Evaluation and benchmarking of lean manufacturing system environment: A graph theoretic approach

Article history: Received April 20, 2015 Received in revised format October 10, 2015 Accepted October 15 2015 Available online October 16 2015 Manufacturing industries are consistently working on improving their operational performance to remain competitive in the market. LM is a well-recognized approach for improving the overall performance. It contains several elements covered under a few lean attributes. This paper presents the application of Graph Theory and Matrix Approach (GTMA) for the identification of relative importance of different lean attributes in a lean environment using qualitative and quantitative factors. The Lean Manufacturing Attributes (LMA’s), affecting the overall LM environment, of a manufacturing industry were identified and analyzed for the implications on the managerial decisions. .In this proposed study, The GTMA approach is applied to prioritize the LMA’s based on their relative importance. Growing Science Ltd. All rights reserved. 6 © 201

[1]  S. G. Deshmukh,et al.  Lean manufacturing system for medium size manufacturing enterprises: an Indian case , 2010 .

[2]  Navi Mumbai,et al.  Lean Manufacturing in Indian Industries A Review , 2013 .

[3]  S. Spear,et al.  Decoding the DNA of the Toyota Production System , 1999 .

[4]  Bengt Klefsjö,et al.  The machine that changed the world , 2008 .

[5]  Damian George,et al.  Improvement of manufacturing operations at a pharmaceutical company , 2011 .

[6]  S.K.P.N. Silva.  Applicability of Value Stream Mapping (VSM) in the Apparel industry in Sri Lanka , 2011 .

[7]  Vikas Kumar,et al.  Lean manufacturing in Indian context: A survey , 2015 .

[8]  Christian Engel,et al.  Knowledge Management in Lean Production Systems , 2012 .

[9]  Dixit Garg,et al.  Evaluating Distribution Process of a Supply Chain in Just-in- Time Environment Using Application of Graph Theory , 2014 .

[10]  Rakesh Kumar,et al.  Barriers in Implementation of Lean Manufacturing System in Indian industry: A , 2014 .

[11]  Inderpreet Singh Ahuja,et al.  An evaluation of TPM initiatives in Indian industry for enhanced manufacturing performance , 2008 .

[12]  Peter T. Ward,et al.  Lean manufacturing: context, practice bundles, and performance , 2003 .

[13]  Purna Chandra Biswal.  Discrete Mathematics and Graph Theory , 2013 .

[14]  Sandeep Grover,et al.  Role of human factors in TQM: a graph theoretic approach , 2006 .

[15]  Trevor A Spedding,et al.  The evolution of lean Six Sigma , 2010 .

[16]  Steven Skiena,et al.  Computational Discrete Mathematics: Combinatorics and Graph Theory with Mathematica ® , 2009 .

[17]  Bhanu S. Ragu-Nathan,et al.  Development and validation of a measurement instrument for studying supply chain management practices , 2005 .

[18]  J. Gross,et al.  Graph Theory and Its Applications , 1998 .

[19]  O. P. Gandhi,et al.  Failure cause analysis of machine tools using digraph and matrix methods , 2002 .

[20]  Rachna Shah,et al.  Defining and developing measures of lean production , 2007 .

[21]  G. Anand,et al.  Selection of lean manufacturing systems using the PROMETHEE , 2008 .

[22]  Dixit Garg,et al.  JIT supply chain; an investigation through general system theory , 2013 .

[23]  Vikas Kumar.  JIT Based Quality Management: Concepts and Implications in Indian Context , 2010 .

[24]  S. K. Sharma,et al.  Lean can be a survival strategy during recessionary times , 2009 .

[25]  Shahram Taj,et al.  The impact of lean operations on the Chinese manufacturing performance , 2011 .

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