A STRUCTURAL EQUATION MODEL ASSESSMENT OF LEAN MANUFACTURING PERFORMANCE

A STRUCTURAL EQUATION MODEL ASSESSMENT OF LEAN MANUFACTURING PERFORMANCE Tipparat Laohavichien Department of Operations Management, Faculty of Business Administration Kasetsart University, Thailand fbustrl@ku.ac.th Sawat Wanarat Department of Operations Management, Faculty of Business Administration Kasetsart University, Thailand fbussww@ku.ac.th ABSTRACT The purpose of this paper is to empirically test a framework which identifies the relationships between lean practices, organizational performance and innovation performance of Thai manufacturing firms. Specifically, this study examines the direct effects of lean practices on organizational performance and whether innovation performance mediates the relationship between lean practices and organizational performance. A structural equation model (SEM) is estimated using data provided by 119 Thai manufacturing firms. The results show that lean practices have a direct and significant impact on organizational and innovation performance of Thai firms. Innovation improvement caused by lean practices also results in better organizational performance. The results of this paper show the importance of lean practices and how they directly influence organizational and innovative performance. This result will be encouraging to firm in other developing countries. Keyword: SEM, Lean practices, Innovative performance, Organizational performance INTRODUCTION The successful implementation of lean practices has become accepted by Toyota as source of competitive advantage (Doolen and Hacker, 2005; Womack et al. 1990). There are several studies that have examined the effects of lean on performance. The results showed that lean practices might not be universally valid in all organizational MLB 260

contexts (Boyle et al., 2011, Cooney, 2002). Many researchers confirmed that the relationship of lean on financial performance is mixed (York and Miree,2004; Boyd et al., 2006; Wayhan and Balderson, 2007). The study of Furlan et al. (2011) indicated that not all the plants implement lean manufacturing bundles show the improvement on operational performance. This paper investigates the relationship of lean practices on organizational performance and innovation performance, and the relationship of innovation performance on organizational performance of manufacturers in Thailand using a structural equation model (SEM). This allows us to evaluate whether the lean practices that are effective in advanced economies like Japan are also effective in a developing country like Thailand. The next sections of the paper review existing literature, explain the research methodology and the data analysis. The final section examines the results and provides conclusions and suggestions for future research. LITERATURE REVIEW The research model of this study is shown in Figure 1. The model proposed that lean practices implemented by Thai manufacturers improve their organizational and innovation performance. Also the improvement of innovation performance will improve the organizational performance. The lean practices, organizational performance and innovation performance are discussed in the next subsection. Lean Practices H 1 Organizational Performance H 2 H 3 Innovation Performance Figure 1 Research Model. MLB 261

Lean Practices Lean practices are designed to as one of inventory management system. With lean practices, manufacturer can reduce lead times through lower level of inventory (Bayou and de Korvin, 2008). The dominant principle of lean practices is waste elimination. Ohno (1988) classified wastes into 7 types as follows: defects, over-production, waiting for the next step, unnecessary transport or materials, unnecessary movement of workers, inappropriate processing, and excess inventory. Toyota in Japan claimed that the company significant improvements in cost and quality by lean implementation (Womack et al. 1990). Literatures show that there are a number of tools that are important for lean implementation. In this study, lean practices were measured in three bundles including setup time reduction, cellular manufacturing, and quality improvement (Fullerton and Wempe, 2009). Setup time reduction measures the extent to which the manufacturer does the following activities: (1) redesigns equipment to shorten setup time, (2) uses special tools to shorten setup time, (3) trains employees to reduce setup time, and (4) redesigns jigs or fixtures to shorten setup time. Cellular manufacturing measures the extent to which the manufacturer does the following activities: (1) groups equipment into product families, (2) groups equipment into families products that have similar processing requirements, (3) groups equipment into families products that have similar routing requirements, and (4) groups equipment into families products that have similar designs. Quality improvement measures the extent to which your firm does the following activities: (1) conducts process capability studies, (2) uses designs of experiments, and (3) uses statistical process control (SPC) charts. MLB 262

Organizational Performance Many researches showed that lean implementation effect organizational performance. Motwani (2003) mentioned that lean practices eliminate wastes and improve process. Krafcik (1998) stressed that lean practices improve quality, productivity, and customer responsiveness. Rahman et al. (2010) stated that lean practices can reduced lead times in production and increase velocity and flow in the supply chain. In addition, lean practices can reduce human effort, tool investment, product development time, and manufacturing space (Zayko et al., 1997). In this study, organizational performance adopted the same items from Chong et al. (2011). Six organizational performance measures in this study are lead time, inventory turnover, product rejection/return, sales level, cost reduction, and meeting customers requirement. In this study, lead time is defined as the time between the customer orders is made and the customer orders are completely satisfied. Inventory turnover measures the speed of goods move through and replenished by the system. Product rejection/return measures by comparing the manufacturers current product rejection or return rate with the industrial standard. Sales level is measured by evaluating whether the manufactures sales level is equal, above, or below the standard of the industry they are. Cost reduction is measured by evaluating whether the manufacturers cost is higher, equal, or lower than their industrial competitors. In addition, manufacturers were asked to respond whether they are lagging, below averaged, average, above, or the leader in the industry in terms of meeting customers requirement. Innovation Performance Many studies suggest that lean practices are the wide-ranging encompassing product development, collaboration with customers and pipelining a process from suppliers to customers (Bhasin, 2011). Through lean practices, manufacturers need to MLB 263

share information internally (e.g. engineers, product designers, and marketing employees) and externally (e.g. customers, suppliers, and distributors). Therefore, the organization that implements lean practices should evidence the better innovation performance than the one without lean implementation. Danneels (2002) mentioned that innovation happened when organizations have competences relating to customers and technologies. In this study, innovation performance adopted the same items from Chong et al. (2011). Two innovation performance measures in this study are process innovation and product innovation. In this study, process innovation is defined as the changes in product delivery and/or development processes as defined by method, functionality, administration, or other features. There are four items to measure process innovation including: (1) we are fast in adopting process with the latest technological innovations; (2) we use up-to-date/new technology in the process; (3) we use the latest technology for new product development; and (4) the process, techniques and technology change rapidly in our company. Product innovation is defined as the changes in the products or products features. There are five items to measure product innovation including: (1) we have enough new products introduced to the market; (2) we have new products which are first in market; (3) the speed of new product development is fax enough/competitive; (4) we are technologically competitive; and (5) we are able to produce products with novelty features. Based on a review of the literature, the research model in the level of variables is shown in Figure 2. The hypotheses of this study are based on Figure 2 as following: H 1 Implementation of lean practices has a positive influence on organizational performance H 2 Implementation of lean practices has a positive influence on innovation performance H 3 Innovation performance has a positive effect on organizational performance MLB 264

Lean Practices - Setup time reduction - Cellular manufacturing - Quality improvement H 1 Organizational Performance - Lead time - Inventory turnover - Product rejection/return - Sales level - Cost reduction - Meeting customers requirement H 2 H 3 Innovation Performance - Process innovation - Product and service innovation Figure 2 Research Model in the Level of Variables. RESEARCH METHODOLOGY Sample and Data Collection A survey instrument was developed in order to test the research model. The items and questions in the proposed questionnaire were adopted existing studies. The questionnaire was pre-tested with several senior executives from a manufacturing firm to ensure that the wording and format of the questions were appropriate. Data for this study were collected using a self-administered questionnaire that was distributed to 550 Thai manufacturing firms. The sample was selected randomly from the Thailand Manufacturers Directory. The data collections took nine months and were collected from April 2012 to December 2012. The survey was completed by senior officer in the firms. Out of the 550 surveys sent out, 119 were returned, yielding a response rate of 21.63 per cent. MLB 265

Variable Measurement The scale of lean practices (LP), which included 11 items, was adapted from Fullerton and Wempe (2009). For the innovation performance (IP), 9 questions were used to measure process innovation that adapted from Chong et al. (2011). The scale of organizational performance (OP), three was designed to measure that adapted from Chong et al. (2011). The survey used a five-point Likert-type scale (1= strongly disagree, 5 = strongly agree) for measuring lean practices and innovation performance. Table 1 specifies the items used in each variable measurement. Validation of Measures Before testing conceptual model, several reliability and validity issues need to be addressed. First, the reliability of scales was measured by Cronbach s alpha. In this study, all values of Cronbach s alpha ranged from 0.78 to 0.88 (see Table 1). Usually Cronbach s alpha of 0.7 or above was considered to be criteria for internal consistency of the established scales (Bagozzi and Yi, 1998). Second, the confirmatory factor analysis was used to assess the convergent and discriminate validity of measures with structural equation modeling. The measurement model fit the data (x 2 /df = 2.174 GFI 0.961, AGFI = 0.926, RMSEA = 0.067, TLI = 0.966, CFI = 0.978) and all factor loadings were highly significant (p < 0.001), which indicated the unidimensionality of the measures (Anderson and Gerbing, 1998). MLB 266

TABLE 1: MEASURED IN THE RESEARCH Factors Standardized Coefficients Cronbach Alpha (Loadings) Lean practices Setup time reduction 0.78 ST1: Redesigns equipment to shorten setup time 0.864 ST2: Uses special tools to shorten setup time 0.895 ST3: Trains employees to reduce setup time 0.884 ST4: Redesigns jigs or fixtures to shorten setup time 0.855 Cellular manufacturing 0.867 CM1: Groups equipment in product families CM2: Similar processing requirements 0.723 CM3: Similar routing requirements 0.865 CM4: Similar designs 0.746 Quality improvement 0.754 QI1: Conducts process capability 0.835 QI2: Uses designs of experiments 0.956 QI3: Uses statistical process control (SPC) chart 0.854 Innovation performance 0.88 IP1: We are fast in adopting process with the latest 0.846 technological innovations IP2: We use up to date/new technology in the process 0.875 IP3: We use the latest technology for new product 0.835 development IP4: The process, techniques and technology 0.843 change rapidly in our company IP5: We have enough new products introduced to 0.776 the market IP6: We have new products which are first in market 0.946 IP7: The speed of new product development is fax 0.953 enough/competitive IP8: We are technologically competitive 0.835 IP9: We are able to produce products with novelty features 0.877 MLB 267

TABLE 1 (CONTINUE): MEASURED IN THE RESEARCH Factors Standardized Coefficients Cronbach Alpha (Loadings) Organizational performance: 0.82 OP1: Cost reduction 0.765 OP2: Lead time minimization 0.744 OP3: Level of sales 0.767 OP4: Inventory turnover 0.774 OP5: Effectiveness in meeting customers 0.787 requirement OP6: Avoidance of product reject/return 0.764 Model fit: x 2 /df = 2.174 GFI 0.961, AGFI = 0.926, RMSEA = 0.067, TLI = 0.966, CFI = 0.978 Data Analysis To test the research hypotheses, structural equation modeling was performed using AMOS 16 software. Compared with conventional analytical techniques in the literature on lean practices, organizational performance and innovation performance such as correlation analysis, structural equation modeling (SEM) has the following advantages (Anderson and Gerbing, 1988). First, it can estimate relationships among latent constructs indicated by observed variables. Second, it can measure recursive relationship between constructs. Third, it can allow for correlations among measurement errors. SEM used several goodness-of-fit indices, including Chi-Square statistics divided by the degree of freedom (x 2 /df) was recommended to be less than 3, Goodness-of fit (GFI), Adjusted goodness-of fit (AGFI, Comparative Fit Index (CFI), Tucker-Lewis (TLI) were recommended to be greater than 0.90; and Root Mean Square Error of Approximation (RMSEA) was recommended to be 0.05 up and acceptable up to 0.08. MLB 268

RESULTS Overall, the model had a very good fit with the data (x 2 /df = 2.174 GFI 0.961, AGFI = 0.926, RMSEA = 0.067, TLI = 0.966, CFI = 0.978) and all of the paths were significant at the level of 0.001. Figure 3 was drawn on the basis of the results of structural equation modeling by AMOS 16.0. Figure 3 showed that lean practices had a significant positive influence on organizational performance (its standard coefficient was 0.601with significance level of 0.01), which supported H1. At the same time, lean practices had positive effect on innovation performance (its standard coefficient was 0.680 with significance level of 0.01), which supported H2. Innovation performance also had positive effect on organizational performance (its standard coefficient was 0.215 with significance level of 0.05), which supported H3. Lean Practices 0.601** 0.680** Organizational Performance Innovation Performance 0.215* Figure 3: The Results of SEM Notes: **p = 0.01, *p = 0.05 MLB 269

Table 2 shows the total effects, direct effects and indirect effects corresponding to Figure 3. As shown in Table 2, lean practices had a direct positive influence on organizational performance, while lean practices had indirect positive influence on organizational performance TABLE 2 SEM RESULT: THE TOTAL EFFECTS, DIRECT EFFECTS AND INDIRECT EFFECTS Construct Direct Effect Indirect Effect Total Effect Lean practices Organizational Performance 0.601** 0.146** 0.747** Lean practices Innovation Performance 0.680** - - Innovation Performance Organizational 0.215* - - Performance Notes: n = 119. Measurement models are estimated using ML. Bootstrapping is required in AMOS to determine the statistical significance of direct and indirect. **p = 0.01, *p = 0.05 CONCLUSION This study has provided empirical justification for the proposed research model which investigates the relations between lean practice, organizational performance and innovation performance among Thai manufacturing firms. Previous studies have suggested that lean practices had significant positive effect on organizational performance (Rosemary 2008; Motwani 2003; Krafcik 1988; Rahmai et al. 2010). Extending, this study has empirically examined how lean practices influenced organizational performance by introducing an important mediator Innovation performance. In addition, this study showed that lean practices are applicable to developing countries like Thailand. This study suggests the international managers that lean practices are universal tools to complete in today manufacturing. Moreover, MLB 270

international managers should be aware of mediating effect of innovation performance to organization performance. More effort in research and development could be improve the organization performance. There are some limitations of this study. There was only one respondent per company, so there is the possibility of common method variance (Ketokivi and Schroeder, 2004). And this study used self-reported lean practices, organizational performance and innovation performance, which may allow common method bias. REFERENCES Anderson, J.C. and Gerbing, D.W. (1988), Structural equation modeling in practice; a review and recommended two-step approach, Psychological Bulletin, No. 103, pp. 411-23 Bagozzi, R. and Yi, Y. (1988), On the evaluation of structural equation models, Journal of the Academy of Marketing Science, Vol. 16 pp. 74-94 Bayou M. and de Korvin A. (2008). Measuring the leanness of manufacturing systems a case study of Ford Motor Company and General Motors, Journal of Engineering Technology and Management, Vol. 25, 287 304. Bhasin, S. (2011). Measuring the leanness of an organisation. International Journal of Lean Six Sigma, Vol. 2, No. 1, 55-74. Boyd, D. T., Kronk, L.A., and Boyd, S.C. (2006). Measuring the effects of lean manufacturing systems on financial accounting metrics using data envelopment analysis. Investment Management and Financial Innovations, Vol. 3, No.4, 40-54. Boyle, T. A., Scherrer-Rathje, M., and Stuart, I (2011). Learning to be lean: the influence of external information sources in lean improvements. Journal of Manufacturing Technology Management, Vol.22, No.5, 587-603. MLB 271

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