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Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System
With the current global downturn, the organizations need to develop new strategies and innovative approaches to ensure that every aspect of sustainability is achieved. For this purpose, the organizations need an indicator that measures the fitness if an organization. The purpose of this project is t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134161/ https://www.ncbi.nlm.nih.gov/pubmed/30211288 http://dx.doi.org/10.1016/j.dib.2018.07.049 |
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author | Kulkarni, Chaitanya Kulkarni, Soham Kandasamy, Jayakrishna |
author_facet | Kulkarni, Chaitanya Kulkarni, Soham Kandasamy, Jayakrishna |
author_sort | Kulkarni, Chaitanya |
collection | PubMed |
description | With the current global downturn, the organizations need to develop new strategies and innovative approaches to ensure that every aspect of sustainability is achieved. For this purpose, the organizations need an indicator that measures the fitness if an organization. The purpose of this project is to analyze the ‘Fitness’ of an organization using the dataset related to leanness, agility and sustainability in ANFIS (Adaptive Neuro-Fuzzy Inference System) in order to determine whether the company is fit enough to sustain in global markets or not. The project does so by integrating both neural networks and fuzzy logic principles with lean, agility and sustainability principles. FIT manufacturing is the integration of Lean, Agile and sustainability manufacturing in one system as a whole which would help in attaining maximum output and sustain effectively in global markets. FIT Manufacturing adopts an integrated approach towards the use of Lean, Agility and Sustainability to achieve a level of fitness that is unique to each company. The database in the paper contains lean, agile and sustainable indices reviewed by experts. FIT does not prescribe that every aspect of Lean, Agile and Sustainability methodologies must be applied to every company, but a selective mix of components will provide the optimum conditions for a company to prosper. |
format | Online Article Text |
id | pubmed-6134161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-61341612018-09-12 Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System Kulkarni, Chaitanya Kulkarni, Soham Kandasamy, Jayakrishna Data Brief Earth and Planetary Science With the current global downturn, the organizations need to develop new strategies and innovative approaches to ensure that every aspect of sustainability is achieved. For this purpose, the organizations need an indicator that measures the fitness if an organization. The purpose of this project is to analyze the ‘Fitness’ of an organization using the dataset related to leanness, agility and sustainability in ANFIS (Adaptive Neuro-Fuzzy Inference System) in order to determine whether the company is fit enough to sustain in global markets or not. The project does so by integrating both neural networks and fuzzy logic principles with lean, agility and sustainability principles. FIT manufacturing is the integration of Lean, Agile and sustainability manufacturing in one system as a whole which would help in attaining maximum output and sustain effectively in global markets. FIT Manufacturing adopts an integrated approach towards the use of Lean, Agility and Sustainability to achieve a level of fitness that is unique to each company. The database in the paper contains lean, agile and sustainable indices reviewed by experts. FIT does not prescribe that every aspect of Lean, Agile and Sustainability methodologies must be applied to every company, but a selective mix of components will provide the optimum conditions for a company to prosper. Elsevier 2018-08-04 /pmc/articles/PMC6134161/ /pubmed/30211288 http://dx.doi.org/10.1016/j.dib.2018.07.049 Text en © 2018 Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Earth and Planetary Science Kulkarni, Chaitanya Kulkarni, Soham Kandasamy, Jayakrishna Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System |
title | Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System |
title_full | Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System |
title_fullStr | Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System |
title_full_unstemmed | Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System |
title_short | Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System |
title_sort | dataset for evaluating fitness index using adaptive neuro-fuzzy inference system |
topic | Earth and Planetary Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134161/ https://www.ncbi.nlm.nih.gov/pubmed/30211288 http://dx.doi.org/10.1016/j.dib.2018.07.049 |
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