Analytics of machine replacement decisions: economic life vs real options

Abstract

Purpose

Despite the existence of multiple asset replacement theories, the economic life replacement method remains a major practical technique for making rational machine replacement decisions. The purpose of this paper is to bridge this method with comprehensive data analytic tools and make it applicable it to modern business reality with abundant data on operating and replacement costs.

Design/methodology/approach

This study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.

Findings

Constructed stochastic algorithms extend the deterministic economic life method and are compared to the contemporary theory of stochastic asset replacement based on real options and dynamic programming. It is proven that both techniques deliver similar results when the cost volatility is small. A major theoretic finding is that the cost uncertainty speeds up the replacement decision.

Research limitations/implications

This research suggests that the proposed stochastic algorithms may become an important tool for managerial decisions about replacement of many similar machines with detailed data on operating and replacement costs.

Originality/value

Compared to the real options replacement theory, major advantages of the proposed algorithms are that they work equally well for any distribution of age-dependent stochastic operating cost. The algorithms are tested on a real industrial case about replacement of medical imaging devices. Numeric simulation supports obtained analytic outcomes.

Citation

Yatsenko, Y. and Hritonenko, N. (2022), "Analytics of machine replacement decisions: economic life vs real options", Management Decision, Vol. 60 No. 2, pp. 471-487. https://doi.org/10.1108/MD-12-2019-1704