Open AccessA Condition-based maintenance and spare parts provisioning based on markov chains
Author(s) -
Hasurhasanah,
Ari Yanuar Ridwan,
Budi Santosa
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/673/1/012101
Subject(s) - spare part , provisioning , production (economics) , schedule , production schedule , computer science , markov chain , lead time , operations research , economic order quantity , reorder point , reliability engineering , reliability (semiconductor) , order (exchange) , operations management , business , supply chain , engineering , scheduling (production processes) , economics , finance , marketing , telecommunications , power (physics) , physics , quantum mechanics , machine learning , macroeconomics , operating system
Machine is a vital tool of the company in helping the production process. Every company expects the production to run smoothly, but sometimes it is hampered by damage that happened to the machine, so that the production process is disrupted and causes losses to the company. Engine damage can be minimized by regularly evaluating the condition of the spare parts. In practice, if the spare parts inventory policy is not accurate it will cause stock outs or overstocks, which can lead to more costs for the company. The worst case is if there is no spare part stored in the warehouse when it is needed, it can make the production floor stopped, which in the end makes the company can’t fulfil their production target. This research aims to obtain an optimal preventive maintenance schedule by calculating the machine’s reliability and inventory provisioning policy for the spare parts according to predicted amount that will be needed in the future calculated using Markov chains so the company can determine the reorder point (r) and the economic order quantity (EOQ).