About the research
Managing an agency’s major equipment fleet is difficult with conflicting priorities. Local agencies are facing huge challenges and economic trade-offs such as leasing versus purchasing and selling versus retaining to optimize their maintenance equipment fleet. These trade-offs require agencies to analyze equipment capital and ownership costs using engineering economics principles in order to make sound and justifiable decisions. However, a consolidation of engineering economics principles and non-economic influential parameters such as the agency’s sustainability goals, volatility of fuel prices, actual annual usage rates for seasonal equipment, technological changes, etc. is required to form a robust equipment fleet management framework that minimizes maintenance and replacement costs. Thus, there is a need to develop a comprehensive methodology and a tool that permit equipment fleet managers to maximize the cost effectiveness of the fleet by optimizing the overall life-cycle value of each equipment unit.
Equipment maintenance optimization has been evolving over the past years. There are various approaches to the equipment maintenance optimization problem that ranges from a simple rule-based approach to a rigorous stochastic life-cycle cost analysis (LCCA) method. Apparently, each approach has its own advantages, assumptions, and limitations and hence the selection of the appropriate approach to optimizing equipment maintenance for local agencies is crucial to ensure successful implementation.
In order to address local agencies’ needs, this project will collect equipment utilization, operation, maintenance, lease, and replacement costs to analyze the life-cycle costs of different types of equipment. Based on the analyzed data, the project will develop a model that considers the equipment life-cycle costs, depreciation, and other influential parameters such as fuel prices and technology advancements to optimize equipment maintenance and replacement plans and associated costs. The final deliverable will be a robust, spreadsheet-based decision support tool.