Decision Support Approach to Computerize Maintenance Management System: Development and Implementation in Food Processing Industry
Downtimes of productions unit may result capacity loss, poor product quality, customer dissatisfaction and environmental impact. In fact, these downtimes can be forecasted and managed more effectively if an organization takes preemptive measures using artificial intelligence techniques such as data...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Malaysian Technical Universities
2008
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11455/1/MUCET08.pdf http://eprints.utem.edu.my/id/eprint/11455/ |
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| Summary: | Downtimes of productions unit may result capacity loss, poor product quality, customer dissatisfaction and environmental impact. In fact, these downtimes can be forecasted and managed more effectively if an organization takes preemptive measures using artificial intelligence techniques such as data mining, neural networks, genetic algorithm etc. This will provide good estimate to predict next failures. The proposed research project reveals the risk factors that either delay or accelerate downtimes. It also demonstrates the extent of such delay, attributable to specific risk factors. Once risk factors are detected, the maintenance managers are aware of the starting and finishing points for each maintenance job due to their prior knowledge about the potential barriers or covariates. We develop a prototype of the Computerized Maintenance Management System (CMMS) as a tool for maintenance management team, which consist of work order, preventive maintenance, corrective maintenance and inventory control system Burhanuddin et. al.(2008). In this study, decision support button created in the CMMS. We are in the progress of developing more decision support algorithms and modules in our future work. |
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