The empirical analysis on supply chain risk management with firm capability perspective of Thailand automotive industry

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This research was conducted by questionnaire and interview data obtained from automotive manufacturers in Thailand. The framework is tested using data from a broad spectrum of small and medium enterprises (SMEs).

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Nội dung Text: The empirical analysis on supply chain risk management with firm capability perspective of Thailand automotive industry

International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 03, March 2019, pp. 1735–1744, Article ID: IJMET_10_03_175 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication Scopus Indexed THE EMPIRICAL ANALYSIS ON SUPPLY CHAIN RISK MANAGEMENT WITH FIRM CAPABILITY PERSPECTIVE OF THAILAND AUTOMOTIVE INDUSTRY Premkamon Jankaweekool Technopreneurship and Innovation Management Program, Chulalongkorn University, Bangkok, Thailand Thitivadee Chaiyawat Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand. Sukree Sinthupinyo Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand. ABSTRACT There are many problems in the quality of the auto parts from the substandard production. This research was conducted by questionnaire and interview data obtained from automotive manufacturers in Thailand. The framework is tested using data from a broad spectrum of small and medium enterprises (SMEs). The statistical analysis using confirmatory factor analysis (CFA) and the level of risks were determined by evaluating the probability of occurrence and severity of risk factors. The results showed the types of risks that arise in the automotive supply chain in a very high-risk level that must be urgently resolved. Its effect on firm capability is examined. The results generally support theoretical predictions and interesting findings emerge. Key words: Supply chain risk management, Automotive industry, Empirical analysis, Firm capability Cite this Article: Premkamon Jankaweekool, Thitivadee Chaiyawat and Sukree Sinthupinyo, The Empirical Analysis on Supply Chain Risk Management with Firm Capability Perspective of Thailand Automotive Industry, International Journal of Mechanical Engineering and Technology 10(3), 2019, pp. 1735–1744. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 1. INTRODUCTION Automotive industry is one of main industries in Thailand which generates economic value for the country. It accounts for 10% of gross domestic product originating from manufacturing and http://www.iaeme.com/IJMET/index.asp 1735 editor@iaeme.com
The Empirical Analysis on Supply Chain Risk Management with Firm Capability Perspective of Thailand Automotive Industry a source of employment for over 500,000 direct jobs of skilled workers in 2012 excludes the value created by other relevant industries, such as upstream industry and service industries in respect of finance, insurance and after-sales service. Thailand is a leading regional and global automotive manufacturer, the top tree rank among ASEAN countries and 18th production country rank in 2017 by The International Organization of Motor Vehicle Manufacturers (OICA: Organisation Internationale des Constructeurs d’Automobiles). A major regional production bases for motorcycle and automotive parts OICA [26]. Moreover, Thailand Automotive Institute reveals an expectation in the growth of the automotive industry in Thailand until 2050, which tends to have a higher production rate continuously [32]. According to its high production today, there are problems and risks in the quality of auto parts that have not met the standards. In 2014, Toyota, one of Japan's major car manufacturers, recalled 6.39 million cars worldwide because it found many different problems, such as driver's seat, steering column, and ignition system. Furthermore, Ford Motor, the second largest car manufacturer in the US, also announced a recall of approximately 434,700 cars because of a potential corrosion problem causing steering control, and secondly, the problem of rear seat frame made of substandard material that may increase the risk of injury if an accident occurs ([33], [34]). The major objective of this research, to analyze the status quo of supply chain risk management in Thailand based on studys conducted in the automotive industry. In particular, the purpose is to investigate the relevance of different risks in terms of their probability of occurrence and their potential impact to business [7]. Furthermore, several instruments of supply chain risk management are analyzed regarding their potential to cope with supply chain risks. Hence, the relationship between the implementations of instrument and the firm capability criteria are analyzed ([4], [6], [35]). Christopher [11] proposed supply chain risk management (SCRM) to be an important antecedent of firm capability leads to data collection from 346 firms, small and medium enterprises from Thailand automotive parts manufacturers, to test the model. The rest of this paper is organized as follows: section 2 provides a brief overview of literatures within the supply chain risk management context, the conceptual framework is presented, and a set of testable hypotheses is proposed. Section 3, the methodology are introduced, which include information about the sample, study measures. In section 4, the data analysis and results are considered to demonstrate the application of our proposed methodology. Section 5 presents the findings of the study. Finally, section 6 concludes the limitations and future research. 2. THE LITERATURE REVIEW & RESEARCH FRAMEWORK There is a continuous growth in the number of articles focusing on supply chain risk management in the past few years. In view of this, we reviewed the journal articles published between 1990 and 2017. The literature survey aims to understanding the important issues and mitigation techniques in SCRM, including the development tendency in this area. http://www.iaeme.com/IJMET/index.asp 1736 editor@iaeme.com
Premkamon Jankaweekool, Thitivadee Chaiyawat and Sukree Sinthupinyo Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Research Research Research Research Research Model Methodology Data Input Output’s Discussion Construction Evaluation Analysis and Conclusions • Define • Mixed • Data • Statisticall • Empirical the search Method Collection yanalyze Findings terms • Define • Data collected • The • Identify questions & collected data limitation the Likert scale from • Confirmat Research s of this • Validation practitioners ory Factor study Gap • Reliability Analysis Figure 1 Process of empirical research. The research process is as illustrated in Figure 1. First, various academic databases were utilized to identify the journal articles including Emerald, IEEE, Pro Quest, Science Direct, Springer, Taylor and Francis, and Wiley and Sons. The high level of relevance, and published in International Journals were selected. Second, the search terms were defined. With respect to criteria, abstracts of articles were examined to check by cover one or more of SCRM topics, including supply chain risk types, risk factors, risk management methods and research gaps identification. Although supply chain risk management has more attention in the past years, there needs for empirical work in this field of supply chain risk management analyzing the main supply chain risks and investigating instruments for an effective supply chain literature ([8], [9], [12], [29], [30], [31], [35]). The literatures were reviewed from the 1990s to 2010s. Tang and Nurmaya Musa [31] adopted the literature citation analysis that identified and classified potential risks associated with material flow, financial flow and information flow. Thun and Hoenig [35] revealed on research, a focus of managing supply chains in the improvement of cost-efficiency. Recently, the catastrophic Thailand flooding of October 2011 since it is an example of the disruptive risk impact both on industries and the whole economy, i.e., the automotive and electronics industries. The chronic floods affected the major industrial sectors with a disastrous impact on the supply chains of Japanese automotive companies with plants in Thailand [18]. The constructs are developed based on the literature. Based on the literature, supply chain risk management is conceived as composed of six components of SCRM. There are identified: demand risk, supply risk, process risk, information technology risk, financial risk and disruptive risk ([3], [6], [11], [12], [15], [19], [20], [30]). In order to control and mitigate the negative effects caused by such risks, a significant amount of work in this area is undertaken. Accordingly, the following three hypotheses are formulated concerning the probability and impact of typical supply chain risks. Hypothesis 1: The higher level of supply chain risk management have the greater of firm capability. Hypothesis 2: The higher level of managerial attitudes toward risk have more effect on firm capability. Hypothesis 3: The small and medium organizations, the moderating effect, greater firm size have stronger relationship between managerial attitudes toward risk and firm capability. 3. THE RESEARCH FRAMEWORK http://www.iaeme.com/IJMET/index.asp 1737 editor@iaeme.com
The Empirical Analysis on Supply Chain Risk Management with Firm Capability Perspective of Thailand Automotive Industry The framework in Figure 2 was derived from the academic researches on supply chain risk management and firm capability ([10], [12], [25], [29], [30], [35]. This research consider the factors that are concerned among SMEs and how those factors are linked together in order to develop a decision-making practice that support the risk management system. Demand Risk Managerial Attitudes toward on Supply Risk Risk A second-order construct H3 H2 Firm Size Process Risk Supply Chain Risk H1 Firm Management Capability Financial Risk Infomation Te chnology Ris k Disruptive Risk Figure 2: The conceptual model. Researchers have conducted that supply chain risks is associated with the development of managerial attitude towards risks, which is crucial for firm capability and firm performance [30]. An organization committed to possess state-of-the-art operational technology [21], which leads to greater innovation capability in both products and processes. Furthermore, supply chain risk management is positively related to firm performance [17]. 3. METHODOLOGY 3.1. Data Collection The data used to test the hypotheses via a survey of executives management from a broad of Thailand automotive industry. The sample frame of 346 small and medium‐sized enterprises (SMEs), there covered an assemblies and importers business, 1st to 3rd Tier Supplier: auto- parts manufacturers. The 889 key informants’ database were randomly drawn from Thailand Automotive Institute (TAI), Ministry of Industry. 3.2. Research measurement Data were obtained through a key informant technique, which is consistent with prior studies [23]. The survey was conducted in two periods with two weeks interval after the first mailing of introductory letters and online/off-line questionnaires, second reminder letters and questionnaires were sent out to correspondents. Ultimately, 432 usable questionnaires were restored, for a response rate of 48.59%. An effective response rate which is considered acceptable for a mail survey because it is greater than 20% [1]. The data was tested for non-response bias by comparing the responses of early respondents and late respondents via values of the population, as firm size and firm capital was recommended by Armstrong and Overton [2]. A multivariate T-test (The Hotelling–Lawley Trace) computed along the key study variables found no significant differences between the http://www.iaeme.com/IJMET/index.asp 1738 editor@iaeme.com
Premkamon Jankaweekool, Thitivadee Chaiyawat and Sukree Sinthupinyo early and late respondents. There were no statistically significant differences at a 95% confidence level as firm size (t = 0.183, p > 0.05) and firm capital (t = 0.131, p > 0.05). Supply chain risk management was measured through 47 observed variables assessing the level of risk cognizance that companies have concerned. The foregoing measurement can produce an outcome, all constructs in the model were measured with two pairwise matrix of Likert-Scale. There are the category of probability or likelihood against the category of consequence severity. In general, well-validated measures reported in previous research were used. When an item had to be modified or developed, through Churchill’s [13] multi-item measures and multiple validity methods are followed. 4. RESULTS AND ANALYSIS 4.1. Measurement validation The Confirmatory Factor Analysis (CFA) first-order indicators are supply risk, demand risk, process risk, information technology risk, financial risk and disruptive risk. Each of these variables was measured by two pairwise five-point Likert-type, ranging from 1 (strongly disagree) to 5 (strongly agree). Before testing this model, a series of tests was determined and removed the redundant items in a construct in obtaining a best fit model to establish the unidimensionality of the measures in Table 1. Table 1: Validity and reliability test Convergent Internal Construct maximum Validity Reliability Reliability shared Construct variable (AVE) (α) (CR) variance (MSV) C1 Demand risk .81 .94 .95 .26 C2 Supply risk .50 .80 .80 .29 C3 Process risk .60 .91 .90 .11 C4 Information technology .55 .80 .84 .36 risk C5 Financial risk .65 .90 .90 .36 C6 Disruptive risk .58 .84 .84 .16 C7 Managerial attitudes .61 .86 .86 .02 toward risk C8 Firm Capability .57 .87 .87 .17 4.2. First-Order Confirmatory Factor Analysis The first-order constructs including supply risk, demand risk, process risk, information technology risk, financial risk, disruptive risk, Managerial attitudes toward risk and firm capability. The validity of the measures was initially assessed by examining the reliability of the constructs and item-to-total correlation. Items with low item-to-total correlation were deleted. A series of chi-square difference tests was then conducted to ensure discriminant validity had been achieved (Farrell [16]). Table 2 presents the factor intercorrelation matrix, Construct correlations values (Phi) ranged from -.01 to .60, and most of the confidence intervals had significant (P < .01), which further confirms discriminant validity. http://www.iaeme.com/IJMET/index.asp 1739 editor@iaeme.com
The Empirical Analysis on Supply Chain Risk Management with Firm Capability Perspective of Thailand Automotive Industry Table 2 Construct correlation matrix (Phi values) Construct variable C1 C2 C3 C4 C5 C6 C7 C8 C1 Demand risk 1.00 C2 Supply risk .19** 1.00 C3 Process risk .32** .35** 1.00 C4 Information technology risk .23** .48** .54** 1.00 C5 Financial risk .23** .49** .42** .60** 1.00 C6 Disruptive risk .12* .22** .40** .32** .29** 1.00 C7 Managerial attitude towards on risk .07 .05 .12* .05 .04 .11* 1.00 C8 Firm Capability - .01 .21** .06 .25** .41** .11 .11* 1.00 * Significant at P < .05., ** Significant at P < .01. 4.3. Structural Equation Model of SCRM In the framework, supply chain risk management (SCRM) is a second-order construct composed of supply risk, demand risk, process risk, information technology risk, financial risk and disruptive risk in structural equation model as Figure 3. To establish that supply chain risk management is a single second-order factor, the null hypothesis that the first-order factors converge to a second-order construct was tested ([22], [24]). Figure 3 The structural equation model. http://www.iaeme.com/IJMET/index.asp 1740 editor@iaeme.com
Premkamon Jankaweekool, Thitivadee Chaiyawat and Sukree Sinthupinyo Table 3-A presents the loadings, t values, and fit indices from fitting this model to the data. As can be seen, the model fits the data quite well, the ratio of chi-square to degrees of freedom (CMINDF) is 1.829; the comparative fit index (CFI) is .926; the goodness-of-fit index (GFI) is .952; the adjusted goodness-of-fit index (AGFI) is .936; the root mean square error of approximation (RMSEA) is .044 at PCLOSE = .991. Table 3-A: Summary of Supply Chain Risk Management second-order measurement model. Standardized Construct Indicator (Factor) Regression Weights (CRa) Demand Inaccurate demand forecasts .39** (8.07a) Risk High level of service/product required by customers .34** (7.07) Demand uncertainty (/Bullwhip effect) .36** (7.50) Deficient customer relation management(CRM) function .88b 2. Supply Risk Supplier capacity .80** (6.99) Supplier selection/outsourcing .96** (7.03) Supply product monitoring(/quality) .84** (7.05) Sourcing flexibility risk .77b 3. Process Risk Product quality risk .84** (20.87) Manufacturing readiness level .78** (18.56) Production(/Technology) capability risk .80** (19.25) Error on safety inventory .79** (19.00) Process design risk .77b 4. Information The critical information flow risk .24** (4.32) Technology Error on IT to make decision and execute processes .35** (7.22) Risk Information system security and disruption .42** (8.36) Intellectual property: Information outsourcing(/sharing) .43** (8.15) The transition to new information technology systems risk .52b 5. Financial Debt and credit rating .51** (8.58) Risk Financial liquidity .59** (9.69) Economic recession .60** (9.81) Exchange rate risk .51b 6. Disruptive Natural disaster .21 ( .96) Risk Fire and accidents 1.55 ( .97) Government regulations (Tax and Law) risk .08b Table 3-B, There reveals factor loadings from the measurement item to respecting first- order constructs range from .09 to .99 and factors are significant at P < .01. Measurement of goodness of fit support the null hypothesis that the first-order factors converge to a single higher-order construct. The second-order construct explains by R2; Demand Risk 98%, Supply Risk 89%, Financial Risk 94%, Information Technology Risk 52%, in variation of the first- order factors respectively. At the same time, the variation of first-order factors process risk and disruptive risk are lower than the usually accepted level at P < .05 ([5],[38]). Thus, the second- order factor model was employed to represent a composite of supply chain risk management. Table 3-B: Standardized Regression Weights First-order Construct Supply Chain Risk Management (C.R.) 1. Demand Risk .99** (21.64a) 2. Supply Risk .96** (6.27a) 3. Process Risk .00 (- .02a) 4. Information Technology Risk .72** (10.67a) 5. Financial Risk .97** (8.45a) 6. Disruptive Risk .09 ( .84a) Goodness of fit: χ = 982.354, df = 537; CMINDF = 1.829; CFI = .926; AGFI = .936; GFI = .952; 2 RMSEA = .044; PCLOSE = .991 Notes: a Critical Ratio (Z-Test) from regression weights estimate are shown in parentheses. 4.4. The Result of Path Analysis http://www.iaeme.com/IJMET/index.asp 1741 editor@iaeme.com
The Empirical Analysis on Supply Chain Risk Management with Firm Capability Perspective of Thailand Automotive Industry Having satisfied the requirement arising from measurement issues, the structural model in Figure 3 was subsequently tested. The results are presented in Table 4. The coefficient on the path from supply chain risk management to firm capability is .41 (t = 7.57, P < .01). Hence, this positive relationship suggests that Hypothesis 1 is supported. The path coefficient from managerial attitudes toward on risk to firm capability is .13 (t = 2.46, P < .05), which supports Hypothesis 2. Managerial attitudes toward on risk significantly affects to firm capability. The structural model explains 16.60% and 1.70%, respectively, of the variance in the endogenous theoretical construct, firm capability. Table 4: Results of path analysis Paths Standardized Regression Weights (CRa) 1. Supply Chain Risk Management --> Firm Capability .41** (7.57a) 2. Managerial Attitudes Toward on Risk --> Firm Capability .13* (2.46a) a Notes: Critical Ratio (Z-Test) from regression weights estimate are shown in parentheses. 5. DISCUSSION AND CONCLUSIONS This research demonstrates the relationship between the sources of risks, supply chain risk management, and organizational capability among the Thailand automotive industry. The results support all but one of the hypotheses and reveal that SCRM is critical for firm capability. Based on the findings, a number of guidelines can be offered to both scholars and practitioners regarding the role of supply chain risk management in capability of innovation firm [11]. Distinctly finding that SCRM has a direct influence on firm capability (coefficient = .41, t = 6.48, P < .01). This study suggests that SCRM enhances an organizational performance directly and indirectly through its influence on effective and efficient manufacturing process, business image, good governance, competitive advantage and business sustainability. In line with that, the positive effect managerial attitudes toward risk have a positive significant relationship with firm capability (coefficient = .13, t = 2.39, P < .05). The exploratory analysis revealed how importance of risk mitigation strategies in their business practice through managerial attitudes toward risk can bring ways to help the company to prevent risks from occurring, which leads improvement in their performance and capability [30]. The empirical tests suggest that the effect of managerial attitudes toward risk to firm capability is affected by the size of organization. Medium enterprises are more likely to employ activities and turn it into risk management knowledge sharing. Small enterprises need to establish an efficient mechanism for rapidly internalizing process like “routines to learn routines” [14]. Firms tend to underestimate disruption risk in the absence of accurate supply chain risk assessment. Many managers tend to ignore possible events that are very unlikely. This may explain why few firms take commensurable actions to mitigate supply chain disruption risks in a proactive manner. Firms may rarely invest in improvement programs in a proactive manner because ‘‘nobody gets credit for fixing problems that never happened [28].’’ It is important to point out that managers should encourage employees to persevere supply chain risk management that may lie outside the immediate scope of their work. Managers are quite insensitive to estimates of the probabilities of possible outcomes and tend to focus on critical performance targets, which affect the way they manage risk. Through cross-functional integration, employees learn and develop new skills as well as share existing knowledge in SCRM, both are crucial for firm capability ([6], [31]). ACKNOWLEDGEMENT This research was supported by Technopreneurship and Innovation Management Program: the Graduate School, Chulalongkorn University, Thailand. We thank Prof. Dr.Achara Chandrachai, http://www.iaeme.com/IJMET/index.asp 1742 editor@iaeme.com
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