Availability Modeling of Sugarcane Harvesting System by Using Markov Chain

Sugarcane, as an important industrial crop, is always considered as one of the strategic commodity and supported by governments. One of the most important repairable systems in agro-industrial companies is a sugarcane harvester machine. The failures of this machine cause a delay in operations and reduce product yield and quality. This machine has a key role in sugarcane harvesting operations of the agro-industries. Availability of sugarcane harvester machine was determined by using the Markov chain method which is a robust probabilistic method according to the actual conditions of the sugarcane harvesting system in the agro-industries. The methodology outlined in this study has been utilized to 12 sugarcane harvester machines, namely CASE IH Austoft 7000. According to the results, harvesting system availability was calculated as 87.5%, 86.4%, 95.3%, and 90.4% for the first, second, third, and fourth harvesting groups, respectively. For these groups, the down probability of the system is evaluated to be 12.5%, 13.6%, 4.7%, and 9.6%, respectively. On average, the down probability was 10.1%, meaning that the machine will not be available at 10.1% of days at harvesting season. Due to the high sensitivity of the crop that delayed harvesting, agro-industry managers should try to reduce this amount by increasing system reliability and optimizing planned maintenance activities to decrease scheduled downs that have a direct effect on harvest time.