A Hybrid Simulation Study to Determine an Optimal Maintenance Strategy

Ig. Jaka Mulyana (Universitas Katolik Widya Mandala Surabaya - Indonesia)
Ivan Gunawan    (Universitas Katolik Widya Mandala Surabaya - Indonesia) Orcid ID
Yunia Vera Angelia (Universitas Katolik Widya Mandala Surabaya - Indonesia)
Dian Trihastuti (Universitas Katolik Widya Mandala Surabaya - Indonesia)

 ) Corresponding Author
Copyright (c) 2020 Ig. Jaka Mulyana, Ivan Gunawan, Yunia Vera Angelia
With the increasing complexity of the process industry, having excellent maintenance management is essential for manufacturing industries. Various parts that interact and interdependent with each other make a well-planned maintenance strategy is one of the major challenges facing by industry. The whole system could be interrupted just simply because of the failure of a component.  Therefore, a review of a maintenance strategy must be done from a system perspective. It is suggested that the optimal preventive maintenance time interval is not only determined by the lowest maintenance cost of each machine but also its impact on the whole system. Two main indicators that can accommodate the system perspective are reliability and revenue. A large number of machines and the array of machines can be synthesized in the reliability indicator. Moreover,  the creation of maximum revenue is always the main goal for a business. The best maintenance strategy will be determined from the revenue obtained by a process industry. The process industry discussed in this study is a flour mill which is very well known in Surabaya. This study applied a hybrid simulation to solve this problem. Monte Carlo simulation was used to observe the machine individually and the results are reviewed using the application of System Dynamics. Three improvement scenarios were proposed in this simulation study. Scenario 2 was chosen as the best scenario because it was able to generate the highest revenue at the end of the period. Scenario 2 recommends setting the preventive maintenance time interval considering resource availability.
maintenance strategy; monte carlo simulation; system dynamics; preventive planned maintenance; corrective maintenance
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