Fleet Asset Life Cycle Costing with Intelligent Vehicles

Abstract

Life cycle costing seeks to find the optimum economic life of a particular asset considering acquisition, maintenance, operational, and disposal costs over the time it is held. The economic life can vary depending on interest rates, depreciation, maintenance, and overhead. A model was built to calculate economic life cycles for four classes of passenger cars and three classes of motor trucks and truck tractors within Minnesota’s Department of Transportation using data from the M4 information system. For class 330 snowplows in Districts 1 and 6, cost data from M4 regularly were under-reported in comparison to the Minnesota Accounting Procurement System (MAPS) from previous work. Due to high uncertainty of input data integrity in M4, various sensitivity analyses were run. Results included families of cost curves to estimate optimal life cycles for varying cost parameters. A key finding is that data may not be recorded fully, accurately, or assigned to the correct asset, indicating the need for automating as much future data collection as possible. With good data, decision makers can determine how long assets should be kept and maintained in general as a fleet, keeping in mind that results from this model are not indicative of any single unit.