Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.17 No.4 pp.783-795

Structural Equation Modelling based Empirical Analysis of Operational and Technological Factors for Lean Implementation

Protik Basu*, Pranab K Dan
Army Institute of Management, Judges Court Road, Alipore, Kolkata, India
Rajendra Mishra School of Engineering Entrepreneurship, Indian Institute of Technology, Kharagpur, India
* Corresponding Author, E-mail:,
October 29, 2017 February 9, 2018 April 10, 2018


This study attempts to explore the impact of operational and technological factors (OTFs) towards successful implementation of lean manufacturing (LM). Based on extensive literature survey, the authors endeavoured to develop an exhaustive list of all the input manifests related to the OTFs necessary for LM implementation, coupled with a similar exhaustive list of the benefits likely to be accrued from the successful implementation of LM. The critical analysis of collected data identified seven key input constructs and three output constructs which should act in unison to maximize the benefits of implementing lean. Through the structural model presented in this paper, nine hypotheses were proposed and tested for validation by the data collected from Indian manufacturing companies. Seven hypotheses were validated while two hypotheses were not supported. Reasons behind the non-acceptance of the two hypotheses have been explored and mentioned. This structural model can be considered as a guide to integrate the OTFs to successfully implement lean. The novelty of this work lies in the fact that this is one of the very rare studies that attempted to have a survey-based empirical analysis for evaluating the impact of such factors towards an effective lean implementation in the Indian manufacturing sector.



    Lean management is an integrated socio-technical system to induce a competitive advantage within an organization. In recent times, Lean Manufacturing (LM) has gained increasing importance (Shah et al., 2008) and acceptance as a strategy for effective performance improvement in different industries (Susilawati et al., 2015). Though the concept of lean has never been a single-point invention but the outcome of a dynamic learning process that has evolved from the automotive and textile sectors as a response to environmental uncertainties in Japan (Holweg, 2007), the term “lean” was coined by Krafcik (1988) to denote a production strategy that aims to reduce the usage of resources of all kinds, in contrast to traditional mass production system. Derived originally from the Toyota Production System (TPS), the principles, methods and tools of LM became immensely popular after the release of the book ‘The Machine that Changed the World’ (Womack et al., 1990) and lean practices became globally acceptable beyond the limits of Japanese cultural centeredness; as those became effectively transferable to other countries and organisations (Holweg, 2007).

    Although there have been quite a few studies focusing on the various aspects of lean implementation (Stone, 2012), a comprehensive study of the operational and technological factors (OTFs) which are crucially significant for the implementation of lean, is not available. The purpose of this paper is to have a detailed study of the influence of such factors on lean implementation and the effect of LM towards satisfaction of the customer, as well as its impact on the organizational objectives. This study entails an empirical research on a model for LM implementation centering a critical study on relevant literature. This study provides an understanding in a realistic situation in LM implementation where practically all considered input manifests related to operational and technological factors and all considered output manifests have been assimilated to formulate the research problem. Therefore the focus is to develop a research strategy that combines all the stated objectively identified variables in order to gain insights of an integrated model that would provide effective directions for LM implementation.

    In November 2011, the Government of India (GOI) announced a National Manufacturing Policy (NMP) with the observation that the share of manufacturing in India’s GDP has stagnated at 15-16% since 1980 in contrast to the share of manufacturing (25-34%) in other Asian economies (NMP, 2011). Considering the current impetus on developing the industrial sector, the GOI recently introduced the ‘Lean Manufacturing Competitiveness Scheme’ (LMCS) that aims to increase competitiveness with the help of lean concepts (LMCS, 2013). Hence, there is a huge scope to enrich the Indian industries with the lean benefits, the implementation status being quite low. There have been a few empirical studies on lean in the Indian manufacturing sector but the scope of those papers is mostly restricted to a few case studies centering on the assessment of current status of LM.

    This paper attempts to evaluate the Indian manufacturing industries, through an in-depth empirical research, for the influence of the OTFs on lean manufacturing implementation by using Structural Equation Modelling. The novel aspect of this study is that it focuses on an integrative perspective of the OTFs for implementation of LM. No such exhaustive study is available on extant literature on the input OTFs and the output factors of lean implementation, especially in the Indian scenario.

    This paper is organized as follows. Section 2 discusses the related literature and develops the hypotheses, based on which the research model is established. Section 3 outlines the research methodology and the model validation is carried out in Section 4. Section 5 discusses the theoretical and managerial implications of the results and finally Section 6 concludes the study with a summary, limitations and suggestions for future research.


    Womack et al. (1990) defined lean as the combination of the advantages of craft and mass production, while “avoiding the high cost of the former and the rigidity of the latter.” The term “lean” is used to specify that less of everything is being used in lean production and this “everything” includes inventory, human effort, manufacturing space, investment in tools and engineering hours to develop a new product. Lean focuses on perfection in all activities.

    2.1. Literature Review

    This research article is based on a systematic literature review. By a different combination of key words, 418 publications in refereed journals were finally shortlisted for this research work. The target journal articles for this review were those published after 1990 that is, post publication of the book, ‘The Machine that Changed the World’ (Womack et al., 1990).

    The manufacturing era had started with craft manufacturing. Thereafter, there evolved the age of mass production, where same item was produced in large numbers to take advantage of economies of scale. More recently, there is the emergence of the concept of LM to increase productivity and achieve significant improvements in the industry (Susilawati et al., 2015). In the past few decades much attention has centred on lean and many researchers have contributed to the definition of LM (Vinodh and Chintha, 2011). Bhamu and Singh Sangwan (2014) have provided 33 such definitions of lean in a chronological order, “reflecting the changing goals, principles and scope.” Being a multi-dimensional concept (Shah and Ward, 2003), lean includes a variety of management principles and practices under its umbrella and recently rigorous academic research has highlighted a broad set of such practices under LM (Shah et al., 2008) to reduce cost through the persistent removal of wastes and through the simplification of all manufacturing and support processes (Kajdan, 2008). Focus is to produce only what is demanded by the customer and only at the necessary time and quantity, thereby eliminating waste and utilizing resources efficiently (Chavez et al., 2015).

    Though there have been a few hundreds of papers on lean, there is hardly any which focuses on the comprehensive study of the OTFs towards a successful implementation of LM. This paper endeavours to fill the gap and attempts to offer an exhaustive list of the relevant parameters. The present study seeks, specifically in the context of the Indian manufacturing industries, to have an all-inclusive model integrating the OTFs for successful lean implementation. Role of human resources in LM implementation in India is an extensive area of research by itself and has not been included in this study since the focus of this paper is on OTFs. Moreover, this study concentrates only on internal variables and hence, external variables involving customers and suppliers have also been excluded from the present scope. The model developed can be used by academicians and practitioners to control and have the desired results by effective utilization of the OTFs in its entirety and thereby to administer and accelerate the lean implementation process.

    2.1.1. Identification of Input Manifest Variables and Latent Constructs

    In the first phase of the research, an intense literature survey revealed eighty four various terminologies related to OTFs as enablers for LM implementation. Since all of these 84 items were not distinct in nature, these were verified and based on the similarity of the attributes, combined into 32 manifest variables through a Delphi exercise with seven practitioners and researchers known for their knowledge and experience in lean production. These 32 manifest variables were conceptually mapped onto 7 distinct latent constructs, considering the salient inherent similarities between them. Finally, through an Exploratory Factor Analysis (discussed in Section 4.2 below), these 32 measures were reduced to 29 manifests, grouping of which matched with the 7 latent constructs. The latent constructs and their respective manifest variables are enumerated in Table 1.

    2.1.2. Identification of Output Manifest Variables and Latent Constructs

    Many studies have enumerated the benefits of lean. In the first phase, this study identified 215 various terminologies as output attributes from literature survey. These were again verified and based on the similarity of the attributes, combined into 13 manifest variables through a Delphi exercise with seven lean experts and researchers. The 13 manifest variables are conceptually mapped onto three distinct latent constructs, considering the salient inherent similarities between them. These are: Successful LM Implementation, Organizational Goal Satisfaction and Customer Satisfaction. From past literature, it may be inferred that successful LM implementation will lead to satisfaction of both the customer as well the organizational goals. Exploratory Factor Analysis (discussed in Section 4.2 below), confirms the said conceptual mapping. The latent constructs and their respective manifest variables are enumerated in Table 2.

    2.2. Hypotheses Development

    This study is built on the conceptual foundation of the literature followed by Delphi technique and EFA on the input and output variables of LM implementation, and combines both to propose the theoretical research model as illustrated in Figure 1.

    In this model, there are 9 hypotheses which are being tested for validity. Based on past literature, the hypotheses posited in this study include the following:

    • H1. Integrative planning and scheduling (IPS) is positively related to successful LM implementation (SLM).

    • H2. Internal operations synchronization (IOS) is positively related to successful LM implementation (SLM).

    • H3. Management information system (MIS) is positively related to successful LM implementation (SLM).

    • H4. Product design and development (PDD) is positively related to successful LM implementation (SLM).

    • H5. Quality governance (QG) is positively related to successful LM implementation (SLM).

    • H6. Strategic process control (SPC) is positively related to successful LM implementation (SLM).

    • H7. Role of technology (RT) is positively related to successful LM implementation (SLM).

    • H8. Successful LM implementation (SLM) is positively related to organizational goal satisfaction (OGS).

    • H9. Successful LM implementation (SLM) is positively related to customer satisfaction (CS).


    The objective of this work is to have an empirical study on the comprehensible list of input and output variables related to LM implementation in the Indian manufacturing context. The basic steps of the research methodology, based on the outline provided by Singh Sangwan et al. (2014), are: identification of input and output manifest variables and latent constructs, model proposition and hypotheses development, development of survey instrument, sampling and data collection and model validation (EFA, CFA and SEM). Identification of the input and output manifest variables and latent constructs, thereby proposing the research model and developing the hypotheses have been presented in the previous section. This section focuses on the development of the survey instrument, sampling and data collection.

    3.1. Development of Survey Instrument

    An extensive questionnaire covering the input and output manifest variables was developed. The survey questionnaire was divided into three major parts. The first part of the survey questionnaire was designed to capture the personal information and organization profile of the respondent. The second and third parts captured the responses on the input and output manifests. The present study used five-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Indifferent, 4 = Agree, 5 = Strongly agree) to get the responses for each item on the perception about the importance/significance of a manifest variable involved in LM.

    3.2. Sampling and Data Collection

    The empirical research is focused on lean implementation in the Indian manufacturing sector. The list of manufacturing companies was obtained from Capitaline Plus, one of India’s most popular and up-to-date databases. The population of interest was all industries with manufacturing facilities in India and having number of employees exceeding 100 (Chavez et al., 2015;Shah and Ward, 2003). This excluded those involved in agriculture, forestry, fishing and services. An initial list of 454 such manufacturing facilities was formed. Each company was contacted over phone to see their interest to participate in the survey. Executives in the managerial level were considered who were directly involved in the manufacturing process. Subsequently questionnaires were sent and visits were made to collect responses. Multiple responses from the same organization were also considered. A total of 782 respondents were contacted and after three to four follow-up contacts, a total of 467 usable filled-in (complete) questionnaires were received, ignoring 44 questionnaires consisting of missing responses on various manifests. Non response bias was investigated by comparing early respondents to late respondents, depending on the return date (Fullerton et al., 2014). Very few responses were received after the first contact. Early responders (n = 193) were classified as those that responded following first two contacts and late responders (n = 274) as those who responded after three or four contacts. No statistically significant differences were found between early and late respondents for any of the variables in the research model, implying an absence of non-response bias.


    The hypotheses are examined using a structural equation model (Figure 1) populated with survey data from 467 responses from the Indian manufacturing sector.

    4.1. Content Validity

    The comprehensive list of manifest variables was developed using extensive literature survey, based on which, initially 32 input manifest variables and 13 output manifest variables were identified through a Delphi exercise with seven practitioners and researchers known for their knowledge and experience in LM implementation. Further, to assess content validity, a panel of experts from industry and academics was requested to go through the questionnaire. Their remarks and suggestions were taken into consideration and several questions were rephrased and modified in the final instrument to receive meaningful responses.

    4.2. Exploratory Factor Analysis (EFA)

    EFA) A principal-components-based EFA was conducted on both input and output manifests to develop a parsimonious representation for the various constructs in the survey (Fullerton et al., 2014), to rule out variables not appropriately related to the constructs (Moori et al., 2013) and to reduce and summarize the data (Fullerton and Wempe, 2009). Factors were extracted using maximum likelihood method followed by varimax rotation. Kaiser criterion (Eigen values > 1) was employed to extract factors. For all items to contribute well to the represented factors, the minimum factor loading value of 0.45 was considered (Hair et al., 2009). The results indicate that 29 items could be identified as input manifest variables as compared to original 32 items. No item was eliminated out of the output manifests. The same 7 input latent constructs (with Eigen values > 1) emerged from the input manifests and 3 output latent constructs (with Eigen values > 1) emerged from the output manifests. Moreover, according to Podsakoff et al. (2003, p. 889), if a “substantial amount of common method bias is present, either (a) a single factor will emerge from the factor analysis, or (b) one general factor will account for the majority of the covariance among measures.” In this analysis, only 9.8% of the variance was explained by the first factor in case of input factors and 27.2% of the variance was explained by the first factor in case of output factors. Hence, it may be concluded that that the level of common method bias that may exist in the data is low and not of significant concern.

    EFA alone is not sufficient to assess all the essential measurement properties of the constructs (Singh Sangwan et al., 2014). There are two measurement models which are to be evaluated – a measurement model for the input constructs and another measurement model for the output constructs. In this study, each of these measurement models was evaluated with a confirmatory factor analysis (CFA) and then the full model was evaluated using a structural equation model (SEM). This two-step modeling approach was recommended by Schumacker and Lomax (1996) and was also followed by Fullerton et al. (2014). Results of EFA and SEM are discussed in the following two sections. The measurement model assesses the convergent validity and discriminant validity, and the full model is used for the assessment of predictive validity (Fullerton et al., 2014). To test both the measurement models and the structural model, the maximum likelihood approach in AMOS 21 was used.

    4.3. Confirmatory Factor Analysis (CFA)

    Results from CFA for individual manifests are indicated in Table 3. The t-values are all significant to p < 0.000. The factor/latent correlation matrix (Table 4) indicates that the factors are positively and significantly correlated with one other. Construct validity is assessed by both convergent and discriminant validity (Ghobakhloo and Hong, 2014). Convergent validity is found to be adequate because AVE (Average Variance Explained) of each variable is significantly greater than 0.5 (Fornell and Larcker, 1981). For satisfactory discriminant validity, a construct is considered to be distinct from other constructs if the square root of the AVE for it is greater than its correlations with other latent constructs (Barclay et al., 1995). The square root value of the AVE for each construct is indicated on the diagonal of Table 4 in boldface and is found to be larger than the correlation of that construct with all other constructs in the model. Hence the results satisfy the discriminant validity. Cronbach’s α- coefficients are used to test internal consistency or reliability of each construct (Cronbach, 1951) and the acceptable standard of Cronbach’s α-coefficient is 0.70 (Nunnally, 1978). Since, all the α-coefficients have values more than 0.7 (Table 3), they are acceptable. However, Cronbach’s α assumes equally weighted measures. Hence, the composite reliability values (they do not assume equal weighted measures) are also assessed (Table 3) and are found to be above the acceptable standard of 0.7 (Fornell and Larcker, 1981).

    4.4. Structural Equation Modelling (SEM)

    Table 5 and Figure 1 exhibit the results of the structural model. Before the path coefficients can be assessed, the fitness of the structural equation model is evaluated. The ratio of χ2 to degrees of freedom is found to be 1.812, indicating an acceptable fit (Kline, 2015). Value of RMSEA is 0.031 which is much less than the acceptable upper limit of 0.08 (Browne and Cudeck, 1992). The remaining fit indices indicated in Table 5 (GFI, AGFI, NFI, CFI) exceed the acceptable fit level of 0.8 (Hair et al., 2009) and PGFI and PNFI exceed the acceptable fit level of 0.5 (Kaynak, 2003). Hence the goodness of fit statistics generally indicate a good fit to the data. The path coefficients or loadings indicate the strengths of the relationships between the endogenous and exogenous variables and the t-value of each path indicates whether the hypothesis is supported or not by the empirical data.


    The lean implementation model, discussed above, would be very useful to the lean practitioners. It helps to integrate all the dimensions of OTFs in one model into a unified coherent complete manufacturing system; LM implementation entails optimization of the manufacturing process to satisfy both customer requirements and organizational goals in a balanced way. On one hand, this model attempts to frame an exhaustive list of all the inputs related to the various OTFs (Table 1) for LM implementation while on the other, the output manifests (Table 2) will effectively guide the lean implementer to evaluate the effect of implementation. The SEM results and their implications are briefly summarised below.

    Sequence of activities and operations needs to be planned to obtain a true linear seamless flow; planning and scheduling actions are essential for continued commitment to improvement. Cycle time reduction has been considered as one of the lean practices (Bevilacqua et al., 2017), that leads to successful lean manufacturing implementation (Shetty et al., 2010). Implementation of reduction of cycle time and lead time as lean practices helps to improve productivity (Nepal et al., 2011). Lean practices and techniques focus on streamlining processes (Shah et al., 2008;Vinodh and Chintha, 2011) to minimise variations and thereby to facilitate cost reduction (Vinodh and Joy, 2012) and improve operational performance (Alsmadi et al., 2012). This current research supports the significance of the integrated nature of planning and scheduling as the hypothesis (H1) that Integrative Planning and Scheduling (IPS) is positively related to successful LM implementation is supported (coef. = 0.185, p < 0.05).

    The Toyota Production System was based around the desire to produce in a continuous flow. Zahraee (2016) and Wickramasinghe and Wickramasinghe (2017) observed that continuous flow is a crucial lean tool for lean manufacturing implementation. Continuous flow is one of the lean practices that encompass a wide variety of shop floor manufacturing initiatives (Shah et al., 2008). The pull system regulates the flows on the factory floor driven by demand from downstream that pulls production upstream. Reduction in setup time (Serrano Lasa et al., 2009;Vinodh and Chintha, 2011) and lot size (Wickramasinghe and Wickramasinghe, 2017) assist in the LM implementation process. In the process, TPM attempts to eliminate any losses in equipment and production efficiency through active team-based participation (Cullinane et al., 2014;Mostafa et al., 2015). Thus there is enough evidence in past literature that a proper harmonization of internal operations is required to maintain the flow, the heartbeat of lean production (Bevilacqua et al., 2015;Cullinane et al., 2014). The hypothesis (H2) in the present study that Internal Operations Synchronization (IOS) is positively related to successful LM implementation is strongly supported (coef. = 0.429, p < 0.01) and thus complements past literature.

    This research further strongly supports the hypothesis (H4) that Product Design and Development (PDD) has a positive influence on successful LM implementation (coef. = 0.357, p < 0.01). This is notably different from past researches in which the role of PDD in LM implementation has not been given much importance. As stated by Sharma et al. (2016), R&D has not been adequately exploited and needs a revamp in terms of its focus and its organization and not much mention regarding R&D in LM has been found during literature review. The only area which has received attention in most researches on lean is Concurrent Engineering (Karim and Arif-Uz- Zaman, 2013;Marodin and Saurin 2013), which can be effectively used for the elimination of non-value adding activities (Pullan et al., 2013) and to integrate customer early in the process (Singh Sangwan et al., 2014). As stated by Saravi et al. (2008), “70 to 80% of the product cost is said to be committed by the end of the conceptual design stage.” Hence, product design needs to be an integral part of any framework for LM implementation. This research clearly indicates that PDD is one of the drivers for successful LM implementation. During the field survey of this research, it was perceived that in Indian manufacturing organizations, more than 70% of the manufacturing cost is committed during product development stage. It may be safely concluded that the effectiveness of LM is rooted in product design and development stage and LM implementation should ideally start from the stage of lean product development (LPD). The final product design and development will depend on the ‘voice of customer’ which needs to be carefully translated into technical requirements (Chan and Wu, 2002) and market research will help to understand these needs of the customer (Thanki and Thakkar, 2014). The above discussion justifies the strong support of the hypothesis that PDD has a positive effect on successful LM implementation.

    According to lean protagonists, Quality tools, techniques and practices are the pillars of the LM implementation. TQM, Kaizen, Statistical Process Control and other Quality tools or practices are considered as enablers of LM by many researchers (Al-Tahat and Jalham, 2015;Susilawati et al., 2015;Vinodh and Chintha, 2011, Wickramasinghe and Wickramasinghe, 2017). Results of this study are also in conjunction with this view of lean experts and the hypothesis (H5) that Quality Governance (QG) positively affects successful LM implementation is strongly supported (coef. = 0.285, p < 0.01).

    Process mapping exercise has been considered as a fundamental technical requirement to be practiced by companies for LM implementation (Bhasin and Burcher, 2006;Bhasin, 2012) and the creation of the Value Stream Map is by itself a value adding process as it helps to have more details and deeper insights of the process (McDonald et al., 2002). Value analysis is one of the key practices associated with lean (Deflorin and Scherrer-Rathje, 2012;Jayaram et al., 2008) and lean thinking focuses on the reduction and removal of wastes by value analysis (Bendell, 2006;Johansson and Osterman, 2017) which has been considered as one of the vital tools and techniques to implement LM system (Upadhye et al., 2010). The findings of this research (coef. = 0.162, p < 0.05) provide empirical support for the argument (hypothesis H6) that successful LM implementation is impacted directly by the latent construct, Strategic Process Control (SPC).

    However, among the input latent constructs, there is no indication that Management Information System (MIS) and Role of Technology (RT) positively affect successful LM implementation in the Indian manufacturing context. These two hypotheses (H3 and H7 respectively) are not supported by the data. During the field survey amongst the Indian manufacturing industries, it was found that lean is perceived as a concept based on the rigor in work practice and focusing more on the work method which is rather domain specific than information technology. Hence, while MIS helps in determining the constraints like the volume and batch size of the delivery schedule or deployment of workforce or equipment (Sharma et al., 2016), lean strategy rather tries to determine more intrinsically, and with a bottom-up approach, how the efficiency can be improved by analyzing and working on the process of core operations such as material removal rate, work standardization, materials handling and so forth. Therefore, data from the survey does not support the hypothesis that MIS has a direct positive effect on successful LM implementation. Chavez et al. (2015), in their study, have concluded that technological turbulence moderates negatively the relationship between internal lean practices and operational/organizational performance. Introduction of new technology often involves an initial financial outlay, which many small and medium enterprises are not willing to undertake in the Indian context. Hence, the hypothesis that technology has a positive role on successful LM implementation could not be established in the current study.

    Past research indicates that implementation of LM contributes substantially to the operating performance of the firm (Achanga et al., 2012;Alsmadi et al., 2012;Bhasin, 2008;Chavez et al., 2015;Gupta and Jain, 2013;Jayaram et al., 2008;Panizzolo et al., 2012;Shah and Ward, 2003, Wickramasinghe and Wickramasinghe, 2017) and to customer satisfaction (Bhasin, 2008;Singh et al., 2009). In this research also, both the hypotheses (H8 and H9) that successful LM implementation positively affect satisfaction of organization goals (coef. = 0.309, p < 0.01) and customer satisfaction (coef. = 0.366, p < 0.01) are strongly supported by the data.

    A novel contribution of the study is that it attempts to make a comprehensive study, keeping all possible dimensions in mind related to the OTFs for LM implementation, develops a conceptual structural model and subsequently validates that model to assess the influence of the OTFs on LM implementation and the effect of LM implementation on customer satisfaction without compromising on the organizational goals. The operational and technological constructs, which have emerged from the analysis, need to be addressed simultaneously by the lean practitioner for effective LM implementation. Lean manufacturing can be implemented easily upon implementation of the dominant factors. Appropriate evaluation of all background and hidden factors at the planning stage is crucial for the successful implementation of lean. This study will also enable and facilitate the managers to analyse the various factors in different stages of planning, which will, in turn, have direct impact on the implementation process. Since there is a significant lack of integrative comprehensive framework for LM implementation in past literature (Pinho and Mendes, 2017), this work is an attempt to integrate the operational and technological enablers in a single study to provide a holistic approach to lean implementation in a manufacturing industry. The model developed in this study will act as a guiding force for developing a logical approach which will enable lean practitioners to prioritise and allocate the resources in an effective way, based on the study of the factors and their interrelationships. The practical significance and implication of the study are that the factors identified will lead to a systemic development to satisfy the demand of the market without forgoing any organizational goal. All the input latent constructs have their respective and specific contributions, and higher the integration is, the better will be the implementation of LM. The latent output constructs, namely, organizational goals satisfaction and customer satisfaction will ultimately help to decide whether the organization has been able to reach its stage of selfactualization in the long run.


    A comprehensive study of the operational and technological input manifest variables, along with the output manifest variables for successful lean implementation, has been focussed in this study. Though a considerable amount of work has been done on LM, there is hardly any research available on such a comprehensive study of both input parameters, as well as the benefits of LM implementation. Over the last few decades, many researchers have put forward many factors responsible for LM implementation but a comprehensive collation of all such possible OTFs has not been reported. This work is an attempt to bridge this gap in the manufacturing sector. Based on literature survey, followed by Delphi exercise, a structural model is suggested for administering lean implementation. The model suggested in this study, based on the list of manifests, can be treated as a guide for successful LM implementation in a manufacturing enterprise, keeping all relevant factors in mind. The model thus conceptualized is then validated with the use of appropriate statistical tools with the help of empirical data from the Indian manufacturers.

    In what way the most effective LM, as a policy, strategy and approach, impacts the performance of an organization is an important finding of this study. It will assist the manufacturing industries to focus on salient factors, re-orient or re-design them and monitor them for improved and desired performance. This work sets the direction for further research where more exploration and investigation of the input and output parameters may be undertaken for advancing knowledge in the domain.

    Certain limitations of this work confine the interpretation of the findings. This study is limited to the manufacturing sector and has scope for extending the concept to the service sector as well. Finally, survey studies with a larger sample size (considering the total number of variables involved) and a larger cross-sectional random sample may provide better understanding of the results found in this study.



    Structural model for OTFs for LM implementation.


    Input manifests and latent operational and technological factors for LM implementation

    Output manifests and latent constructs of successful LM implementation

    Results from CFA

    Correlations table

    Results of structural equation modeling


    1. Achanga, P. , Shehab, E. , Roy, R. , and Nelder, G. (2012), A fuzzy-logic advisory system for lean manufacturing within SMEs, International Journal of Computer Integrated Manufacturing, 25(9), 839-852.
    2. Al-Tahat, M. D. and Jalham, I. S. (2015), A structural equation model and a statistical investigation of lean-based quality and productivity improvement, Journal of Intelligent Manufacturing, 26(3), 571-583.
    3. Alsmadi, M. , Almani, A. , and Jerisat, R. (2012), A comparative analysis of lean practices and performance in the UK manufacturing and service sector firms, Total Quality Management and Business Excellence, 23(3-4), 381-396.
    4. Barclay, D. , Higgins, C. , and Thompson, R. (1995), The partial least squares (PLS) approach to causal modeling:Personal computer adoption and use as an illustration, Technology Studies, 2(2), 285-309.
    5. Bendell, T. (2006), A review and comparison of six sigma and the lean organizations, The TQM Magazine, 18(3), 255-262.
    6. Bevilacqua, M. , Ciarapica, F. E. , and De Sanctis, I. (2017), Lean practices implementation and their relationships with operational responsiveness and company performance: An Italian study, International Journal of Production Research, 55(3), 769-794.
    7. Bevilacqua, M. , Ciarapica, F. E. , and Paciarotti, C. (2015), Implementing lean information management: Thecase study of an automotive company, Production Planning & Control, 26(10), 753-768.
    8. Bhamu, J. and Singh Sangwan, K. (2014), Lean manufacturing: Literature review and research issues, InternationalJournal of Operations and Production Management, 34(7), 876-940.
    9. Bhasin, S. (2008), Lean and performance measurement, Journal of Manufacturing Technology Management, 19(5), 670-684.
    10. Bhasin, S. (2012), Performance of lean in large organizations, Journal of Manufacturing Systems, 31(3), 349-357.
    11. Bhasin, S. and Burcher, P. (2006), Lean viewed as a philosophy, Journal of Manufacturing Technology Management,17(1), 56-72.
    12. Browne, M. W. and Cudeck, R. (1992), Alternative ways of assessing model fit, Sociological Methods andResearch, 21(2), 230-258.
    13. Chan, L. K. and Wu, M. L. (2002), Quality function deployment: A literature review, European Journal ofOperational Research, 143(3), 463-497.
    14. Chavez, R. , Yu, W. , Jacobs, M. , Fynes, B. , Wiengarten, F. , and Lecuna, A. (2015), Internal lean practices and performance: The role of technological turbulence, International Journal of Production Economics, 160, 157-171.
    15. Cronbach, L. J. (1951), Coefficient alpha and the internal structure of tests, Psychometrika, 16(3), 297-334.
    16. Cullinane, S. J. , Bosak, J. , Flood, P. C. , and Demerouti, E.  (2014), Job design under lean manufacturing and thequality of working life: A job demands and resources perspective, The International Journal of HumanResource Management, 25(21), 2996-3015.
    17. Deflorin, P. and Scherrer-Rathje, M. (2012), Challenges in the transformation to lean production from different manufacturing-process choices: A path-dependent perspective, International Journal of Production Research, 50(14), 3956-3973.
    18. Fornell, C. and Larcker, D. F. (1981), Evaluating structural equation models with unobservable variablesand measurement error, Journal of Marketing Research, 18(1), 39-50.
    19. Fullerton, R. R. and Wempe, W. F. (2009), Lean manufacturing, non-financial performance measures, and financial performance, International Journal of Operations and Production Management, 29(3), 214-240.
    20. Fullerton, R. R. , Kennedy, F. A. , and Widener, S. K. (2014), Lean manufacturing and firm performance: The incremental contribution of lean management accounting practices, Journal of Operations Management, 32(7-8), 414-428.
    21. Ghobakhloo, M. and Hong, T. S. (2014), IT investments and business performance improvement: The mediating role of lean manufacturing implementation, International Journal of Production Research, 52(18), 5367-5384.
    22. Gupta, S. and Jain, S. K. (2013), A literature review of lean manufacturing, International Journal of Management Science and Engineering Management, 8(4), 241-249.
    23. Hair, J. , Black, W. C. , Babin, B. J. , and Anderson, R. E. (2009), Multivariate Data Analysis (7th ed.), Pearson Education Ltd., UK.
    24. Holweg, M. (2007), The genealogy of lean production, Journal of Operations Management, 25(2), 420-437.
    25. Jayaram, J. , Vickery, S. , and Droge, C. (2008), Relationship building, lean strategy and firm performance: An exploratory study in the automotive supplier industry, International Journal of Production Research, 46(20), 5633-5649.
    26. Johansson, P. E. and Osterman, C. (2017), Conceptions and operational use of value and waste in lean manufacturing: An interpretivist approach, International Journal of Production Research, 55(23), 6903-6915.
    27. Kajdan, V. (2008), Bumpy road to lean enterprise, Total Quality Management, 19(1-2), 91-99.
    28. Karim, A. and Arif-Uz-Zaman, K. (2013), A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations, Business Process Management Journal, 19(1), 169-196.
    29. Kaynak, H. (2003), The relationship between total quality management practices and their effects on firm performance, Journal of Operations Management, 21(4), 405-435.
    30. Kline, R. B. (2015), Principles and Practice of Structural Equation Modeling (4th ed.), Guildford Press, New York, NY.
    31. Krafcik, J. F. (1988), Triumph of the lean production system, MIT Sloan Management Review, 30(1), 41-52.
    32. LMCS (Lean Manufacturing Competitiveness Scheme) (2013), cited 2015 April 14, Available from:
    33. Marodin, G. A. and Saurin, T. A. (2013), Implementing lean production systems: Research areas and opportunities for future studies, International Journal of Production Research, 51(22), 6663-6680.
    34. McDonald, T. , Van Aken, E. M. , and Rentes, A. F. (2002), Utilising simulation to enhance value stream mapping: A manufacturing case application, International Journal of Logistics, 5(2), 213-232.
    35. Moori, R. G. , Pescarmona, A. , and Kimura, H. (2013), Lean manufacturing and business performance in brazilian firms, Journal of Operations and Supply Chain Management, 6(1), 91-105.
    36. Mostafa, S. , Lee, S. H. , Dumrak, J. , Chileshe, N. , and Soltan, H. (2015), Lean thinking for a maintenance process, Production and Manufacturing Research, 3(1), 236-272.
    37. NMP (National Manufacturing Policy) (2011), cited 2015 February 10, Available from:
    38. Nepal, B. P. , Yadav, O. P. , and Solanki, R. (2011), Improving the NPD process by applying lean principles: A case study, Engineering Management Journal, 23(3), 65-81.
    39. Nunnally, J. (1978), Psychometric Theory (2nd ed.), McGraw-Hill, New York, NY.
    40. Panizzolo, R. , Garengo, P. , Sharma, M. K. , and Gore, A. (2012), Lean manufacturing in developing countries: Evidence from Indian SMEs, Production Planning and Control, 23(10-11), 769-788.
    41. Pinho, C. and Mendes, L. (2017), IT in lean-based manufacturing industries: Systematic literature review and research issues, International Journal of Production Research, 55(24), 7524-7540.
    42. Podsakoff, P. M. , MacKenzie, S. B. , Lee, J. Y. , and Podsakoff, N. P. (2003), Common method biases in behavioral research: A critical review of the literature and recommended remedies, Journal of Applied Psychology, 88(5), 879-903.
    43. Pullan, T. T. , Bhasi, M. , and Madhu, G. (2013), Decision support tool for lean product and process development, Production Planning and Control, 24(6), 449-464.
    44. Saravi, M. , Newnes, L. , Mileham, A. R. , and Goh, Y. M. (2008), Estimating cost at the conceptual design stage to optimize design in terms of performance and cost, Collaborative Product and Service Life Cycle Management for a Sustainable World, 123-130.
    45. Schumacker, R. E. and Lomax, R. G. (1996), A Beginner’s Guide to Structural Equation Effects in Structural Equation Modeling, Erlbaum, Mahwah, NJ.
    46. Serrano Lasa, I. , Castro, R. D. , and Laburu, C. O. (2009), Extent of the use of Lean concepts proposed for a value stream mapping application, Production Planning and Control, 20(1), 82-98.
    47. Shah, R. and Ward, P. T. (2003), Lean manufacturing: Context, practice bundles, and performance, Journal of Operations Management, 21(2), 129-149.
    48. Shah, R. , Chandrasekaran, A. , and Linderman, K. (2008), In pursuit of implementation patterns: The context of lean and six sigma, International Journal of Production Research, 46(23), 6679-6699.
    49. Sharma, V. , Dixit, A. R. , and Qadri, M. A. (2016), Modeling lean implementation for manufacturing sector, Journal of Modelling in Management, 11(2), 405-426.
    50. Shetty, D. , Ali, A. , and Cummings, R. (2010), Surveybased spreadsheet model on lean implementation, International Journal of Lean Six Sigma, 1(4), 310-334.
    51. Singh Sangwan, K. , Bhamu, J. , and Mehta, D. (2014), Development of lean manufacturing implementation drivers for Indian ceramic industry, International Journal of Productivity and Performance Management, 63(5), 569-587.
    52. Singh, B. , Garg, S. K. , and Sharma, S. K. (2009), Lean can be a survival strategy during recessionary times, International Journal of Productivity and Performance Management, 58(8), 803-808.
    53. Stone, K. B. (2012), Four decades of lean: A systematic literature review, International Journal of Lean Six Sigma, 3(2), 112-132.
    54. Susilawati, A. , Tan, J. , Bell, D. , and Sarwar, M. (2015), Fuzzy logic based method to measure degree of lean activity in manufacturing industry, Journal of ManufacturingSystems, 34, 1-11.
    55. Thanki, S. J. and Thakkar, J. (2014), Status of lean manufacturing practices in Indian industries and government initiatives: A pilot study, Journal of Manufacturing Technology Management, 25(5), 655-675.
    56. Upadhye, N. , Deshmukh, S. G. , and Garg, S. (2010), Lean manufacturing system for medium size manufacturing enterprises: An Indian case, International Journal of Management Science and Engineering Management, 5(5), 362-375.
    57. Vinodh, S. and Chintha, S. K. (2011), Leanness assessment using multi-grade fuzzy approach, International Journal of Production Research, 49(2), 431-445.
    58. Vinodh, S. and Joy, D. (2012), Structural equation modelling of lean manufacturing practices, International Journal of Production Research, 50(6), 1598-1607.
    59. Wickramasinghe, G. L. D. and Wickramasinghe, V. (2017), Implementation of lean production practices and manufacturing performance: The role of lean duration, Journal of Manufacturing Technology Management,28(4), 531-550.
    60. Womack, J. P. , Jones, D. T. , Roos, D. , and Carpenter, D. S. (1990), The Machine that Changed the World, Rawson Associates, Macmillan Publishing Company, New York, NY.
    61. Zahraee, S. M. (2016), A survey on lean manufacturing implementation in a selected manufacturing industry in Iran, International Journal of Lean Six Sigma,7(2), 136-148.
    Do not open for a day Close