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Analyzing the drivers for lean practices of commercial banking using interpretive structural modelling

Publication Type : Conference Paper

Publisher : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Institute of Electrical and Electronics Engineers Inc..

Source : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Institute of Electrical and Electronics Engineers Inc. (2016)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019973593&doi=10.1109%2fICCIC.2016.7919632&partnerID=40&md5=45efa406b8dcfdb10a634d760ac92097

ISBN : 9781509006113

Keywords : Artificial intelligence, banking, Conservation, Continuous improvements, Continuous process improvement, Efficiency improvement, lean, Lean practices, Process management, Structural modelling, Waste management

Campus : Coimbatore

School : School of Business

Department : Electrical and Electronics

Year : 2016

Abstract : Banking is one of the continuously growing service sectors in India. Banking efficiency improvement through optimization of banking processes is crucial. The concepts of lean management principles are a set of practices followed to achieve continuous process improvement through waste minimisation. This paper identifies and analyses the set of drivers which are instrumental in bringing about the lean practices in a bank. This paper makes use of the Interpretive Structural Modelling approach to identify the relationship between the drivers and their degree of influence on the banking processes. This model inferred that the general management leanness is ranked in the first level and hence should be given the highest importance followed by process management leanness and continuous improvement leanness in the successive rank levels.

Cite this Research Publication : R. Abinaya and Dr. Suresh M., “Analyzing the drivers for lean practices of commercial banking using interpretive structural modelling”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, 2016.

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