Evaluation of Lean Manufacturing Systems Using MADM and Fuzzy TOPSIS

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Lean manufacturing is a management philosophy derived from Toyota Production System (TPS) which aims to increase the overall values of the product or service provided to the customer through elimination of non-value added activities. In the era of globalisation, to remain competitive in the global market, many medium and small sized Indian industries adopt lean manufacturing. This paper focuses on implementation of lean manufacturing in Indian MSMEs. To examine the implementation, attributes which influence lean manufacturing are obtained and industries’ performances on these criteria are rated. In this paper, the methodology selected from many of the multi criteria models is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the actual real world situation, because of the unreliable nature of the information gathered, the attributes are often not absolute and are imprecise. These data can be considered as fuzzy and the aim of this paper is to adopt TOPSIS decision making method to problems with fuzzy data. The rating and weights of each data are expressed as triangular fuzzy numbers. These attributes are then normalized and the TOPSIS methodology is carried out to determine the effect of implementing lean manufacturing technique in an industry. The best industry is identified by fuzzy TOPSIS on the basis of performance towards the considered attributes is consistent with results identified by TOPSIS.

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2628-2638

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July 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Abdulmalek FA and Rajgopal J (2007), Analyzing the Benefits of Lean Manufactur-ing and Value Stream Mapping Via Simulation: A Process Sector Case Study. International Journal of Production Economics, Vol. 107, No. 1, pp.223-236.

DOI: 10.1016/j.ijpe.2006.09.009

Google Scholar

[2] Achanga P, Shehab E, Roy R, and Nelder G (2006), Critical success factors for lean implementation within SMEs, Journal of Manufacturing Technology Management, Vol. 17, No. 4, pp.460-471.

DOI: 10.1108/17410380610662889

Google Scholar

[3] Bhim Singh, S.K. Garg and S.K. Sharma (2010, ), Scope for lean implementation: a survey of 127 Indian industries, International Journal of Rapid Manufacturing, Vol. 1, No. 3, pp.323-333.

DOI: 10.1504/ijrapidm.2010.034253

Google Scholar

[4] Chunqiao Tan(2011), A multi-criteria interval-valued intuitionistic fuzzy group decision making with Choquet integral-based TOPSIS, Expert Systems with Applications, Vol. 38, No. 4, p.3023–3033.

DOI: 10.1016/j.eswa.2010.08.092

Google Scholar

[5] Crute, V., Ward, Y., Brown, S. and Graves, A. (2003), Implementing Lean in aerospace - challenging the assumptions and understanding the challenges, Technovation, Vol. 23, No. 12, pp.917-928.

DOI: 10.1016/s0166-4972(03)00081-6

Google Scholar

[6] Czabke, J., Hansen, E.N. & Doolen, T.L. (2008), A multisite field study of lean thinking in US and German secondary wood products manufacturers, Forest Products Journal, Vol. 58, No. 9, pp.77-85.

Google Scholar

[7] Fu Yuan-guang(2009), The TOPSIS Method of Multiple Attribute Decision Making Problem with Triangular-fuzzy-valued Weight, International Workshop on Modelling, Simulation and Optimization.

DOI: 10.1109/wmso.2008.95

Google Scholar

[8] G.R. Jahanshahloo, F. Hosseinzadeh Lotfi and M. Izadikhah (2006), Extension of the TOPSIS method for decision-making problems with fuzzy data, Applied Mathematics and Computation. Vol. 181, No. 2, p.1544–1551.

DOI: 10.1016/j.amc.2006.02.057

Google Scholar

[9] Gurumurthy A. and Kodali R (2008), A multi criteria decision making model for justification of lean manufacturing systems, International Journal of Management Science and Engineering Management, Vol. 3, No. 2, pp.54-62.

DOI: 10.1080/17509653.2008.10671039

Google Scholar

[10] Jia-Wen Wang , Ching-Hsue Cheng and Huang Kun-Cheng (2009), Fuzzy hierarchical TOPSIS for supplier selection, Applied Soft Computing, Vol. 9, No. 1, p.377–386.

DOI: 10.1016/j.asoc.2008.04.014

Google Scholar

[11] Ma Ga(Mark)Yang, Paul Hong n and Sachin B. Modi (2011).

Google Scholar

[12] Mabry and Morrison (1996), Transformation to lean manufacturing by an automotive component supplier. Computers ind. Engng, Vol. 31, No. 2, pp.95-98.

DOI: 10.1016/0360-8352(96)00087-3

Google Scholar

[13] Ningombam Devarani Devi, Sorokhaibam Khaba and Pranab Kumar Dan(2013), A Study on Application of lean Manufacturing Methodologies in Indian Electronics Manufacturing Industry, Research Journal of Engineering Sciences, Vol. 2, No. 5, pp.11-14.

Google Scholar

[14] Priti B. Khadse, Avinash D. Sarode and Renu Wasu (2013), Lean Manufacturing in Indian Industries A Review, International Journal of Latest Trends in Engineering and Technology, Vol. 3, No. 1, pp.175-181.

Google Scholar

[15] Rachna Shah and Peter T. Ward(2003), Lean manufacturing: context, practice bundles, and performance, Journal of Operations Management, Vol. 21, No. 2, p.129–149.

DOI: 10.1016/s0272-6963(02)00108-0

Google Scholar

[16] Renato A. Krohling and Vinicius C. Campanharo(2011), Fuzzy TOPSIS for group decision making: A case study for accidents with oil spillin the sea, Expert Systems with Applications, Vol. 38, No. 4, p.4190–4197.

DOI: 10.1016/j.eswa.2010.09.081

Google Scholar

[17] Saeed Rouhani, Mehdi Ghazanfari and Mostafa Jafari (2012), Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS, Expert Systems with Applications, Vol. 39, No. 3, p.3764–3771.

DOI: 10.1016/j.eswa.2011.09.074

Google Scholar

[18] T. Melton (2005), The benefits of lean manufacturing: What Lean Thinking has to Offer the Process Industries. Trans IChemE, Part A, Chemical Engineering Research and Design, Vol. 83, No. A6, p.662–673.

DOI: 10.1205/cherd.04351

Google Scholar