The management of heavy construction equipment is a difficult task. Equipment managers are
often called upon to make complex economic decisions involving the machines in their charge.
These decisions include those concerning acquisitions, maintenance, repairs, rebuilds,
replacements, and retirements. The equipment manager must also be able to forecast internal
rental rates for their machinery. Repair and maintenance expenditures can have significant
impacts on these economic decisions and forecasts. The purpose of this research was to identify a
regression model that can adequately represent repair costs in terms of machine age in cumulative
hours of use. The study was conducted using field data on 270 heavy construction machines from
four different companies. Nineteen different linear and transformed non-linear models were
evaluated. A second-order polynomial expression was selected as the best. It was demonstrated
how this expression could be incorporated in the Cumulative Cost Modeldeveloped by Vorster
where it can be used to identify optimum economic decisions. It was also demonstrated how
equipment managers could form their own regression equations using standard spreadsheet and
database software.