Scholarly article on topic 'Green supply chain management performance in automobile manufacturing industry under uncertainty'

Green supply chain management performance in automobile manufacturing industry under uncertainty Academic research paper on "Materials engineering"

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Abstract of research paper on Materials engineering, author of scientific article — Ru-Jen Lin, Rong-Huei Chen, Thi-Hang Nguyen

Abstract Increasing pressures and challenges to improve economic and environmental performance have caused developing countries in general and automobile manufacturing firms in particular to consider and start implementing green supply chain management. It is emerging as an important approach which not only reduces environmental issues but also brings economic benefit to manufacturers. Recently, there are intensive studies on the issues, which have been dealt with extensively by practitioners and academicians. However, studies on performance evaluations are few. In responses this study explores the criteria that influence the performance of the automobile manufacturing industry, using the fuzzy set theory and Decision Making Trial and Evaluation Laboratory. The hybrid method evaluates its performance to find key criteria in improving the manufacturers’ green performance. Findings show that the increase of cost for purchasing environmentally friendly material is the most influential and significant criterion, while the pollution control initiatives is the most effective criterion. Managerial implications are also discussed and concluding remarks are made.

Academic research paper on topic "Green supply chain management performance in automobile manufacturing industry under uncertainty"

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Procedía

Social and Behavioral Sciences

ELSEVIER Procedía - Social and BehavioralSciences 25 (2011) 233 - 245

International Conference on Asia Pacific Business Innovation & Technology

Management

Green supply chain management performance in automobile manufacturing industry under uncertainty

Ru-Jen Lina*, Rong-Huei Chenb, Thi-Hang Nguyenc

Increasing pressures and challenges to improve economic and environmental performance have caused developing countries in general and automobile manufacturing firms in particular to consider and start implementing green supply chain management. It is emerging as an important approach which not only reduces environmental issues but also brings economic benefit to manufacturers. Recently, there are intensive studies on the issues, which have been dealt with extensively by practitioners and academicians. However, studies on performance evaluations are few. In responses this study explores the criteria that influence the performance of the automobile manufacturing industry, using the fuzzy set theory and Decision Making Trial and Evaluation Laboratory. The hybrid method evaluates its performance to find key criteria in improving the manufacturers' green performance. Findings show that the increase of cost for purchasing environmentally friendly material is the most influential and significant criterion, while the pollution control initiatives is the most effective criterion. Managerial implications are also discussed and concluding remarks are made.

©2011 Published by Elsevier Ltd. Selection and/or peer-review under re sponsibility of the Asia Pacific Business Snnovation and Technology Management Society

Keyword: green supply chain management, fuzzy set theory, Decision Making Trial and Evaluation Laboratory (DEMATEL)

1. Introduction

The world economy has been globalized dramatically since the World War II. Especially in automobile industry, globalization does not only mean the circulation of goods and services which is increasing in the combining of domestic market into international

Graduate School of Business and Management*,

Department of Business Administrationb, Lunghwa University of Science and Technology, No. 300, Sec. 1, Wanshou Rd., Guishan, Taoyuan, Taiwan, 33306,

Abstract

1877-0428 © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Asia Pacific Business Innovation and Technology

Management Society

doi:10.1016/j.sbspro.2011.10.544

market but also the creation of opportunities for enterprises that target domestic and international market (Gan, 2003; Tseng, 2009b). Taking these advantages, many countries have globalized their economies or outsourced in order to benefit from lower costs of production and services. The world's manufacturing situation has shown that this trend will be mainly carried out in Asia in the next decades (US-AEP, 1999). The increase of industrialization and globalization in developing countries creates more opportunities for manufacturing industry but concurrently increases environmental burden (Rao, 2002). This is due to the fact that some countries have been considered as the gathering points of end-of-life products for disposal from developed countries (Zhu et al., 2005); however, in the situation of lacking of the infrastructure or tools care for end-of-life products (Puckett and Smith, 2002). Hence, the environmental issue is weight down on these nations.

Environmental impact (e.g. air emissions) occurs at all stages of a product life cycle from resource extraction to manufacture, use, reuse, recycle, and disposal (Zhu et al., 2007). Hervani et al. (2005) proposed that green supply chain management (GSCM) practices which include green purchasing, green manufacturing, materials management, green distribution/marketing and reverse logistics refer to the involvement of environmental thinking into the supply chain management from the extraction of raw materials to product design, manufacturing processes, delivery of the final products to the consumers and end-of-life management (Srivastava, 2007). Hence, (GSCM) has emerged as an important approach to reduce environmental risks and environmental burdens in manufacturing and disposal as well as enhance profit and competitive advantages (van Hock and Erasmus, 2000)

Increasing pressures and challenges to improve economic and environmental performance have caused the countries in particular to consider and start implementing GSCM. Hence, the evaluation of performance in the industry under uncertainty is the main content of this study. GSCM performance includes four main aspects such as environmental, operational, and positive and negative economic performance (Zhu et al., 2005; Srivastava, 2007; Tseng 2009b, Tseng and Chiu, 2011). To aid in GSCM performance evaluation, this study utilized the fuzzy set theory and Decision Making Trial and Evaluation laboratory (DEMATEL) method. Fuzzy set theory, as defined by Zadeh (1965), is a mathematical computation method to describe and treat ambiguity in decision making. The decision maker states the preferences by linguistic terms what the fuzzy set theory is the solution for such linguistic preferences or uncertainty (Awasthi et al., 2010). The relations between cause and effect groups of criteria are then converted into a visual structural model (Hori and Shimizu, 1999), thus fuzzy DEMATEL method is considered beneficial in providing directions to determine the key criteria and seek the best ways for greening the automobile manufacturing processes. Hence, the fuzzy DEMATEL is considered appropriate for this study.

This purpose focuses on reflecting automobile manufacturing industry as well as the environmental performance of enterprises in developing countries while evaluating the GSCM performance's criteria. The contribution of this study is to help automobile manufacturers improve their environmental images and enhance competitive position worldwide in the context that automobile industry is still a potential industry in developing countries. This study is organized as follows. Section 1 introduces GSCM definitions and objectives. Section 2 presents the literature review related to GSCM and proposed criteria.

The fuzzy DEMATEL is described in section 3 to develop and validate the GCSM performance criteria. Section 4 demonstrates the steps conducted based on this method to establish the structural model. Section 5 discusses the implications of results and provides suggestions for future research. Concluding remarks are presented in section 6.

2. Green supply chain management

GSCM is defined to be the addition of green issues into supply chain management (Hervani et al., 2005). In addition, Zhu and Sarkis (2004) state that GSCM supply chain involves from suppliers to manufacturers, customers and reverse logistics throughout the so called closed-loop supply chain. Hervani et al. (2005) indicate there are various activities involving GSCM such as reuse, remanufacturing, and recycling which are embedded in green design, green procurement practices, total quality environmental management, environmentally friendly packaging, transportation, and various product end-of-life practices.

In the global economy, the automobile industry transforms rapidly with the dramatic expansion of leading automobile manufacturers (e.g. Honda, Toyota, General Motor, Ford, Daimler Chrysler, Suzuki, Hyundai, and Fiat) into the Asia region (Kumar and Bali Subrahmanya, 2010). Greening the automobile industry has been disputed in international energy and environmental policy studies. Green supply chain in automobile industry has become the main interest in many industrial fields. The evaluation and measurement of its performance is essential when environmental issues have been addressed all over the world (Olugu et al., 2010). However, there have been few studies exploring the issue of GSCM performance evaluation. Hence, applying green concepts into automobile manufacturing is essential to reduce environmental impacts, enhance market competition, and ensure regulation compliance (Gan, 2003). Zhu et al. (2008) claim that the automobile manufacturing industry in developing countries is a potential and promising industry because it creates a huge market, especially after entering WTO. However, automobile supply chains are lagging. For instance, Zhu et al. (2007) indicate that Chinese automobile industry is quite nascent and the recycling of used cars is not paid enough attention to. Facing environmental burdens, the Chinese government has enacted tighten environment regulations (Zhu et al., 2007). Hence, Chinese automobile enterprises have started to study GSCM experiences from international partners (Zhang and Peng, 2000). Other example is that Malaysia government has not been addressed environmental issues, especially end-of-life vehicles recovery (Amelia et al., 2009). Since Malaysian automobile industry develops rapidly, GSCM forces local automobile manufacturers and government to become concern about their environmental burdens (Eltayeb et al., 2011). For these reasons, GSCM is emerging as an important approach to reduce environmental risks and brings economic benefit to manufacturers (Diabat and Govindan, 2010).

2.1. ProposedDEMATEL method

Fuzzy set theory was firstly introduced by Zadeh (1965), which defines the notion of membership function in order to cope with different linguistic variables. The fuzzy set theory

has been utilized to classify geographic entities due to vague class definition since the beginning of the 1970s (Chang et al., 2011). A part of the truth in fuzzy is expressed by each number between 0 and 1, meanwhile crisp sets equivalent to binary logic (Tseng and Lin, 2009). Al-Najjair and Alsyouf (2003) indicated the advantage of using fuzzy is to reveal and treat ambiguous or imprecise judgments mathematically. Moreover, fuzzy logic is useful in coping with the ambiguity of human thinking and expression in decision making. It is a beneficial way to change linguistic terms into fuzzy number whereas solving the vagueness related to the process of linguistic estimation (Tseng and Lin, 2009).

Fontela and Gabus (1976) and Jassbi et al. (2011) propose the DEMATEL method as a structural modeling approach to present the criteria's cause- effect relationships in measuring a problem. This study utilizes the fuzzy DEMATEL method to analyze and evaluate the interrelationships among the criteria of GSCM performance in automobile manufacturing industry in developing countries. Through selected criteria affecting GSCM performance in the manufacturing industry derived from a review of literature, this study employs four criteria which are supposed to be suitable for evaluating GSCM performance in the automobile manufacturing industry to stand out in, namely environmental performance, positive economic performance, negative economic performance, and operational performance.

2.2. Proposed GSCM criteria

Environmental performance- Charles and Pao (2002) found that environmental performance is the integration of evaluating profits from enterprises and environmental impacts. ISO 14001 identified environmental performance as measurable results of an organization's management of its environmental aspects. ISO 14031 is defined as a process that focuses on evaluation of environmental performance. Since the amount of vehicles in developing countries has been dramatically increasing and causing alarming air pollution; concurrently, the development of transportation system and car usage has significantly increased the consumption of metal, oil and energy, which in turn leads to resource exhaust and directly affects environmental performance. The automobile manufacturing industry in developing countries, especially after entering WTO has become one of the most affected industries and need to implement environmental performance (Walsh, 2000; Zhao, 2004; Zhu et al., 2007).

Economic performance is the first priority for manufacturers in implementing GSCM. Proceeding from enterprises' global management, economic performance represents the economic yield of the profit. It includes the effects proceeded from its environmental action (Zhu and Sarkis, 2004; Zhu et al., 2008; Claver et al., 2007). Economic performance mentions the effective use of various inputs in the production's processes. It is divided into 2 categories: (1) positive and (2) negative economic performance. For evaluating economic performance in the automobile industry, this study defines related positive economic as the decrease of cost for energy consumption, material purchasing, waste treatment, and waste disposal. In contrast, the increase of investment and operational cost and costs for purchasing

environmentally friendly materials is referred as the negative economic performance (Zhu and Sarkis, 2004; Kumar and Subrahmanya, 2010).

Operational performance- Operations are the foundation of efficient distribution and manufacturing which in turn leads to financial returns. Measuring operational performance is needed when dealing with customer satisfaction, internal processes and activities. In this study, for selected categories for evaluating operational performance are identified to be: (1) scrap/ waste reduction, (2) quality improvement, (3) delivery improvement, and (4) capacity utilization improvement (Moriones and Merino, 2002).

Environmental performance has been well-established in automobile manufacturing industry in developed countries. Evidence is that in EU countries, the European Union ELVs Directive (2000/53/EC) requires manufacturers in the industry fields to conduct plans for reducing toxic material consumption, designing and producing vehicles to dismantle, reuse, remanufacturing, recovery and recycling the end-of-life vehicles; increasing material recycling, and ensuring components used do not contain mercury, hexavalent chromium, cadmium, or lead. This directive is seen as a factor to promote the establishment of an environmentally conscious automobile industry. However, developing countries tend not to focus on these issues. For instance, Malaysian National Automotive Policy has not solved the environmental impacts although it has brought Malaysia's automobile manufacturing industry a success and become the largest producer in South East Asia (Gerrard and Kandlikar, 2007; Amelia et al., 2009).

Zhu et al. (2007) highlight environmental issues in Chinese automobile industry and the tightening of government about environmental regulations. Christmann and Taylor (2001) suggested exporting and selling to foreign customers are two main drivers which enforce China firms to improve environmental performance. Foreign partners such as Ford, GM or Toyota have required the manufacturers to obtain the ISO 14001 certification (GEMI, 2001). In this study, four main categories to evaluate environmental performance of automobile industry are proposed, namely (1) pollution control initiatives, (2) the use of environment friendly technology, (3) the use of environment friendly materials, and (4) partnership with green organizations and environmental certification (Zhu et al., 2007; Awasthi et al., 2010).

Table 1. Selected criteria for evaluating GSCM performance

Aspects

Criteria

Environmental performance Pollution control initiatives (C1)

Use of environment friendly technology (C2) Partnership with green organizations and suppliers (C3) Environmental certification (C4)

Positive economic performance

Decrease of cost for materials purchasing (C5) Decrease of cost for energy consumption (C6) Decrease of fee for waste treatment (C7) Decrease of fee for waste discharge (C8) Increase of investment (C9)

Negative economic

performance Increase of operational cost (C10)

Increase of cost for purchasing environmentally friendly materials (C11) Operational performance Scrap/ waste reduction (C12)

Quality improvement (C13) Delivery improvement (C14) _Capacity utilization improvement (C15)_

3. Methodology

This section justified using linguistic information in complex evaluation systems. A complex evaluation environment can be divided into subsystems to more easily judge differences and measurement scores. The proposed hybrid method is used to construct a visual map for further strategic decision.

3.1. Fuzzy set theory

Some important definitions and notations of fuzzy set theory from Chang et al. (2011) were reviewed. The fuzzy aggregation method always needs to contain a defuzzification method because the results of human judgments with fuzzy linguistic variables are fuzzy numbers. The term defuzzification refers to the selection of a specific crisp element based on the output fuzzy set, which converts fuzzy numbers into crisp score. This study applies the converting fuzzy data into crisp scores, the main procedure of determining the left and right scores by fuzzy minimum and maximum; the total score is determined as a weighted average according to the membership functions.

Table 2. Linguistic scales for the importance weight of criteria

Five-points scale Linguistic variable Triangular fuzzy numbers (TFN)

1 No influence (0, 0.1, 0.3)

2 Very low influence (0.1, 0.3, 0.5)

3 Low influence (0.3, 0.5, 0.7)

4 High influence (0.5, 0.7, 0.9)

5 Very high influence (0.7, 0.9, 1.0)

3.2 The DEMATEL method

The DEMATEL method is especially practical and useful for visualizing the structure of complicated causal relationships with matrices or digraphs (Fontela & Gabus, 1976). The matrices or digraphs portray a contextual relation between the elements of the system, in which a numeral represents the strength of influence. Hence, the DEMATEL method can convert the relationship between the causes and effects of criteria into an intelligible

structural model of the system. The essentials of the DEMATEL method suppose that a system contains a set of criteria C ={C1,C2, ... , Cn}, and the particular pairwise relations are determined for modeling with respect to a mathematical relation.

3.3 The application procedures of fuzzy DEMATEL

To further explore the fuzzy DEMATEL research method in uncertainty, the analysis procedures are explained as follows:

Step 1: Identifying decision goal- gathering the relevant information to evaluate the advantages and disadvantages and monitoring the results to ensure the goals are achieved. This is necessary to form two expert committees for group knowledge to achieve the goals. Step 2: Developing evaluation criteria and survey instrument- this is important to establish a set of criteria for evaluation. However, the criteria have the nature of complicated relationships within the cluster of criteria. To gain a structural model dividing evaluation criteria into the cause and effect groups, the fuzzy DEMATEL is appropriate to be applied in this study. Acquiring the responded instrument- to ensure the relationships among the evaluation criteria, it is necessary to consult two groups of experts to confirm reliable information of the criteria influences and directions

Step 3: Interpret the linguistic information into fuzzy linguistic scale- using linguistic information to convert fuzzy assessments applying in Eqs. (5)-(9) are defuzziffied and aggregated as a crisp value

Table 3. The prominence and relation axis for cause and effect group

Criteria D (Sum ) R(Sum) (D+R) (D-R)

C1 3.844 4.692 8.536 0.848

C2 3.883 3.541 7.424 0.342

C3 3.711 3.223 6.934 0.488

C4 3.317 3.855 7.172 0.538

C5 3.183 3.973 7.155 0.790

C6 3.661 3.873 7.534 0.212

C7 4.095 4.231 8.325 0.136

C8 4.183 4.226 8.409 0.043

C9 3.745 2.972 6.717 0.773

C10 3.909 3.265 7.174 0.643

C11 4.770 3.701 8.471 1.070

C12 3.934 4.234 8.168 0.300

C13 3.439 3.267 6.706 0.172

C14 2.779 2.721 5.500 0.057

C15 3.339 4.018 7.357 0.679

Step 4: Analyze the criteria into causal and effect diagram- the crisp value is composed of the initial direct relation matrix. The normalized direct relation matrix can be obtained direct relation matrix can be obtained through Eq. 10. Using Eqs. (11)- (15), a causal and effect diagram can be constructed.

4. Results

This study applies the fuzzy DEMATEL to GSCM performance in order to build up a cause and effect model for automobile manufacturing enterprises. This research conducts four proposed steps as follows:

Step 1: Determining decision objectives and assembling the relevant information for developing 15 GSCM performance criteria to study the interrelationships of criteria in uncertainty.

Step 2: This study sets up above 15 criteria for evaluation that are presented in Figure 1 through interview and extensive literature reviews.

Step 3: Clarifying the linguistic information into fuzzy linguistic scale as shown in Table 1. This study collected data to evaluate criteria which affect automobile manufacturing industry and applied the Eqs. (5) - (9) and convert the fuzzy numbers into crisp value. Step 4: The crisp value of criteria from the fuzzy assessment is composed of the initial direct relation matrix. Once the normalized direct relation matrix is obtained and the total relation matrix is acquired by using formula. Then, the prominence axis (D+R) is created by adding D to R which involves in the importance of criterion and the relation axis (D-R) is created by subtracting D from R (Table 2). Hence, the causal and effect diagram can be obtained by mapping the data values of the (D+R, D-R) which will supply valuable insights for resolving problem those manufacturers are facing. The causal and effect diagram is shown in Figure 1.

The study findings are described in the causal diagram (Fig.2). The fifteen criteria can be divided into a causal and an effect group. The causal group includes "use of environment friendly technology" (C2), "partnership with green organizations and suppliers" (C3), "increase of investment" (C9), "increase of operational cost" (C10), "increase of cost for purchasing environmentally friendly materials" (C11), "quality improvement" (C13) and "delivery improvement" (C14). The effect group contains "pollution control initiatives" (C1), "environmental certification" (C4), "decrease of cost for materials purchasing" (C5), "decrease of cost for energy consumption" (C6), "decrease of fee for waste treatment" (C7), "decrease of fee for waste discharge" (C8), "scrap/ waste reduction" (C12) and "capacity utilization improvement" (C15). Obtaining results are valuable for making decisions from the causal diagram. These two cause and effect groups are used to analyze respectively as causal criteria and effective criteria in evaluating GSCM performance in developing automobile manufacturing industry.

5. Managerial implications

The framework includes environmental and non-environmental criteria that can be used to supervise and set up evaluation platform for GSCM performance of automobile manufacturing enterprises. This proposed framework can provide managers in developing countries with better understanding of the application of green issues in the manufacturing processes by examining the 15 criteria. Valuable cues can be drawn from the causal and

effect diagram to identify lucid decisions. Those decisions may then be used to resolve problems that the automobile industry is facing improve firms' environmental image and increase their competitive advantage worldwide.

Automobile manufactures should control and pay attention to the cause group criteria. The cause group criteria refer to the influencing criteria, whereas the influenced criteria are implied by the effect group criteria (Fontela & Gabus, 1976) The effect criteria (C1, C4, C5, C6, C7, C8 and C15) are shown to be more difficult to change than the cause criteria (C2, C3, C9, C10, C11, C13 and C14). Since the cause criteria are difficult to move while the effect criteria can be moved easily (Tseng, 2010), this study suggests that manufacturers should take the cause criteria into deep consideration in finding the best way to greening automobile manufacturing process.

The causal and effect structure from the causal diagram implies that "increase of cost for purchasing environmentally friendly materials" (C11) is the most critical cause criteria and also the most important among these 15 criteria. "Pollution control initiatives" (C1) is the most influential criteria. The "increases of cost for purchasing environmentally friendly materials" (C11) is the cause of all issues in automobile industry; its highest intensity relates to other criteria and the main objective are pollution control initiatives. It means this cause criterion may reach the effect criteria which are C1, C4, C5, C6, C7, C8, C12 and C15. Simultaneously, the result shows "delivery improvement" (C14) is less important in the cause group because this criterion is less important in the improvement of automobile manufacturing enterprise's environmental image.

The results imply that firms which desire to reduce environmental impact and enhance competitiveness in international business need to focus on GSCM through environmental tools and right investments or operations. In order to achieve pollution control and reduce the amount of scrap and waste, automobile manufacturers should consider the use of environmentally friendly materials and technology in addition to green supplier collaboration. Since environmentally friendly materials replace harmful materials and help enterprises reduce air pollution, scrap and waste, it reduces cost of material purchasing, waste disposal, and energy consumption. In other words, the application of green into the SCM and manufacturing processes can maximize the control on pollution to meet the stringent environmental regulations.

6. Concluding remarks

The fast growth of automobile manufacturing industry worldwide has created business opportunities but concurrently increased substantial environmental burdens (Rao, 2002). These burdens such as air emissions, scrap/waste, scarce resources occur at all stages of a product's life cycle from extraction for manufacture, use and reuse, recycle or disposal (Zhu et al., 2007). GSCM has emerged as an important approach to reduce these environmental burdens and improve green image and competitive advantage (van Hock and Erasmus, 2000). Hence, automobile firms at some developing countries such as China or Malaysia have implemented GSCM and started to learn experiences from international partners. In other

words, evaluating the performance in automobile manufacturing industry helps firms to comprehend environmental risks and the importance of GSCM in manufacturing process.

This study utilized the fuzzy DEMATEL method to analyze and evaluate the interrelationships among the criteria of GSCM performance in automobile manufacturing industry. The findings show that there are reliable results related to the evaluation of GSCM performance in automobile manufacturing industry by examining 15 criteria using fuzzy DEMATEL method. The results provide directions to automobile manufacturers in determining the key criteria and seeking the best guidance for improving environmental image and competitive advantage.

The results provide suggestions to the automobile industry manufactures. Firstly, cost of purchasing environmentally friendly materials is considered most important in the GSCM evaluation. In reality, this cost significant contributes to the increase of operational and investment cost, which is negative to business. However, manufacturers need to recognize that investments in environmentally friendly raw materials purchasing can help reduce cost of managing environmental impact, waste treatment, and energy consumption. In addition, it is essential to underline strongly the aspects of internal operations in enterprises. Automobile enterprises should improve product quality combining with the use of environmentally friendly materials, collaboration with green suppliers and stringent regulations to meet certain environmental requirements, improve firms' image as well as their positions worldwide.

This study contains several limitations that future studies need to have further examine. First, this study applies the fuzzy DEMATEL to conducts the evaluation of criteria through individual rather than a full-fledged industrial survey. Second, GSCM is still a fairly new concept which has not been widely implemented in the industry; hence, the expert system only bases on few industrial and professional experts (Olugu et al., 2011). Third, since the evaluation criteria were selected from GSCM performance evaluation related literature, some possible criteria influences may be eliminated. Future studies need to mention more studies as well as status of automobile industry in various countries to highlight the application GSCM performance in the industry. Future research can also use different methods to identify more criteria to justify the GSCM performance.

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