determine the optimum preventive maintenance frrequency for each of the pieces of equipment if breakdown time is normally distributed
To determine the optimum preventive maintenance frequency for each piece of equipment, we need to consider the trade-off between the cost of maintenance and the potential cost of breakdowns.
One approach is to use a cost-based optimization model. Here are the steps to follow:
1. Gather data: Collect historical data on breakdowns for each piece of equipment, including the time between breakdowns and the associated costs (e.g., repair costs, production losses, etc.). It is also important to know the cost of preventive maintenance.
2. Calculate the cost of preventive maintenance: Determine the cost of performing preventive maintenance for different frequencies. This cost includes labor, materials, and any other associated expenses.
3. Calculate the cost of breakdowns: Estimate the cost of breakdowns for different time intervals between maintenance. This can be done by considering the historical breakdown data and the associated costs.
4. Build a cost model: Use the collected data to create a cost model that calculates the total cost (preventive maintenance cost + breakdown cost) for each maintenance frequency. The model will typically involve statistical analysis, such as calculating the mean and standard deviation of the time between breakdowns.
5. Perform optimization: Apply optimization techniques to find the maintenance frequency that minimizes the total cost. This can be done using techniques like linear programming or simulation-based optimization.
6. Evaluate the results: Analyze the results obtained from the optimization model to determine the optimum preventive maintenance frequency for each piece of equipment. Consider factors such as cost savings, risk reduction, and the practicality of implementing the recommended maintenance schedule.
It is worth mentioning that the assumption of normally distributed breakdown time is just one assumption that can be used in the analysis. Depending on the specific characteristics of the equipment and the nature of breakdowns, alternative statistical distributions and modeling techniques may be more appropriate.