Authors: Tauno Kekale, Kongkiti Phusavat, Bordin Rassameethes, Prachuab Klomjit
Addresses: Department of Production, University of Vaasa, FIN-65101 Vaasa, Finland. ' Department of Industrial Engineering, Department of Operations Management, Kasetsart University, Bangkok, Thailand. ' Department of Industrial Engineering, Department of Operations Management, Kasetsart University, Bangkok, Thailand. ' Department of Industrial Engineering, Department of Operations Management, Kasetsart University, Bangkok, Thailand
Abstract: The purposes of this research are to extend the existing guidelines for manual lifting tasks and to evaluate the impacts from the web-based and interactive features on workers| preference. This web-based tool, the ML-Expert System was designed to address the safety and health of workers. A comparison was made between the two groups, in accordance with the National Institute for Occupational Safety and Health (NIOSH) and the ML-Expert system, over a period of five months. The overall result indicates the need to extend the existing basic guidelines for manual-lifting work by adapting to a unique working environment. Finally, the web-based and interactive features of the ML-Expert appeared to help increase satisfaction and workplace learning.
Keywords: manual handling; lifting tasks; workplace improvements; expert systems; prototypes; workers; workforce; National Institute for Occupational Safety and Health; NIOSH; ML-Expert; machine learning; working environments; interactive training; job satisfaction; workplace learning; Thailand; factories; online learning; e-learning; electronic learning; internet; world wide web; knowledge acquisition; web-based learning; innovation.
International Journal of Innovation and Learning, 2010 Vol.8 No.1, pp.78 - 89
Published online: 06 Jul 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article