Indian Journal of Science and Technology
DOI: 10.17485/ijst/2017/v10i11/106133
Year: 2017, Volume: 10, Issue: 11, Pages: 1-6
Original Article
S. Y. Jani1*, T. N. Desai2 and P. H. Darji1
1C. U. Shah University, Surendranagar - Ahmedabad Highway, Nr. Kothariya Village, Dist. Surendranagar, Wadhwan – 363030, Gujarat, India; [email protected] 2Sardar Vallabhbhai National Institute of Technology, Ichchhanath Circle, Athwa, Surat – 395007, Gujarat, India;
*Author for correspondence
S. Y. Jani
C. U. Shah University, Surendranagar - Ahmedabad Highway, Nr. Kothariya Village, Dist. Surendranagar, Wadhwan – 363030, Gujarat, India; [email protected]
Lean Manufacturing practices are basically waste reduction perspective, in which value adding activities are focused over non value adding activities, however there are other advantages like process improvement, muda elimination, muri elimination but its main principal is work improvement through process flow and pull kanban.which finally results in increased productivity and key performance factor improvement like quality, cost, financial impact. Performance measures can only be controlled, if the critical factors are identified and explored among various process parameters. The objective of the paper is to compare the ANN base SPSS model with conceptual model and its linear regression model for lean manufacturing for SMEs. The comparison will gives the closeness of the data obtained for Model development and theory development .The residual indices were measured and equated to predict the performance measures. The operational level factors were explored for different performance measures of lean practices and their synaptic weightage for ANN was determined by activating hidden layer method in SPSS 20.
Keywords: Artificial Intelligence, Neural Networks, Productivity, SMEs (Small and Medium scale Manufacturing Enterprises), Waste
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