Seminar about "Failure-based Maintenance Decision Support Model in Small and Medium Industries using Decision Making Grid Model" at Faculty of Computing in Rabigh
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Dr. Burhanuddin
Aboobaider presented a seminar about " Failure-based Maintenance
Decision Support Model in Small and Medium Industries using Decision
Making Grid Model " at Faculty of Computing and Information Technology
in Rabigh on Monday 06/2/2012 at 12:00 noon. Faculty members attended
the seminar. The following is the brief of the seminar:
Overtime, industries
have to control production cost in order to maintain good profit
margin. When equipment not well maintained, their performance and
productivity decreased. Maintenance department as an imperative unit in
industries should attain all maintenance data, process information
instantaneously, and subsequently transform it into a useful decision.
Then act on the strategies to increase machineries productivity.
Previous
research shows
Decision Making Grid (DMG) and Fuzzy Inference System (FIS) are able to
identify strategies for maintenance decision. In our research, we use
Labib (2008) approaches to extract top ten problematic machines using
structured query language and analyze them with DMG model in the
production floor. In this seminar, we will demonstrate on how to reveal
risk factors i.e. frequency of failures and downtime of the machines.
Next we recommended maintenance strategies i.e. operate to failure,
service level upgrade,
condition-based
maintenance, design-out maintenance, total productive maintenance and
reliability centered maintenance for one of the food processing
factories in Malaysia. We extracted data from food processing factory's
database. We analyze the data using DMG and FIS, then provide
maintenance strategies to the maintenance managers. From the
experimental study, we have validated that breakdowns reduced
tremendously with the given strategies using MDG model. We proposed DMG
to be embedded in Computerized Maintenance Management System for future
research work in order to generate promising result in the manufacturing
plant.
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