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The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.

Title:

The Replace Repair Decision for Heavy Equipment
Authors:
Hyde, Adam S.
James S. Gillespie
James S. Gillespie
Year: 2004
VTRC No.: 05-R8
Abstract: The fleet of equipment operated by the Virginia Department of Transportation (VDOT) constitutes a large investment, on the order of half a billion dollars. A means of identifying earlier and more accurately those pieces of equipment whose timely replacement would keep the cost of maintaining and operating the fleet to a minimum might entail significant savings for VDOT. The purpose of this study was to evaluate the realism of several cost forecasting equations with a relatively small set of equipment cost data. The approach used in the study was (1) a survey of the practice in other states and other agencies and (2) regression analysis of a set of available maintenance and repair cost data from VDOT's Equipment Management System. The authors found that a logarithmic model of variable cost as a function of fuel expense provides a plausible fit to the cost data but that a great deal of the variation in the data remained unexplained. The authors recommend that when identifying candidates for replacement from among the hundreds of (superficially identical) machines within a given equipment type, VDOT's central office and district equipment management compute one additional statistic: the ratio between the average labor and parts cost per dollar of fuel (or per mile) year to date and the average labor and parts cost per dollar of fuel (or per mile) life to date. This statistic would permit an estimate of the expected unit cost for the following year. The authors further recommend that more equipment cost data be archived at the end of each fiscal year.