Volume 04, Issue 07 (July 2015)

The Study on Selection of Green Supply Chain Partners in USA Logistics Industry

DOI : 10.17577/IJERTV4IS070399

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Chia-Nan Wang, Ho Thi Hong Xuyen, 2015, The Study on Selection of Green Supply Chain Partners in USA Logistics Industry, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 04, Issue 07 (July 2015),

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The Study on Selection of Green Supply Chain Partners in USA Logistics Industry

Chia-Nan Wang

Department of Industrial Engineering and Management National Kaohsiung University of Applied Sciences, Taiwan

Ho Thi Hong Xuyen

Department of Industrial Engineering and Management National Kaohsiung University of Applied Sciences, Taiwan

AbstractChoosing the suitable green supply chain partners in logistics industry is important to reduce environment risk. The main purpose of this paper is to evaluation of performance measure for green supply chain partners in U.S.A logistics industry using Data development analysis. To conduct a valid and reliable evaluation process while applying the logistics companies case in U.S.A, we integrated the slacks-based measure of super efficiency (super-SBM) and Malmquist index to directly handle the slacks, explore best performer, analyzed the inter- temporal efficiency change, which is decomposed into catch-up and frontier-shift effects and find influential factors in selecting green supply chain partners (GSCPs) criteria from 2010 to 2013. The results show that most GSCPs have higher efficiency and contribute more effort to improving technical change during 2010-2013. By comparing the efficiency of GSCPs in logistics industry, this research provides an approach of decision-making information in logistics as well as contributes to reduce carbon dioxide (CO2) emissions in environmental protection.

KeywordsGreen supply chain management, logistics, Data developent annalysis, Malmquist index, carbon dioxide emissions

  1. INTRODUCTION

    Nowadays, environmentally sustainable green supply chain management has emerged as an important organizational philosophy to achieve corporate profit and market share objectives by reducing environmental risks and impacts while improving ecological efficiency of these organizations and their partners [1]. Thus, it's important to do business with companies that are demonstrating their commitment to sustainable transportation and logistics providers. Logistics and transportation are one of the most important activities that are essential for sustaining our daily lives. However; the U.N. Framework Convention on Climate Change estimates that more than 20 percent of global emissions of greenhouse gases are produced by the transport of goods and people. As a result, there is a pressing need for action, particularly by the logistics industry. The purpose of this research is to evaluate the performance of green supply chain partners in U.S.A logistics industry by integrating the slacks-based measure of super efficiency (super-SBM) models and Malmquist productivity index in Data development analysis (DEA) to select the most eligible green supplier, in order to achieve environmentally sustainable supply chain and about determining strategies considered as most cost-effective for managing and responding to environmental issues in logistics.

  2. PROPOSED METHODOLOY

    This study used Supper – efficiency model (Super- SBM- Oriented) based on slack based measure and Malmquist model to evaluate the efficiency in logistics industry, especially in Green supply chain partners (GSCPs). According to Inbound Logistics, there are 75 green supply chain partners in USA logistics industry [2]. To get credible and equitable data, the plants belonging to third party logistics (3PLS) were first selected for evaluation. Next, the plants belonging to air/ expedited and trucking with complete financial statement were chosen. Finally, only 16 plants were considered in this study.

    Data collection

    The conceptual framework is proposed in four stages. The evaluation process was followed in the framework as below:

    Choose input/output

    Stage one Stage two

    DEA model design

    Stage three

    Super-SBM Model O-V

    Malmquist Non Radial

    Research conclusion and suggestions

    Stage four

    Fig. 1. Procedure of proposed method

    Explanation of Figure 1:

    • Stage one: Data collection. This study used companies that are related to logistics as DMUs, which includes 3PLS, air/ expedited and trucking that are U.S.A listed companies at stock exchange market as Table I

    • Stage two: Choose input/ output variable. The data sources for this study consist of 16 plants annual reports for the period from 2010 to 2013. Information was collected from market observation posting system of U.S.A stock exchange cooperation.

    • Stage three: model design. Firstly, we use the SuperSBM- O-V model which proposed by Tone (2002) [3] is an appropriate version of DEA for ranking these efficient Green supply chain partner companies in this study. Then, we implement the Output-oriented Malmquist productivity index [4] to a sample of Green supply chain partners. This model was chosen to compute in order to evaluate the

      Table III shows the results of efficiency change scores of GSCPs as well as their components of the companies which belong to Green supply chain partners. The results of output technical efficiency change present that there are 3 companies (DMU9, DMU11, DMU14) having no evidence of changes in the input technical efficiency level during the period of 2010-2013.

      TABLE II. EFFICIENCY RANK AND SCORE

      productivity change of a DMU between two time periods.

      2010 2011 2012 2013

      td>

      11

      DMU

      Score

      Rank

      Score

      Rank

      Score

      Rank

      Score

      Rank

      DMU1

      0.390304

      15

      1.046552

      8

      1.046552

      8

      1.084461

      7

      DMU2

      0.764153

      13

      0.909759

      14

      0.909759

      14

      1.012374

      9

      DMU3

      1.32628

      4

      1.130601

      6

      1.130601

      6

      1

      11

      DMU4

      1.610008

      1

      1.701967

      1

      1.701967

      1

      1.331328

      2

      DMU5

      1.115172

      7

      1.15746

      5

      1.15746

      5

      1.140892

      5

      DMU6

      1.419855

      3

      1.383747

      2

      1.383747

      2

      1.366234

      1

      DMU7

      2.15E-02

      16

      0.472521

      16

      0.472521

      16

      0.449934

      16

      DMU8

      0.788557

      12

      1.019557

      9

      1.019557

      9

      1.05502

      8

      DMU9

      0.999893

      10

      1

      11

      1

      11

      1

      11

      DMU10

      0.674219

      14

      1.126958

      7

      1.126958

      7

      1.290887

      3

      DMU11

      0.999711

      1

      11

      1

      11

      1

      11

      DMU12

      1.000338

      8

      1.003082

      10

      1.003082

      10

      0.576199

      15

      DMU13

      1.130438

      6

      1.315366

      3

      1.315366

      3

      1.132695

      6

      DMU14

      1

      9

      1

      11

      1

      11

      1

      11

      DMU15

      1.184168

      5

      1.171036

      4

      1.171036

      4

      1.269728

      4

      DMU16

      1.524519

      2

      0.628216

      15

      0.628216

      15

      1.003267

      10

    • Stage four: Research conclusion and suggestions. The results show that they can guarantee the viability of the company. Based on the super efficiency scores and MPI index, we find that most GSCPs have higher efficiency and contribute more effort to improving technical change.

    TABLE I. GREEN SUPPLY CHAIN PARTNERS LIST

    DMUS

    Full English name of companies

    Stock name

    DMU1

    Ryder

    R

    DMU2

    Werner Enterprises, Inc.

    WERN

    DMU3

    Hub Group Inc

    HUBG

    DMU4

    C.H. Robinson Worldwide

    CHRW

    DMU5

    FedEx Corporation

    FDX

    DMU6

    United Parcel Service, Inc.

    UPS

    DMU7

    Con-way Freight

    CNW

    DMU8

    J.B. Hunt Transport Services, Inc

    JBHT

    DMU9

    Celadon Group, Inc.

    CGI

    DMU10

    Old Dominion Freight Line

    ODFL

    DMU11

    Saia Inc

    SAIA

    DMU12

    CSX Corporation

    CSX

    DMU13

    Norfolk Southern Corp.

    NSC

    DMU14

    Knight Transportation

    KNX

    DMU15

    Union Pacific Coporation

    UNP

    DMU16

    Swift Transportation Co

    SWFT

  3. RESEARCH RESULTS

    1. Performance rankings- Super SBM

      The Super-SBM oriented (Super-SBM-O-V) model is applied to assess the relative performances and used as a ranking measure of the 16 GSCPS in U.S.A. It can be found out from Table II, Super SBM is highly in the measurement of efficiency and the rank is clear [5]. The results show that the sixth (United Parcel Service, Inc.) DMU6 has best value and the score always larger than 1 from 2010 to 2013, it is also ranked in the first place in 2013. DMU4 (C.H. Robinson Worldwide, Inc.) is ranked in the second place, and DMU10 (Old Dominion Freight Line) is ranked as the third best DMUs in 2013. That means these company reach the efficiency of output. In other words, DMU7 over invested in input. Thus, if it wants to reach the efficiency level, it should lower its inputs.

    2. Components of the Malmquist productivity index: (1) efficiency change

      First, we observe the efficiency effect of DMUs. The change in efficiency is called catch-up effect [4]. The annual efficiency change index for each DMUs is shown in Table III and figure 2.

      TABLE III. ANNUAL EFFICIENCY CHANGE FROM 2010 TO 2013

      Catch-up

      10=>11

      11=>12

      12=>13

      Average

      DMU1

      2.681378

      1.00908

      1.026899

      1.572452

      DMU2

      1.190547

      1.099574

      1.012022

      1.100714

      DMU3

      0.85246

      0.992735

      0.890958

      0.912051

      DMU4

      1.057117

      1.002585

      0.780212

      0.946638

      DMU5

      1.037921

      1.127276

      0.874396

      1.013198

      DMU6

      0.974569

      0.777484

      1.269922

      1.007325

      DMU7

      21.98297

      0.989519

      0.962285

      7.978257

      DMU8

      1.292941

      1.035065

      0.999727

      1.109244

      DMU9

      1

      1

      1

      1

      DMU10

      1.6715

      0.994086

      1.152276

      1.272621

      DMU11

      1

      1

      1

      1

      DMU12

      1.002743

      0.639761

      0.897881

      0.846795

      DMU13

      1.16359

      0.874917

      0.984236

      1.007581

      DMU14

      1

      1

      1

      1

      DMU15

      0.98891

      1.114017

      0.973304

      1.02541

      DMU16

      0.412075

      0.998849

      1.59885

      1.003258

      Average

      2.456795

      0.978434

      1.026436

      1.487222

      Max

      21.98297

      1.127276

      1.59885

      7.978257

      Min

      0.412075

      0.639761

      0.780212

      0.846795

      SD

      5.228949

      0.123321

      0.188527

      1.738908

      Fig. 2. Annual efficiency change from 2010 to 2013

      In 2013, DMU7 (Con-way Freight) had the largest improvement in efficiency change with score is 7.978257. According average index shows that as a whole, the performance of these companies had been improved from 2010 to 2013. The efficiency change score of these companies was always larger than 1 except for DMU12, its efficiency change scores lower than other companies.

    3. Components of the Malmquist productivity index: (2) technical change

      Technical-efficiency or the so-called innovation or frontier-shift effect measures can be compared across time by means of the Malmquist index. In turn, the Malmquist index can be decomposed into two parts: change in technical efficiency and change in best- practice [4].

      The results show that during the period of 2010 to 2013. There are 8 logistics companies that having the output technical improvement. There are 8 companies still improve their level of input technical change during the period of 2010 to 2013 as the previous year. Table IV and figure3 shows that DM9 (Celadon Group, Inc.) has an efficiency score of one in all the years. There are have 8 companies with technical change scores have efficiency score larger than 1, which indicates that they were reach efficiency change level. DMU6 (United Parcel Service, Inc.) has the highest average in the technical efficiency in the period 2010 to 2013. DMU16 (Swift Transportation Co) has scores smaller than 1 from 2011 to 2013. The interpretation of this is that Swift Transportation Co. has low per capital incomes because it seems that it was not investment in new technologies.

      TABLE IV. TECHNICAL (FRONTIER) CHANGE OVER THE PERIOD 2010 TO

      2013

      Frontier

      10=>11

      11=>12

      12=>13

      Average

      DMU1

      0.938197

      1.336349

      1.211213

      1.16192

      DMU2

      0.913608

      1.04317

      0.992737

      0.983171

      DMU3

      1.029052

      1.0419

      1.027608

      1.032853

      DMU4

      1.015689

      1.040676

      1.033418

      1.029928

      DMU5

      1.184574

      0.860028

      1.240945

      1.095182

      DMU6

      1.068291

      1.00985

      2.048535

      1.375558

      DMU7

      0.834259

      1.131308

      0.970415

      0.978661

      DMU8

      0.866662

      1.026548

      0.98498

      0.959397

      DMU9

      1

      1

      1

      1

      DMU10

      0.838103

      1.076613

      1.033319

      0.982678

      DMU11

      1.17893

      1.12247

      1.044798

      1.115399

      DMU12

      1.291143

      1.56839

      1.083977

      1.314503

      DMU13

      1.114362

      0.986038

      1.038669

      1.046356

      DMU14

      1.00802

      1.019136

      1.006997

      1.011384

      DMU15

      1.070172

      1.080795

      1.021964

      1.057644

      DMU16

      1.04493

      0.79002

      0.963337

      0.932762

      Average

      1.024749

      1.070831

      1.106432

      1.067337

      Max

      1.291143

      1.56839

      2.048535

      1.375558

      Min

      0.834259

      0.79002

      0.963337

      0.932762

      SD

      0.129004

      0.177179

      0.262985

      0.124039

      Fig. 3. Technical (Frontier) Change over the Period 2010 to 2013

    4. Productivity changes: (3)the Malmquist productivity index and its decomposition.

    The Malmquist index indicates the change of productivity between period t and t+1. In this case, if MI > 1, this indicates an improvement in efficiency by which is meant that the productivity of a specific logistic companies increases over the previous year thats mean these companies are moving along the best production frontier; while MI = 1 and MI < 1 indicate a reduction in efficiency which means that the productivity of a specific logistics companies decreases over the previous year.

    Table V and figure 4 shows the results of Malmquist index during 2010 to 2011 that there are an improvement on the productivity level in 14 logistics companies with a MPI values larger than 1. On the contrary, the productivity levels of 2 companies in the same period are decrease with a MPI less than 1, which indicates that productivity loss. The worse productivity in this period comes from the deterioration of input technical efficiency in most cases.

    From 2012 to 2013, ten of the companies had productivity growth and other six of the companies had productivity loss. The reduction of the productivity level in this period is mostly from the regression of the input technical efficiency. DMU7 had the highest productivity growth, followed by DMU1. The main source of improvement comes from the development of technical efficiency and technical change.

    Fig. 4. Annual productivity change (MPI) from 2010 to 2013

    TABLE V. ANNUAL PRODUCTIVITY CHANGE (MPI) FROM 2010 TO 2013

    Malmquist

    10=>11

    11=>12

    12=>13

    Average

    DMU1

    2.515661

    1.348482

    1.243794

    1.702646

    DMU2

    1.087692

    1.147042

    1.004672

    1.079802

    DMU3

    0.877226

    1.034331

    0.915556

    0.942371

    DMU4

    1.073702

    1.043366

    0.806286

    0.974451

    DMU5

    1.229495

    0.969489

    1.085077

    1.094687

    DMU6

    1.041123

    0.785142

    2.601479

    1.475915

    DMU7

    18.33948

    1.119451

    0.933815

    6.797581

    DMU8

    1.120543

    1.062544

    0.984711

    1.055933

    DMU9

    1

    1

    1

    1

    DMU10

    1.400889

    1.070246

    1.190669

    1.220602

    DMU11

    1.17893

    1.12247

    1.044798

    1.115399

    DMU12

    1.294685

    1.003394

    0.973282

    1.090454

    DMU13

    1.296661

    0.862701

    1.022296

    1.060553

    DMU14

    1.00802

    1.019136

    1.006997

    1.011384

    DMU15

    1.058304

    1.204024

    0.994682

    1.08567

    DMU16

    0.43059

    0.78911

    1.540231

    0.919977

    Average

    2.247062

    1.036308

    1.146771

    1.476714

    Max

    18.33948

    1.348482

    2.601479

    6.797581

    Min

    0.43059

    0.785142

    0.806286

    0.919977

    SD

    4.311724

    0.145044

    0.421705

    1.43309

  4. CONCLUSION

The purpose of this study research is evaluate the performance of green supply chain partners to select the most

eligible green supplier in order to achieve environmentally sustainable supply chain and about determining strategies considered as most cost-effective for managing and responding to environmental issues in supply chain.

The evaluation of green supply chain partners which was published by Inbound Logistics used the technical called Data Envelopment Analysis and Malmquist productivity index to etimate the efficiency scores of the green supply chain partners in U.S.A.

The empirical evidence of this paper provides some implications and suggestions for green supply chain companies to improve more their profit, technical, scale efficiencies and CO2 emission.

REFERENCES

  1. Van Hoek, R.I & Erasmus (2000)." Reversed logistics to green supply chains". Logistics Solutions, 2, 28-33

  2. Inbound Logistics, 75 green supply chain partners 2013.

  3. Tone, K. (2002) A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143, 32-41.

  4. Tone K (2005). Malmquist productivity index efficiency change overtime. In: Cooper WW., Seiford LM, Zhu J. (Eds.), Handbook on Data Envelopment Analysis. Kluwer Academic Publishers, Boston, pp.203-227

  5. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.

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