A Structural Equation Model Assessment of Lean Manufacturing

A STRUCTURAL EQUATION MODEL ASSESSMENT OF LEAN

MANUFACTURING PERFORMANCE

Tipparat Laohavichien

Department of Operations Management, Faculty of Business Administration

Kasetsart University, Thailand

[email protected]

Sawat Wanarat

Department of Operations Management, Faculty of Business Administration

Kasetsart University, Thailand

[email protected]

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

ISS & MLB―September 24-26, 2013

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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.

Figure 1 Research Model.

Lean

Practices

Organizational

Performance

Innovation

Performance

H 1

H 2 H 3


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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.


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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


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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


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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.

Lean Practices

- Setup time reduction

- Cellular manufacturing

- Quality improvement

Organizational Performance

- Lead time

- Inventory turnover

- Product rejection/return

- Sales level

- Cost reduction

- Meeting customers’ requirement

Innovation Performance

- Process innovation

- Product and service innovation

H 1

H 2

H 3


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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 (x2/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).


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TABLE 1: MEASURED IN THE RESEARCH

Factors Standardized

Coefficients

(Loadings)

Cronbach

Alpha

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

technological innovations

0.846

IP2: We use up to date/new technology in the process 0.875

IP3: We use the latest technology for new product

development

0.835

IP4: The process, techniques and technology

change rapidly in our company

0.843

IP5: We have enough new products introduced to

the market

0.776

IP6: We have new products which are first in market 0.946

IP7: The speed of new product development is fax

enough/competitive

0.953

IP8: We are technologically competitive 0.835

IP9: We are able to produce products with novelty

features

0.877


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TABLE 1 (CONTINUE): MEASURED IN THE RESEARCH

Factors Standardized

Coefficients

(Loadings)

Cronbach

Alpha

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’

requirement

0.787

OP6: Avoidance of product reject/return 0.764

Model fit: x2/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 (x2/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.


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RESULTS

Overall, the model had a very good fit with the data (x2/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.

Figure 3: The Results of SEM

Notes: **p = 0.01, *p = 0.05

0.680**

0.215*

0.601** Lean Practices

Innovation

Performance

Organizational

Performance


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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

Performance

0.215* - -

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,


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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

Chong, A. Y. L., Chan F. T. S., Ooi, K. B., and Sim, J. J. (2011). Can Malaysian firms

improve organizational/innovation performance via SCM? Industrial

Management & Data Systems, Vol.111, No.3. 410-431.

Cooney, R. (2002). Is ‘lean’ a universal production system? Batch production in the

automotive industry. International Journal of Operations & Production

Management, Vol. 22, No. 10, 1130-1147.

Danneels, E. (2002). The dynamics of product innovation and firm competences.

Strategic Management Journal, Vol. 23, No. 12, 1095-1121.

Doolen, T. L. and Hacker, M.E. (2005). A review of lean assessment in organizations:

an exploratory study of lean practices by electronics manufacturers. Journal of

Manufacturing Systems,Vol. 24, No. 1,55-67.

Fullerton, R. R. and Wempe, F. W. (2009). Lean manufacturing, non-financial

performance measures, and financial performance. International Journal of

Operations & Production Management, Vol. 29, No.38, 214-240.

Furlan, A., Vinelli, A. and Pont G. D. (2011). Complementarity and lean

manufacturing bundles: an empirical analysis. International Journal of

Operations & Production Management, Vol. 31, No. 8, 835-850.

Ketokivi, M. A. and Schroeder, R.G. (2004), Perceptual measures of performance:

fact or fiction? Journal of Operations Management, 22, 247-262.

Krafcik, J. F. (1998). Triumph of the lean production system. Sloan Management

Review, Vol.30, No. 1, 41-52.

Motwani, J. (2003). A business process change framework for examining lean

manufacturing; a case study. Industrial Management & Data Systems, Vol. 103,

No. 5, 339-346.

Ohno, T. (1988). Toyota Production System-Beyond Large Scale Production.

Cambridge: Productivity Press.


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Rahman, S., Laosirihongthong, T., and Sohal, A. S. (2010). Impact of lean strategy on

operational performance: a study of Thai manufacturing companies. Journal of

Manufacturing Technology Management, Vol.21, No. 7, 839-852.

Wayhan, V. B. and Balderson, E. L. (2007). TQM and financial performance: what has

empirical research discovered? Total Quality Management & Business

Excellence, Vol. 18, No. 4, 403-412.

Womack, J. P., Danial, T. J., and Daniel, R.(1990). The Machine that Changed the

World. New York: Simon and Schuster, 1990.

York, K.M. and Miree, C.E. (2004). Causation or covariation: an empirical

re-examination of the link between TQM and financial performance. Journal of

Operations Management, Vol. 22, No. 3, 291-311.

Zayko, M. J., Broughman, D. J. and Hancock, W.M. (1997). Lean manufacturing

yield world-class improvement for small manufacturer, IIE Solution, April, 36-40.


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A Structural Equation Model Assessment of Lean Manufacturing