About the research
Equipment life-cycle cost analysis (LCCA) is typically used as one component of the equipment fleet management process and allows the fleet manager to make equipment repair, replacement, and retention decisions on the basis of a given piece of equipment’s economic life. The objective of this research was to develop a robust method that permits equipment fleet managers to maximize the cost effectiveness of the fleet by optimizing the overall life-cycle value of each piece in the fleet.
Minneapolis Public Works Fleet Services Division (MPWFSD) equipment fleet data were utilized in developing the proposed stochastic LCCA model for the public agency’s fleet. The research compared output using actual data from current software to output from the new stochastic LCCA method using equipment deterioration curves and probabilistic input variables for capital costs, fuel, and other operating costs to demonstrate enhanced ability to optimize fleet management decisions.
The interest rate was found to have a greater impact on economic life output than fuel prices for a dump truck. The fuel volatility did impact the life-cycle costs when applying the stochastic confidence levels. Based on Monte Carlo simulation sensitivity analysis results, the time factor and engine factor were found to be the most sensitive input variables to the LCCA model. This leads to the conclusion that, when deciding to replace a piece of equipment, engine efficiency should be a high priority due to the costs associated with the time factor, engine factor, and its subsequent annual usage.