| Summary: | Maintenance and risk are important in managing production assets. The
appropriate maintenance system is nessasary applied to increase productivity in
production process. Maintenance strategy needs to be considered carefully in line
with factors that affect the condition of the equipment. Unfortunately, there are
many industries still make mistakes in determining the maintenance strategy. This
is presumably because the people who work in maintenance has not been applied
the optimal maintenance strategy because they think it is complex to be studied
and applied in each machines as well as the unavailability a tool for simplifying
the maintenance strategy.
In this study, researchers want to facilitate the user in decision making of
maintenance strategy. Researchers create decision making tool using the Analytic
Hierarchy Process (AHP). By organizing and assessing alternatives to the
multifaceted hierarchy, AHP provides an effective verification to handle decision
complexity. The software is designed to accommodate the user needs in making
for maintenance strategy on critical and non critical machine. The information
will be used by user to optimalize the maintenance actions. The research was
carried out with a case study in PT. Coca-Cola Amatil Semarang. Software
making is done in Department of Mechanical and Industrial Engineering, Faculty
of Engineering, Gadjah Mada University and PT. Coca-Cola Amatil Semarang.
Data retrieval and process of software testing is done in PT. Coca-Cola Amatil
Semarang.
The results is software named DEFORMEST (Decision for Maintenance
Expert Software). Preliminary testing results indicate that the selected
maintenance strategy to address the critical machine is preventive maintenance
based on global weight 0.5596. Maintenance strategy to address the non critical
machine is preventive maintenance based on global weight 0.3303. Proven
software can be applied well in PT. Coca-Cola Amatil Semarang. This can be
informed by increasing the efficiency of machines in Line 5. Net Line Efficiency
increased by 3.65 % and Gross Line Efficiency increased by 3.13 %. In addition,
the Operation Performance Loss decreased by 4.56.
|
|---|