We analyze a data set containing rental revenues, maintenance costs, and sale prices of five different types of rental machines to econometrically estimate key relationships needed to implement a dynamic programming model of the optimal timing of replacement of rental equipment owned by a large multi-location firm in the equipment rental industry. The model reveals significant potential to improve rental company profitability by improving the strategic timing of equipment replacement. The gains from the optimal replacement strategy come from exploiting seasonal variation in rental demand and the timing of the business cycle due to their effects on rental revenues and the cost of replacement. For some machines we find the optimal replacement strategy is procyclical, but for others we find that a countercyclical replacement strategy –- where replacements are concentrated in slow periods of the business cycle –- can significantly increase firm profits.
The data used in this study are confidential and cannot be made available to third parties as per the conditions of a nondisclosure agreement between the data provider, the authors, and the American Rental Association.
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