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Optimization temperature level toward workers’ productivity

National Symposium on Advancements in Ergonomics and Safety (ERGOSYM2009), 1st – 2nd December 2009, Perlis, Malaysia

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Bibliographic Details
Main Authors: Ahmad Rasdan, Ismail, M. Yusri, M. Yusof, Baba, Md Deros
Other Authors: arasdan@gmail.com
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2014
Subjects:
Artificial Neural Network (ANN)
Optimum
Productivity
Temperature
Environmental
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/37438
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spelling my.unimap-374382014-10-16T04:36:57Z Optimization temperature level toward workers’ productivity Ahmad Rasdan, Ismail M. Yusri, M. Yusof Baba, Md Deros arasdan@gmail.com myusri@rocketmail.com Artificial Neural Network (ANN) Optimum Productivity Temperature Environmental National Symposium on Advancements in Ergonomics and Safety (ERGOSYM2009), 1st – 2nd December 2009, Perlis, Malaysia Production of automotive parts is among the largest contributor to economic earnings in Malaysia. The dominant work involve in producing automotive part were manual assembly process. Where it is definitely used a manpower capability. Thus the quality of the product heavily depends on worker’s comfort in the working condition. Temperature is one of the environmental factors that contribute significant effect on the worker performance. This paper intended to present an optimization of temperature level towards the worker productivity rate at one of the Malaysian industry. An assembly automotive manufacturing industry was chosen to conduct the study by observing and measuring the temperature level and worker’s productivity rate. The data then were analyzed by using Artificial Neural Network's analysis (ANN). ANN analysis technique is commonly used to analysis and obtained the best linear relationship from the collected data. It is apparent that from the linear relationship obtained, the optimum value of production (value 1) is attained when temperature value (WBGT) is 24.5 °C. This finding was also inline when compared to the temperature range of comfort level produced from OSHA standard. The optimum value production rate (value 1) for one manual production line in that particular company is successfully achieved. Through ANN analysis, the optimum environmental factor managed to be predicted. 2014-10-16T04:36:57Z 2014-10-16T04:36:57Z 2009-12-01 Article p.207-212 http://dspace.unimap.edu.my:80/xmlui/handle/123456789/37438 en Proceeding of the National Symposium on Advancements in Ergonomics and Safety (ERGOSYM2009); Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Artificial Neural Network (ANN)
Optimum
Productivity
Temperature
Environmental
spellingShingle Artificial Neural Network (ANN)
Optimum
Productivity
Temperature
Environmental
Ahmad Rasdan, Ismail
M. Yusri, M. Yusof
Baba, Md Deros
Optimization temperature level toward workers’ productivity
description National Symposium on Advancements in Ergonomics and Safety (ERGOSYM2009), 1st – 2nd December 2009, Perlis, Malaysia
author2 arasdan@gmail.com
author_facet arasdan@gmail.com
Ahmad Rasdan, Ismail
M. Yusri, M. Yusof
Baba, Md Deros
format Article
author Ahmad Rasdan, Ismail
M. Yusri, M. Yusof
Baba, Md Deros
author_sort Ahmad Rasdan, Ismail
title Optimization temperature level toward workers’ productivity
title_short Optimization temperature level toward workers’ productivity
title_full Optimization temperature level toward workers’ productivity
title_fullStr Optimization temperature level toward workers’ productivity
title_full_unstemmed Optimization temperature level toward workers’ productivity
title_sort optimization temperature level toward workers’ productivity
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2014
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/37438
_version_ 1643798461703782400
score 13.214268

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