

Meyer-Olkin measure of sampling adequacy and Bartlett’s test. The value of KMO should be

greater than 0.5 if the sample is adequate. SPSS Output shows an abridged version of the R-

matrix. The top half of this table contains the Pearson correlation coefficient between all pairs of

questions whereas the bottom half contains the one-tailed significance of these coefficients. This

correlation matrix can be used to check the pattern of relationships. The KMO statistic varies


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between 0 and 1. A value of 0 indicates that the sum of partial correlations is large relative to the

sum of correlations, indicating diffusion in the pattern of correlations (hence, factor analysis is

likely to be inappropriate). A value close to 1 indicates that patterns of correlations are relatively

compact and so factor analysis should yield distinct and reliable factors. Kaiser (1974)

recommends accepting values greater than 0.5 as acceptable (values below this should lead you

to either collect more data or rethink which variables to include). Furthermore, values between

0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are

great and values above 0.9 are superb.

4.4 Confirmatory Factor Analysis

One of the routes to construct validation of a test is predicting the test's factor structure

based on the theory that guided its construction, followed by testing it. The method of choice for

such testing is often confirmatory factor analysis (CFA). In CFA, the predicted factor structure of

a number of observed variables is translated into the complete covariance matrix over these

variables. Next, this matrix is adjusted to the actual covariance matrix, and subsequently

compared with it. The discrepancy between the two, the "good-ness of fit" (GOF), is expressed

by a number of indices. In CFA, a model is identified if all of the unknown parameters can be

rewritten in terms of the variances and covariances of the x variables. After initial item writing

and data collection, researchers articulate the measurement model within CFA-capable software.

Software packages have made this process increasingly easy, no longer requiring familiarity with

matrix algebra and esoteric programming syntax. CFA is a method used to validate the factor

construction of a set of experiential variables. Confirmatory factor analysis consents the

researcher to check or test the hypothesis that an association between experiential variables. It is


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a multivariate statistical technique that is used to test how well the dignified factors signify the

number of constructs.

• RMSEA (root mean square error of approximation): based on 2, df and N. This index was

devised by Steiger (1990). Its formula is:

By dividing by df, RMSEA penalizes free parameters. It also rewards a large sample size

because N is in the denominator. A value of 0 indicates perfect fit. Hu & Bentler (1998, 1999)

suggested ≤ .06 as a cut- off value for a good fit.

SRMR (standardized root mean square residual: Jöreskog & Sörbom, 1988). To calculate this

index, the residuals (Sij - Iij) in the residual correlation matrix are squared and then summed; this

sum is divided by the number of residuals q, which equals p.(p+1)/2, where p is the number of

variables, including the diagonal with communalities, and the square root of this mean is then

drawn. (S denotes sample correlation matrix, and I de-notes implied correlation matrix.)

A value of 0 indicates perfect fit. Hu & Bentler suggest a cut-off value of ≤ .08 for a good fit.

Notice that Ϟ2 is not used to calculate SRMR.

TLI (Tucker-Lewis index, 1973), also known as NNFI (non-normed fit index), similar to the

next index presented, belongs to the class of comparative fit indices, which are all based on a

comparison of the Ϟ2 of the implied matrix with that of a null model (the most typical being that


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all observed variables are uncorrelated). Those indices that do not be-long to this class, such as

RMSEA and SRMR, are called absolute fit indices. The formula of TLI is

Dividing by df penalizes free parameters to some degree. A value of 1 indicates perfect fit. TLI

is called non-normed because it may assume values < 0 and > 1. Hu & Bentler proposed ≥ .95 as

a cut-off value for a good fit.

CFI (comparative fit index: Bentler, 1990): Here, subtracting df from 2 provides some penalty

for free parameters. The formula for CFI is

Values > 1 are truncated to 1, and values < 0 are raised to 0. Without this “normalization”, this

fit-index is that devised by McDonald & Marsh (1990), the RNI (relative non-centrality index).

Although chi-square is usually examined and reported in CFA, researchers and readers

should recall that sample size affects chi-square. As with any inferential procedure, large

samples produce large chi-square values, which produce statistical significance

As stated earlier the questionnaires were sent to 50 top level managers of Karachi's

manufacturing concerns, expecting at least 6 to 8% of them responding. Only 25 responded in

total. The results were analyzed on SPSS software. Using this software we have analyzed on how

much this factors are reliable and what are the most important factors. We have done all

variability analysis and factor analysis also.


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We have done Reliability test using SPSS for the respondent’s data to check

the reliability of the responses. But as the survey respondents are very low in

number the correlations between the items is also negative and hence we have got a

negative Cronbach’s alpha ( ). Descriptive statistics, alpha coefficients, and item-

total correlation were used to initially analyze the survey data. Factor analysis (FA) was used

to evaluate and shortlist the RL barriers in the industries studied.

Table-6 Reliability Statistics

Reliability Statistics

Cronbach's

AlphaaCronbach's

Alpha Based on Standardized

Itemsa

No of Items

-.245 -.011 10

a. The value is negative due to a negative average covariance among items. This violates

reliability model assumptions. You may want to check item coding.

Table-7 Intraclass Correlation Coefficient

`Intraclass Correlation Coefficient

Intraclass

Correlationb

95% Confidence Interval F Test with True Value 0

Lower Bound Upper Bound Value df1 df2 Sig

Single Measures

Average Measures

-.020a

-.245c

-.056

-1.124

.056

.371

.803

.803

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216

216

.731

.731

Two-way mixed effects model where people effects are random and measures effects are fixed.

a. The estimator is the same, whether the interaction effect is present or not.


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b. Type C intraclass correlation coefficients using a consistency definition-

the between- measure variance is excluded from the denominator variance.

c. This estimate is computed assuming the interaction effect is absent, because it is

not estimable otherwise.

Table-7 Summary Item StatisticsSummary Item Statistics

Mean Minimum Maximum Range Maximum /

Minimum

Variance N of Items

Item Means

Item Variances

Inter-Item Covariances

Inter-Item Correlations

2.976

1.376

-.028

-.001

2.000

.417

-.737

-.487

5.040

2.623

.617

.465

3.040

2.207

1.353

.953

2.520

6.296

-.837

-.955

1.190

.547

.083

.061

10

10

10

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It can be clearly understood that since the single measure and average measures of Intra-class

correlation and Inter-Item Correlation is also negative, it is justifiable that the Cronbach’s Alpha

is Negative.

By performing the Factor Analysis test using SPSS for the respondent’s data, we were able to

analyze the following:

Table-8 KMO & Barlett’s TestKMO & Barlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .516

Approx. Chi-Square 63.035

Bartlett's Test of Sphericity Df 45

Sig 0.39


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Table-9 Total Variance –Component analysis

Total Variance –Component analysis

Comp

onent

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadingsa

Total % of

Variance

Cumulative

%

Total % of

Variance

Cumulative

%

Total

1

2

3

4

5

6

7

8

9

10

2.348

2.132

1.430

1.224

1.007

.608

.471

.302

.287

.189

23.476

21.320

14.299

12.243

10.072

6.085

4.713

3.023

2.874

1.894

23.476

44.796

59.095

71.339

81.411

87.495

92.208

95.231

98.106

100.000

2.348

2.132

1.430

1.224

1.007

23.476

21.320

14.299

12.243

10.072

23.476

44.796

59.095

71.339

81.411

2.258

1.992

1.678

1.403

1.265

Extraction Method: Principal Component Analysis.a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance

b.

Table-10 Structure Matrix

Structure MatrixComponent

1 2 3 4 5Customer Support issues like resolving orderdisputes, product protection, etc are entry barriers to reverse logistics

.592 .693 -.262 .157 .221

Top Management does not support reverselogistics

-.095 -.360 .015 -.868 .006


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Package Tracking System does not allow us tomodel standard Reverse Logistics

-.007 .221 .812 .175 .006

Operating Costs are Very High for ReverseLogistics

-.002 .759 .094 .116 .051

There is no formal structure which facilitateReverse Logistics

-.322 .689 .342 -.041 -.263

Timings of Operations (Delivery Time andCycle time) does not facilitate ReverseLogistics

.763 .131 .140 .042 .085

There aren't any concrete law clauses which support Reverse Logistics

.040 -.008 -.056 .097 .959

Awareness about Reverse Logistics is prettylow in customers

-.779 -.174 .746 -.058 -.393

Lack of adequate Personnel is a barrier toReverse Logistics

.753 -.274 -.294 -.070 -.128

There aren't any performance metrics toReverse Logistics

-.196 -.399 .400 .748 .216

Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization.

It was analyzed from KMO and Barlett’s Test that the significance is 0.039 which means the

result of the responses taken is pretty much significant. From the total variance explained above

which was done using principle variance analysis, it was stated that there are 5 major factors that

affect as barriers to reverse logistics in the manufacturing of Karachi. It can be seen that there are

significant factors that contribute as barriers.

These factors were extracted as individual factors after looking at the structure matrix.

These factors were extracted as major factors that contribute as barriers to reverse logistics in the

manufacturing sector in Karachi, Pakistan, by calculating the relation between different

components for each factor which were clubbed in as a single factor in structure matrix.

Only those components were taken into account for calculating each factor, which had a value of

more than 0.5.


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From the final

matrix (Table 4), the

structural model is generated by vertices and edges (Jharkharia & Shankar, 2005).This graph is

called digraph as

shown in Table-11.

Digraph of

Barriers to

Implement

RL.


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It can be interpreted that as we do the factor analysis and analyze the respondent’s data we were

able to take some output which is as follows:

There were majorly only five factors which are actually acting as barriers to Reverse Logistics in

Karachi's manufacturing sector according to the analysis of the respondents’ survey. The factors

are as follows:

(a) There aren’t any concrete law clauses which support Reverse Logistics in Pakistan. (b)

There aren’t any performance metrics to Reverse Logistics.

(c) Package Tracking System and Low awareness among Customers about Reverse Logistics .

(d) Customer Service issues, Operating Costs and Lack of Formal Structure are acting as

barriers to reverse logistics.

(e) Lack of Personnel and the time are also major constraints to reverse logistics.

It can be concluded from the research that to promote Reverse Logistics in Pakistan, majorly we

should have Legal concerns. It is evident from the experts’ opinion that without legal instructions

industry will not adopt Reverse Logistics. Also there should be performance metrics for reverse

logistics just like for forward logistics’ metrics.

There should be improvement in Package tracking system, increase the awareness among the

customers, proper maintenance of formal structure, time binding deliveries and should

incorporate the customer service centers to promote the reverse logistics. Incorporating all this

structural changes in the manufacturing industry in Pakistan would certainly result in increasing

CAGR for the industry.


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Reverse Logistics can be a major constraint if not improved manufacturing sector. With the

global industry growing at a fast pace than ours, lack of improvement in Reverse Logistics

Processes would surely hinder its growth.In the light of cited intimated analysis it is stated that

reverse logistics is gaining momentum worldwide due to rising costs of materials, resources

scarcity, global awareness and consequences of climate change in Implementing Reserve

Logistics in manufacturing sector evidence from Pakistan. Proactive manufacturing companies

often implement RL practices such as recycling, reuse and general waste management strategies

developed to gain competitive advantage while meeting increasing local and inter-national

demands for environmental protection. In the light of increasing resource scarcity, global take-

back of EoL products legislations and consequences of climate change, it is imperative.

The government of Pakistan has taken revolutionary steps to overcome the barriers in the

successful implementation of supply chain management in Pakistan. CPEC is one of the live

example in Gawader which is fully equipped with all modern Supply chain management

facilities. Moreover to reduce the cost and time all communication channels are kept together

including industrial points, seaports and air ports. This integration not only reduced the cost and

time but also made the supply chain process more organized, fast and swift in Pakistan

Limitations of Study

We have developed a hypothetical model of barriers to implement GSCM in Indian

automobile industry based upon experts’ opinions. The model may be tested in real world setting

to check that the barriers are complete and their relationship exists as in the literature. The results

of model may vary in real world setting. The barriers may be incomplete or their relationships

may be different from the derived model.


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Methodology of thesis 'research barriers in the implementation of reverse logistics'

Data & Analytics