Development of lean manufacturing implementation drivers for Indian ceramic industry

Purpose - – The purpose of this paper is to develop a statistically reliable and valid model of lean manufacturing (LM) implementation drivers for the Indian ceramic industry through an empirical study. Design/methodology/approach - – The research methodology is based on the empirical study of the Indian ceramic industry through a questionnaire specifically developed for the study through literature review and discussions held with practitioners. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling techniques have been used to propose and validate the model. SPSS and AMOS statistical tools have been used for the statistical analysis of the data. Findings - – The study identified 12 drivers for the LM implementation in Indian ceramic industry. Further, these 12 drivers have been categorized into internal, policy and external drivers (ED). Structural model affirms that ED are positively related to policy drivers (PD) and PD are positively related to internal drivers. Research limitations/implications - – This study provides casual relationships among the various drivers, which can be leveraged by the managers for the easy and effective implementation of LM in their organizations. It is expected that the model will help the decision makers during LM implementation in taking informed decisions in prioritizing and sequencing the implementation strategy. The results of the research may apply to other industries as well, but this needs to be validated by collecting data and analysing its results. Practical implications - – The results provide insights into motivating factors that should be focused on while taking lean decisions. The correlation results among drivers will enable the policy makers in government and industry to strategically leverage the resources for the successful implementation of LM in the industry. Originality/value - – This research empirically develops a model of drivers for LM implementation. The novelty of the study is the causal relationship among the drivers which can be used for decision making to implement lean easily and effectively. Moreover, the categorization of the drivers into internal, external and policy categories and driving/driven relationship among these categories provides the top management an incisive insight into broad improvement areas.

[1]  Jan Olhager,et al.  Lean and agile manufacturing: external and internal drivers and performance outcomes , 2009 .

[2]  X. Koufteros.  Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling , 1999 .

[3]  Shad Dowlatshahi,et al.  The development of a conceptual framework for Just-In-Time implementation in SMEs , 2009 .

[4]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[5]  Pius Achanga,et al.  Critical success factors for lean implementation within SMEs , 2006 .

[6]  J. Pearl.  Graphs, Causality, and Structural Equation Models , 1998 .

[7]  Suresh Garg,et al.  Scope for lean implementation: a survey of 127 Indian industries , 2010 .

[8]  Roger G. Schroeder,et al.  A FRAMEWORK FOR QUALITY MANAGEMENT RESEARCH AND AN ASSOCIATED MEASUREMENT INSTRUMENT , 1994 .

[9]  Baba Md Deros,et al.  A survey on lean manufacturing implementation in Malaysian automotive industry , 2010 .

[10]  H. Marsh,et al.  Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. , 1985 .

[11]  P. Garengo,et al.  Lean manufacturing in developing countries: evidence from Indian SMEs , 2012 .

[12]  H. Simon,et al.  Causal Ordering and Identifiability , 1977 .

[13]  G. Koeske,et al.  Some Recommendations for Improving Measurement Validation in Social Work Research , 1994 .

[14]  Rachna Shah,et al.  Use of structural equation modeling in operations management research: Looking back and forward ☆ , 2006 .

[15]  Kuldip Singh Sangwan,et al.  Productivity and quality improvement through value stream mapping: a case study of Indian automotive industry , 2012 .

[16]  Tong Li.  Synthetic evaluation on listed companies in ceramic industry based on a combined weight method , 2011, MSIE 2011.

[17]  Barbara M. Byrne,et al.  Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[18]  Manimay Ghosh,et al.  Lean manufacturing performance in Indian manufacturing plants , 2012 .

[19]  B. Haig.  Exploratory Factor Analysis, Theory Generation, and Scientific Method , 2005, Multivariate behavioral research.

[20]  A. Sohal,et al.  Lean Production: Experience among Australian Organizations , 1994 .

[21]  Angappa Gunasekaran,et al.  Implementation of just-in-time in a small company: A case study , 1997 .

[22]  Sanjay Bhasin,et al.  Prominent obstacles to lean , 2012 .

[23]  Khaled A. Muttar.  An investigation of the validity of objective and subjective measures of organizational climate , 1985 .

[24]  Bin Zhou,et al.  Lean principles, practices, and impacts: a study on small and medium-sized enterprises (SMEs) , 2016, Ann. Oper. Res..

[25]  Peter M. Bentler,et al.  EQS : structural equations program manual , 1989 .

[26]  Barbara M. Byrne,et al.  Structural equation modeling with AMOS , 2010 .