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

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.