Abstract:
Due to intense global competition and increasing demands from stakeholders, companies are striving to improve and optimize their productivity in order to stay competitive. Reliability and availability are some of the main factors that influence performance and competitiveness of production facilities. Therefore, it is the objective of the maintenance department to maximize the machine availability. The use of effective maintenance policies is one of the methods that have been adopted in many manufacturing organizations to maximize the availability of machine downtime. However, the main issue in maintenance policy optimization is in determining the
optimal time to carry out a maintenance task. The purpose of this research was to determine the optimal time for preventive maintenance that can be utilized at East Africa Portland Cement Company (EAPCC) in order to minimize the downtime, maintenance cost and maximize the availability, the productivity and consequently the profitability of this organization. Secondary data from the maintenance department at EAPCC was used. A Root Cause Analysis was conducted to determine the most critical component. Multi-Criterion-Decision-Making (MCDM) was used to determine the most critical plant. The maintenance key performance indicators (KPis) that were used include availability, downtime and number of failures. It was found out that the tuff hopper and the crane were the most critical equipment and component respectively. The failure and repair data were modeled by use of the Wei bull and the lognormal distribution respectively. The Weibull and lognormal parameters were then used in estimating the optimal time for preventive maintenance (PM). It was found out that the most optimal PM interval for the critical component is 9 months. The main causes of downtime on the critical
equipment were found out to be blockage of the tuff hopper and aging of the crane.
Consequently, to minimize the downtime on the most critical equipment and component, three options were proposed: redesign the tuff hopper, replace the crane and use the tool-box. It was found out that replacing the crane and redesigning the tuff hopper have the most attractive Net Present Value.
Due to lack of data, this research did not take into consideration the effect of external factors (factors such as environmental effect, technology improvement and human skills) andmaintenance supportability on PM. Therefore, possible future research should incorporate theseparameters in the estimation of the PM timing at EAPCC.
