ABSTRAK

The high level of production at PT. XYZ necessitates an highly efficient and effective maintenance work. However, with the current applied method, PT. XYZ must assess and manually adjust man-hours in cases of production delays. Therefore, it is necessary to develop a capacity planning tool suitable for aircraft engine maintenance to optimize resource utilization, particularly human resources, in order to efficiently manage the workload. This research aims to develop a capacity planning tool using Microsoft Excel with Monte Carlo Simulation, which can generate valid results and also a user-friendly tool..The development of capacity planning started with forecasting maintenance loads and capacity through Monte Carlo Simulation. Then, capacity planning is executed through deterministic approach with capacity allocation table method and linear programming. Optimisation of capacity planning process is achieved through data solver tools from Microsoft Excel. The capacity planning process is separated into two method in which the first method conduct capacity planning for each sub-process of maintenance activity and the second method is to combine all workload and workers of all sub-process into one capacity planning calculation.Second method the capacity planning process shows that the total cost is lower than first method. For the first method the total minimum cost for each inspector and mechanic workers are $49,772.31 and $45,058.87. For the second method, both inspector and mechanic workers respectively has a total minimum cost of $43,962.28 and $43,955.47. For inspector workers, there is a difference of $5,810.03 from method 1 to method 2 where it is around 11.7% reduction of total cost. For Mechanic workers there are a difference of $1,103.40 from method 1 to method 2 where it is around 2.4% reduction of total cost. The recommendations from the research are to use other methods of forecasting besides Monte Carlo Simulation, to use other program besides Microsoft Excel and to include fire and hire employees in the model of capacity.