development and validation of critical and sub-critical ... · critical factors for green supply...

@IJRTER-2016, All Rights Reserved 26

Critical Factors for Green Supply Chain Management: Development

and Validation in the Manufacturing and Supply Chain Industries

Shreeshailyasiddha Kole1, Avinash Sarode2 1ME Pursuing, Mechanical Engineering, LTCOE, Navi Mumbai, India

2Professor, Mechanical Engineering, LTCOE, Navi Mumbai, India

Abstract —The purpose of this research is intended to address the critical Factors (CFs) of Green Supply Chain Management (GSCM) by through a review of literature and expert consultation, forty seven critical factors leading to responsive Green Supply Chains have been identified. The approach of the research includes a literature review, in-depth interviews and surveys for major activities Government Involvement, Company Management Concept and Design, , Material (Purchase), Vendor Allocation, Warehouse, Manufacturing, Packaging, Transport, Marketing, End User Consumer, Reverse Logistics, Recycle / Reuse, Employee management, IT system support is covered throughout the research, Targeted research area is “Macro & Medium Scale industries of Manufacturing sector with Manufacturing and SCM an Industrial engineering experts” by considering occurrence of CFs. Factor analysis and reliability analysis is done by using Statistical Package for the Social Sciences (SPSS) software to help managers understand the significant environmental dimensions. Factor analysis is used to evaluate the relative importance of various environmental factors. Reliability specifies the extent to which an experiment, test or any other measuring procedure yields the similar results. The data analyzed is by using “mean score”. Keywords: Green Supply Chain Management, environmental performance measures, factor analysis, Critical Factor, Corporate sustainability, SPSS

I. INTRODUCTION

To achieve a suitable prevention strategy, a number of organizations have started developing and deploying a new concept called green supply chain management (GSCM). Distinct the traditional environmental management, the green supply chain concept contains all phases of a product’s life cycle, starting from the extraction of raw material stage, followed by design, production, and distribution stages, to the product’s use by customers, and its final disposal at the end of the product’s life cycle[1][2][3]. Adding ‘green’ component to supply-chain management involves addressing the effect and relationships among supply-chain management and natural environment. Similar to the concept of supply-chain management, the boundary of GrSCM is dependent on the goal of the investigator [4]. This Research describes the implementation and practices of CFs in GSCM among various manufacturing industries. Fifteen practices namely Government Involvement, Company Management Concept and Design, , Material (Purchase), Vendor Allocation, Warehouse, Manufacturing, Packaging, Transport, Marketing, End User Consumer, Reverse Logistics, Recycle / Reuse, Employee management, IT system support are considered with 47 sub critical factors. The research consists of five sections. Section I introduction after this in Section II, review of the important literature is specified. It helps in creating a connection between GSCM and environmental performance measures. Section III surrounds research methodology. Section IV associated with the result of occurrence CFs in past literature and comparative analysis of various factors of GSCM by considering calculated “Mean Score” are presented. Finally, the conclusion is obtainable in section V.

International Journal of Recent Trends in Engineering & Research (IJRTER)

Volume 02, Issue 06; June - 2016 [ISSN: 2455-1457]


Environmental management is becoming more important for corporations as the emphasis on the environmental protection by organizational stakeholders, governments, employees, customers, competitors and communities, keeps increasing. Programs for instance design for environment, life-cycle analysis, total quality environmental management; ISO 14001 GSCM standards are popular for environmentally conscious practices.

II. LITERATURE REVIEW

Number of literature reviews on the sustainable and GSCM have been accomplished in the past few years. Some of these reviews have been general and comprehensively covered complete field [5], whilst others have focused on specific aspects such as performance measurement, critical factors [6]. “A supply chain which contain product design, material procuring and selection, manufacturing operation, logistics till end consumer by support of top management and under pressure by the government legalization to make green / sustainable environmental process[4] can define as GSCM.” In following table we have explained, collectively findings of past research in column, reference no. of research, type of considered research, tools used in research and important findings. In each row we have explained a single reference.

Table 1: Literature Table

Ref. No.

Type of

Research Tools Findings

[7] Descriptive

Fundamental Conceptual

Environmental Performance Index (Epi)

Industrial example on different aspects like, 1. Management in electronic industry- Hierarchy process. 2. Textile enterprise- Industrial Operations 3. Green transportation reduces cost of practices.

[8] Descriptive

Applied Empirical

1 Peruse Of Literature 2. Interpretive Structural Modeling 3. MICMAC Analysis

1. Their contextual relationships, to develop a hierarchy of CSFs to implement GSCM towards sustainability in Indian perspective 2. “Scarcity of natural resources” CSF is found as the lowest dependence power and highest driving power 3. MICMAC analysis: - The driving power-dependence power illustration assists to categorize various CSFs of greening the supply chain.

[9] Descriptive

Applied Qualitative

1. Approaches For GSCM Implementation Emphasized By Various Authors. 2.Fuzzy Analytic Hierarchy Process (FAHP) 3.Factor Analysis

1. Determine the importance of the dimensions and approaches, the judgments collected from respondents generated the normalized local and global Weights for approaches to implementing GSCM. 2. FAHP proposed Despite focusing on the Taiwanese electronics industry, the results of this study provide an insight Into recognizing and prioritizing the approaches for implementing GSCM.




[10] Descriptive

Applied

1. Questioner Method 2. Statistical Tests For Critical Factors

1. By Questioner method they collected the data for appropriate test on CFS for to establish reliability and validity. 2. Statistical tests perform to show valid of critical factors.

[11] Empirical

Quantitative Conceptual

1.Depth Interviews And Questionnaire Surveys 2.Choice Of Assessment Method 3.Reliability Test 4.Validity Test

1. The relationship between GSCM and environmental performance as well as financial performance is explained. 2. Choice of assessment method ML (maximum likelihood) of SEM is heavily influenced by variable distribution properties, ML can be used to evaluate the model of the present study. 3. By data analyzing sing statistical package for social science and structural equation modeling we find path analysis model for hypothetical study.

[4] Descriptive


1. Approaches For GSCM Implementation In Integrated Manner. 2. Vis-À-Vis The Contexts.

1. This study gives integrated and fresh look into the area of GSCM.

[12] Descriptive


1. Partial Least Squares Technique

1. Implications for designing strategic plans for the Malaysian automotive industry. 2. Green Innovation Initiatives (GII) have a positive effect on the environmental, social, and economic categories of sustainable performance

[13] Descriptive


1. Questionnaire Survey 2. Factor Analysis 3. Cluster Analysis

1. Green marketing oriented group performed best but as per resource based view it is deployed. 2.We got a taxonomy of GSCM

[1] Descriptive

Applied Conceptual

1. Peruse Of Literature 2. Interpretive Structural Modeling

1. To discover behavioral factors affecting GCSM which help to achieve green-enabled needs. 2. In this research to extract the interrelationships among the identified behavioral factors.

[2] Descriptive

Applied Empirical

1. Interpretive Structural Modeling 2. MICMAC Analysis

1. ISM model for the drivers affecting the implementation of GSCM.




[14] Descriptive

Applied Empirical

1. Interpretive Structural Modeling 2. MICMAC Analysis

1.This analysis study used to illustrate the relative driving and dependence power among the factors

[15] Descriptive


Questionnaire Mail Survey

The paper reveals that the more slack resources and organizational capabilities suppliers had, the more willingly they were to participate in those initiatives

[10] Descriptive


Questionnaire Mail Survey

1. Fills a gap in the literature on the identification and establishment of critical factors for GSCM implementation. 2. Evaluated the perceptions of GSCM in the organizations.

[16] Descriptive


1. Literature Study 2. Survey Method

1. Green activities in electronic parts manufacturers in Thailand. 2. Evaluation by survey it proposes the suggestions to develop GSCM in electronics industry

[17] Descriptive


Peruse Of Literature

1. Systematic grouping of the different performance measures 2. Framework for performance measurements in supply chains

[6] Analytical Applied

Quantitative

1. Peruse Of Literature 2. Bibliometric Analysis

1. The study provides a strong roadmap for further study in the field of GSCM. 2. Provide details of each publication.

[18] Analytical Applied

Quantitative

1. Linear Multi-Objective Programming Model

1. Net profit improved in particular study by system optimization. 2. Proposed model

[19] Descriptive


1. Peruse Of Literature 1. integrative framework of GSCM performance tools 2.Future issues that should be addressed

As per as past research study [20].We found 47 Sub- CFs in different literatures. For further studies we classified these Sub-CFs into Fifteen main CFs which is mentioned in row and in column following table as per the authors mentioned in their paper [11] [12] [13], it helps to know the importance of CFs to implement GSCM towards sustainability.




Table 2: Citation of different Critical Factors of GSCM

Lit

eratu

re R

efer

ence.

No.

Gover

nm

ent

Involv

emen

t

Con

cep

t an

d D

esig

n

Com

pan

y M

an

agem

ent

Mate

rial

(Pu

rch

ase

)

Ven

dor

All

oca

tion

Ware

hou

se

Man

ufa

ctu

re

Pack

agin

g

Mark

etin

g

Tra

nsp

ort

En

d U

ser

Con

sum

er

Rev

erse

Logis

tics

Rec

ycl

e/R

euse

IT s

yst

em s

up

port

Em

plo

yee

man

agem

ent

[1] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[2] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[3] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[4] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[6] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[7] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[15] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[8] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[9] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[10] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[11] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[13] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[16] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[17] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[18] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[19] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[20] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[21] ✔✔✔✔ ✔✔✔✔

[22] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[23] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[24] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[25] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

[26] ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔

III. RESEARCH METHODOLOGY

Based on the literature reviewed, a tentative list of the sub-criteria and criteria for green supply chain was developed. The task of designing the questionnaire was carried out after reviewing a variety of literature. In the pre-testing phase of the questionnaire, practicing industry representatives were consulted for their view on the criteria selected and whether all the relevant criteria were covered in the questionnaire. Further Survey was conducted, in which main criteria’s are considered as questions. Then, based on their feedback, the criteria list was modified and put into a structured form, with each sub-criteria falling under their respective criteria/major criteria. At the end of the pre - testing stage, 47 sub-criteria under the heading of seven major criteria were finalized. Each




criterion in the questionnaire was judged on a five point Likert Scale, where, Low critical=1, Low to moderate critical=2, Moderate critical=3, Moderate to high critical=4, Very High Critical=5. Likert scale is a tried and tested scale has been successfully used in many cases, including supplier selection. Reliability indicates the extent to which an experiment, test or any other measuring procedure yields the similar results [21]. The reliability assessment was conducted on Statistical Package for the Social Sciences (SPSS) software. The methodology adopted was similar to the one described by Pallant J. in her book on SPSS.

Next part of survey is to target area, in this we need to identify scale of industry, type of industry and respondents working area. For this we used General observation method, in this method we consider effect of CFs in present industry stat. Following table shows relation between Individual CFs with criteria By the above observation we have stated our target statement as “Macro & Medium Scale industries of Manufacturing &/or E-commerce sector with Manufacturing and SCM an Industrial engineering experts.”

Based on the literature we developed a survey questionnaire

Figure 2: Conceptual grounded theory framework [22]

For calculating sample size we did the pilot study, in which we had sent questionnaires to 20 people and we have received 5 responses basis which we calculated sample size. The approach “specifying precision of estimation desired” is used in this work to find out sample size. With 95% confidence level, the sample size is estimated by using Value of standard variate at a given confidence level 1.96 for 95% confidence level with acceptable error assumed to be 3% true mean value for this study we found sample size ≃≃≃≃ 345.

15

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CF

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TARGETED AREA

Figure 1: Targeted Area


@IJRTER-2016, All Rights Reserved

with higher values indicating greater reliability. Nunnally suggested a minimum value of 0.7. Cronbach‟s alpha values are dependent on the number of items in the scale is less than 5 (in this study, where each criterion has 5 or less subthen Cronbach‟s alpha values can be quite small. Here, the mean intercalculated. J. Pallant [21]recommended their optimum value to be above 0.3. Item analysis was conducted for each of the 46 parameters concluded a mean score method. These dimensions are represented in the form of a questionnaire, for measuring the different fimplementation [24].

IV. FACTOR ANALYSIS,

A. Reliability Analysis

Reliability indicates the extent to which an experiment, test or any other measuring procedure yields the same results. Reliability analysis was conceded out using total 46 criteria on SPSS software. The final Cronbach‟s values and the range of correlation coefficient give an idea about the scale chosen. It also helps to find that the sub-criteria have been correctnot. The final Cronbach's alpha values should be more than 0.7. Table 3 shows the reliability analysis of the major criteria selected for the study

Major Critical Factors Total

number of

Items

Government Involvement 5 Concept and Design 4 Company Management 2 Material (Purchase) 3 Vendor Allocation 5 Warehouse 3 Manufacturing 4 Marketing 2 Transport 2 End User Consumer 4 Reverse Logistics 2 Recycle / Reuse 5 IT system support 2 Employee management 3

14%

14%

39%

33%

Experience of respondents in Years

Figure 2Experience of Respondents for Survey


Volume 02, Issue 06; June

2016, All Rights Reserved

All responses are received from targeted area sector. Above pie chart shows the experience of respondents, which mainly says that we have 33% response received from top management with more than 20years of experienced people, 39% response received from 10 experienced. We have also received responses from international platform, 14% of each response was from 1 to 5 and 5 to 10 years’ experience. The common method to measureby using Cronbach's alpha, which was carried out using SPSS. The value ranges from 0 to 1,

with higher values indicating greater reliability. Nunnally suggested a minimum value of 0.7. s alpha values are dependent on the number of items on the scale

items in the scale is less than 5 (in this study, where each criterion has 5 or less subs alpha values can be quite small. Here, the mean inter-item correlations were also

recommended their optimum value to be above 0.3. Item analysis was conducted for each of the 46 parameters concluded a mean score method. These dimensions are represented in the form of a questionnaire, for measuring the different facets of GSCM practices

ANALYSIS, RESULT ANALYSIS AND DISCUSSION

Reliability indicates the extent to which an experiment, test or any other measuring procedure yields results. Reliability analysis was conceded out using total 46 criteria on SPSS software. The

s values and the range of correlation coefficient give an idea about the scale chosen. criteria have been correctly assigned to their respective criteria or

not. The final Cronbach's alpha values should be more than 0.7. Table 3 shows the reliability analysis of the major criteria selected for the studyTable 3Reliability analysis

Total

number of

Items

Cronbach’s

Alpha

Cronbach’s

Alpha Based on

Standardize Items

0.835 0.836 0.826 0.821 0.873 0.874 0.834 0.836 0.911 0.914 0.875 0.879 0.894 0.896 0.892 0.892 0.829 0.834 0.927 0.928 0.861 0.861 0.932 0.933 0.940 0.940 0.878 0.878

Experience of respondents in Years

1 to 5

5 to 10

10 to 20

20+

Experience of Respondents for Survey



32

All responses are received from targeted area sector. Above pie chart shows the experience of respondents, which mainly says that we have 33% response received from top management with more than 20years of experienced people, 39% response received from 10 to 20 years’ experienced. We have also received responses from international platform, 14% of each

5 and 5 to 10 years’

The common method to measure reliability is by using Cronbach's alpha, which was carried out using SPSS. The value ranges from 0 to 1,

with higher values indicating greater reliability. Nunnally suggested a minimum value of 0.7. items on the scale [23]. If the number of

items in the scale is less than 5 (in this study, where each criterion has 5 or less sub-criteria under it) item correlations were also

recommended their optimum value to be above 0.3. Item analysis was conducted for each of the 46 parameters concluded a mean score method. These dimensions are

acets of GSCM practices

DISCUSSION

Reliability indicates the extent to which an experiment, test or any other measuring procedure yields results. Reliability analysis was conceded out using total 46 criteria on SPSS software. The

s values and the range of correlation coefficient give an idea about the scale chosen. ly assigned to their respective criteria or

not. The final Cronbach's alpha values should be more than 0.7. Table 3 shows the reliability analysis

Standardize Items

Range of

correlation

coefficients

0.527-0.719 0.435-0.780 0.777-0.777 0.535-0.843 0.740-0.808 0.752-0.786 0.671-0.834 0.804-0.804 0.715-0.715 0.761-0.863 0.757-0.757 0.781-0.890 0.886-0.886 0.650-0.868




B. KMO and Bartlett’s Test of Sphericity

Table 4 KMO and Bartlett’s test of sphericity

By examining the strength of relationships among the sub-criteria we can make next appropriateness for Factor analysis. This was conducted by three measure Bartlett's significance Value (p) by test of sphericity, correlation matrix and Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy.

The Bartlett's test of sphericity should be significant (p < 0.05) in the factor analysis to be considered appropriate. The KMO index ranges from 0 to 1 with 0.6 recommended as the minimum value [21]. Meanwhile Digalwar and Sangwan [23]recommended KMO value more than 0.5 as optimal. The reliability analysis in Table 3 shows correlations are greater than 0.3 it means reliability analysis confirms all fourteen major criteria are suitable for applying factor analysis.

Analysis of the KMO measure using SPSS in Table 4 reveals that all the measures meet the required standard. The Bartlett’s test indicates that all the criteria’s are significant (p < 0.05). Table 4 shows KMO and Bartlett’s test of sphericity analysis of the major criteria selected for the study.

C. Factor Analysis

The components were extracted in SPSS using principal component analysis with varimax rotation. Initially, factors with Eigen value over one were extracted and the screen plot along with the unrotated factor solution analyzed. Factor analysis was conducted on each criterion. Those factors with a significant slope above the bend in the screen plot were extracted [21].A sample screen plot for Vendor Allocation criterion is shown in Fig.3

Criteria KMO

Bartlett's

significance

Value (p)

Bartlett's

Approx.

Chi-Square

Government

Involvement

0.628 0 89.588

Concept and

Design

0.740 0 59.220

Company

Management

0.500 0 30.957

Material

(Purchase)

0.610 0 56.575

Vendor Allocation 0.717 0 135.910 Warehouse 0.741 0 52.649 Manufacturing 0.830 0 82.388 Packaging Marketing 0.500 0 34.871 Transport 0.500 0 24.013 End User

Consumer

0.782 0 120.128

Reverse Logistics 0.500 0 28.475 Recycle / Reuse 0.888 0 136.613 IT system support 0.500 0 51577 Employee

management

0.662 0 64.523

Figure 3Sample scree plot for Vendor Allocation




Table 5Results of Factor Analysis

Item loading

Range

Eigen

value

%

variance

Government Involvement 0.681-0.840 3.037 60.731

Concept and Design 0.613-0.894 2.643 66.081

Company Management 0.942-0.942 1.777 88.830

Material (Purchase) 0.753-0.944 2.275 75.842

Vendor Allocation 0.828-0.892 3.723 74.467

Warehouse 0.889-0.909 2.415 80.511

Manufacturing 0.804-0.915 3.053 76.324

Packaging 0.950-0.950 1.804 90.214 Marketing 0.926-0.926 1.715 85.766 Transport 0.858-0.926 3.293 82.329

End User Consumer 0.937-0.937 1.757 87.835

Reverse Logistics 0.859-0.934 3.949 78.986

Recycle / Reuse 0.971-0.971 1.886 94.315

IT system support 0.826-0.948 2.417 80.552

Fifteen green supply chain factors with 47 original dimensions measured in this study and each dimension has its own importance for effective GSCM performance. Table 6 to Table 20 shows the mean values (M) and standard deviation (S.D) of the criteria and sub-criteria respectively obtained from various respondents. The tables show the important criteria in the descending order of their means. Higher mean values indicate more important criteria. The Critical Factors are arranged in descending order of their Perusal of Literature.

Table 6Performance of Main Critical Factors

Critical Factors Perusal of literature

Survey

Mean Std.

Deviation

Material (Purchase) 15 3.6389 1.2599 Manufacturing 15 3.8125 1.1852

Concept and Design 14 3.6736 1.1845 Vendor Allocation 12 3.5944 1.1306

Government Involvement 11 3.3889 1.2859 Company management 11 4 1.0533

End User Consumer 11 3.6528 1.1325 Reverse Logistics 10 3.7361 1.0554

Recycle/Reuse 10 3.7667 1.2 IT system support 7 3.4444 1.1197

Marketing 6 3.5417 1.1559 Employee management 6 3.8981 1.0191

Transport 5 3.889 1.033 Warehouse 4 3.6019 1.1077 Packaging 2 4 1.257




i. Performance of Government Involvement

. Table 7Performance of Government Involvement

Government Involvement which had 5 underlying dimensions was having Enviromental Policy for GSCM (3.778) as the most important dimension.

ii. Performance of Concept and Design

. Table 8 Performance of Concept and Design

Performance of Concept and Design which had 4 underlying dimensions was having Strategic Planning GSCM (3.8611) as the most important dimension

iii. Performance of Company Management

Table 9 Performance of Company Management

Performance of Company Management which had 2 underlying dimensions was having Initiation and Top Management Commitment for GSCM (4.0556) as the most important dimension

iv. Performance of Material (Purchase) Table 10Performance of Material (Purchase)

Performance of Material (Purchase) which had 3 underlying dimensions was having Implementing Green Purchasing for GSCM (3.8056) as the most important dimension

Mean

Std. Deviation

Variance Range

Central Government Leglisation

3.5833 1.27335 1.621 4.00

State Government Leglisation 3.5278 1.27584 1.628 4.00 Pressure from NGO 2.5000 1.27615 1.629 4.00

Scarcity of natural Resources 3.5556 1.38243 1.911 4.00

Enviromental Policy for GSCM

3.7778 1.22150 1.492 4.00

Mean Std. Deviation Variance Range

Strategic Planning 3.8611 1.17480 1.380 4.00

Green Design 3.7222 1.20975 1.463 4.00 Innovativeness 3.6111 1.27117 1.616 4.00

Production Planning 3.5000 1.08233 1.171 4.00

Mean

Std. Deviation

Variance Range

Initiation and Top Management Commitment

4.0556 1.01262 1.025 3.00

Improvement in community relaxation and corporate image

3.9444 1.09400 1.197 4.00

Mean

Std. Deviation

Variance Range

Reduce resource consumption

3.5000 1.23056 1.514 4.00

Implementing Green Purchasing

3.8056 1.19090 1.418 4.00

Enviromental requirement for purchasing items

3.6111 1.35810 1.844 4.00




v. Performance of Vendor Allocation

Table 11Performance of Vendor Allocation

Performance of Vendor Allocation which had 5 underlying dimensions was having Involvement of supplier and vendor in Green practices for GSCM (3.7778) as the most important dimension

vi. Performance of Warehouse Table 12Performance of Warehouse

Performance of Warehouse which had 3 underlying dimensions was having Inventory Management for GSCM (3.7778) as the most important dimension

vii. Performance of Manufacturing Table 13Performance of Manufacturing

Performance of manufacturing which had 4 underlying dimensions was having Improve efficiency and Green process for GSCM (3.9444) are the most important dimension viii. Performance of Packaging

Table 14Performance of Packaging

Performance of Packaging which had one underlying dimensions was having Green Packaging for GSCM (4.000) as the most important dimension

ix. Performance of Marketing Table 15Performance of Marketing

Performance of marketing which had 2 underlying dimensions was having Enhance Brand Image for GSCM (3.6111) as the most important dimension

Mean

Std. Deviation

Variance Range

Involvement of supplier and vendor in Green practices

3.7778 .95950 .921 3.00

Motivation to Supplier and Vendor

3.7222 1.08525 1.178 3.00

Technology transfer to supplier and vendor

3.4167 1.07902 1.164 4.00

Enviromental auding of supplier 3.5278 1.27584 1.628 4.00

Supplier - Sub supplier relation 3.5278 1.25325 1.571 4.00


Workplace Management 3.4444 .96937 .940 4.00

Green Stock 3.5833 1.15573 1.336 4.00

Inventory Management 3.7778 1.19788 1.435 4.00

Mean

Std. Deviation

Variance Range

Improve efficiency 3.9444 1.09400 1.197 4.00 Re-Program 3.5000 1.20712 1.457 4.00 Reduce emission in process

3.8611 1.24563 1.552 4.00

Green process 3.9444 1.19390 1.425 4.00

Green Packaging

Mean Std. Deviation Variance Range 4.0000 1.12122 1.257 4.00


Green Marketing 3.4722 1.15847 1.342 4.00 Enhance Brand Image 3.6111 1.15333 1.330 4.00




x. Performance of Transport Table 16Performance of Transport

Performance of Transport which had 2 underlying dimensions was having Recyclable or reusable packaging/container in logistics for GSCM (3.9167) as the most important dimension.

xi. Performance of End User Consumer Table 17Performance of End User Consumer

Performance of End User Consumer which had 4 underlying dimensions was having Support from customer for GSCM (3.7222) as the most important dimension

xii. Performance of Reverse Logistics Table 18Performance of Reverse Logistics

Performance of Reverse Logistics which had 2 underlying dimensions was having Network design for GSCM (3.7500) as the most important dimension

xiii. Performance of Recycle / Reuse Table 19Performance of Recycle / Reuse

Performance of Recycle / Reuse which had 5 underlying dimensions was having Reduce for GSCM (3.9167) as the most important dimension

xiv. Performance of IT system support Table 20Performance of IT system support

Performance of IT system support which had 2 underlying dimensions was having IT enables systems support and Encouragement to technology advancement and adoption for GSCM (3.4444) are the most important dimension

Mean

Std. Deviation

Variance Range

Green logistics and Transport

3.8611 .96074 .923 3.00

Recyclable or reusable packaging/container in logistics

3.9167 1.10518 1.221 4.00

Mean

Std. Deviation

Variance Range

Encouragement from customer

3.5278 1.15847 1.342 4.00

Support from customer 3.7222 1.18590 1.406 4.00

Market Demand 3.6944 1.11661 1.247 4.00

Greening post use 3.6667 1.06904 1.143 4.00


Reverse Logistics 3.7222 1.05860 1.121 4.00

Network design 3.7500 1.05221 1.107 3.00


Recycling Program 3.8333 1.13389 1.286 4.00 Disposal 3.8611 1.07312 1.152 4.00 Reuse 3.7500 1.31747 1.736 4.00 Reduce 3.9167 1.15573 1.336 4.00 Joining Recycling organization

3.4722 1.31987 1.742 4.00

Mean

Std. Deviation

Variance Range

IT enables systems support

3.4444 1.10698 1.225 4.00

Encouragement to technology advancement and adoption

3.4444 1.13249 1.283 4.00




xv. Performance of Employee management Table 21Performance of Employee management

Performance of Employee management which had 3 underlying dimensions was having Green Training for GSCM (4.000) as the most important dimension

V. CONCLUSION

Comprehensive study of the outcomes indicates that manufacturing, vendor allocation are the most important critical factor for the GSCM. We perceived with two methods, first is perusal of past literature describe that Material (Purchase), manufacturing and concept and design of system or product are the most important critical factors. Then it surveyed by Government Involvement, Company Management, End User Consumer, Reverse Logistics, Recycle/Reuse are common critical factors effect to GSCM. By perusal for literature we found Packaging is the least effective critical factor in GSCM followed by Warehouse, Transport and Marketing and Employee management.

Survey conducted using 47 subcritical factor of main of main 15 main critical factors in GSCM. The responses collected from experienced industrial professionals. The responses are analyzed by using Reliability analysis on SPSS tool. We have received responses for 47 questions; the data is reduced by Factor Analysis Data Reduction method using SPSS. Second survey describe that the Transport, Vendor Allocation and Manufacturing are the most critical factors in GSCM. Then tracked by Material (Purchase), Concept and Design, Reverse Logistics, Government Involvement, Marketing, Recycle/Reuse and End User Consumer. Least effective critical factor by second survey is Warehouse and Company Management. Employee management and IT system support middling important critical factor in GSCM. GSCM (GSCM) is a relatively new green issue for the majority industries.

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