Analytics of machine replacement decisions: economic life vs real options

Abstract

PurposeDespite 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/approachThis study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.FindingsConstructed 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/implicationsThis 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/valueCompared 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.

Decision Making on Control and Monitoring Equipment Replacement with Sustainability Consideration

“…Other research in the area of asset replacement decisions considers various factors as cited in Madusanka et al (2016). Some of the factors considered for asset replacement are the cost of repairing old assets (Rabbani and Shahmohamad 2014), sunk costs (Muñoz-Porcar et al 2015), lifecycle costs (Akhlaghi 1987;Diniz and Sessions 2020;Hastings 2010;Kelso 2018;McClurg and Chand 2002;Panegossi and da Silva 2021;Yatsenko and Hritonenko 2022), remaining asset life (Gage 2013;Muñoz-Porcar et al 2015), remaining economic life of assets (Kelso 2018;Muñoz-Porcar et al 2015;Yatsenko and Hritonenko 2022), future organizational strategic plans (Gage 2013), asset repairability (Gage 2013), failure record (Kelso 2018;Muñoz-Porcar et al 2015), existing value of assets (Muñoz-Porcar et al 2015), benefits (Alabdulkarim et al 2015;Hastings 2010), capacity and capability (Diniz and Sessions 2020;Hastings 2010), how critical these assets are to the production process (Hastings 2010), technology (Hartman and Tan 2014;Hastings 2010;Panegossi and da Silva 2021), obsolescence (Hastings 2010;Kelso 2018), risk (Hastings 2010), equipment price (Diniz and Sessions 2020;Yatsenko and Hritonenko 2022), salvage value (Diniz and Sessions 2020;Yatsenko and Hritonenko 2022), equipment age (Kelso 2018), and usage rates (Kelso 2018).…”