Browsing by Subject "Fleet management"

Now showing 1 - 3 of 3
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    Benchmarking Fleet Management
    (2003-07-01) Wyrick, David A; Storhaug, Brandon
    Benchmarking is used to assess the best practices in fleet management for MnDOT. Phase I was a regional study that focused on organizational structure, performance measures and targets, policies, and maintenance in a variety of public and private organizations. Phase II was a national assessment focusing on performance measurements that are most useful in state transportation departments. Thirty-five states participated in the Phase II survey and seven benchmarking interviews were conducted (Arizona, Maine, Michigan, New Hampshire, New York, Oregon, Pennsylvania) to assess the number, types, and effectiveness of performance measures used in leading state transportation departments. Analysis of data from both phases identified best practices and gaps that MnDOT should consider. Minnesota is assessed to have one of the better fleet management approaches, but many opportunities for improvement are possible. Recommended performance measures are given for the state, district, and shop levels, with appropriate reporting periods (monthly, quarterly, annually). Recommendations are presented regarding control limits, organizational performance indices, strategic planning, predictive maintenance, purchasing standards, cost/benefit analysis, fleet asset centralization, internal rental rates, bar coding, asset replacement, pursuing the Malcolm Baldrige National Quality Award, and review committee makeup. Possible future benchmarking work includes costing, utilization, and asset life cycle analysis.
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    Fleet Asset Life Cycle Costing with Intelligent Vehicles
    (Center for Transportation Studies, University of Minnesota, 2008-08) Wyrick, David A.; Erquicia, Santiago
    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.
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    FY06 NATSRL-Integration of Automated Vehicle System Data Acquisition into Fleet Management
    (Minnesota Department of Transportation, 2008-03) Wyrick, David; Eseonu, Chinweike
    Previous work at the University of Minnesota Duluth on fleet asset management determined a key shortcoming in life cycle costing analysis was the poor quality of available data. Automation of data acquisition was recommended to minimize errors inherent in manual data collection and data entry. This project investigates the feasibility of collecting data from engine computers on board Class 330 and 350 snowplows and wirelessly transmitting these data directly into the Maximus M5 maintenance information system. Wireless modems were evaluated to select models that could function in the physical setting and temperature conditions of northern Minnesota. One modem met all the criteria and was field-tested in several different snowplow units. The modem functioned, but the interface with the on-board computer prevented data transfer due to embedded proprietary software by the engine manufacturers. Third party providers do offer real-time remote data collection from engine computers. The Minnesota Department of Transportation (Mn/DOT) expressed concern over data protection and cost with this approach, so alternative management strategies were developed. Under these conditions, the recommendation is to increase the frequency of cable-acquired data from the engines and implement best practices for data acquisition.