Deterministic models for premature and postponed replacement

Adkins, Roger; Paxson, Dean

doi:10.1016/j.omega.2013.01.002

Cited by 21 publications

(14 citation statements)

References 45 publications

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“…For simplicity, henceforth we assume that ρ = 0. 4 The same holds for(Adkins and Paxson, 2011a), seeTable 2;Heydari et al (2012), see equation(19);Adkins and Paxson (2013a), see equation(9); Adkins and Paxson (2013b), seeFigure 2;Fleten et al (2016), see equation(17); Støre et al(2016), see equation(18); and Adkins and Paxson (2017), seeTable 3.…”

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confidence: 84%

“…The methodology developed in Adkins and Paxson (2011b) has been applied in several contributions. In the context of replacement options, Adkins and Paxson (2013a) examine premature and postponed replacement in the presence of technological progress, where revenue and operating costs are treated as stochastic. Adkins and Paxson (2017) use a general replacement model to investigate when it is optimal to replace an asset whose operating cost and salvage value deteriorate stochastically.…”

Section: Introductionmentioning

confidence: 99%

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Investment Decisions with Two-Factor Uncertainty

et al. 2017

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This paper considers investment problems in real options with non-homogeneous two-factor uncertainty. It shows that, despite claims made in the literature, the method used to derive an analytical solution in one dimensional problems cannot be straightforwardly extended to problems with two stochastic processes. To illustrate, we analyze an investment problem with two stochastic revenue streams and a constant sunk cost. We show that a semi-analytical approach leads to a sub-optimal investment policy. The main message of our paper is that non-homogeneous investment problems can only be solved numerically.

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confidence: 84%

Section: Introductionmentioning

confidence: 99%

Investment Decisions with Two-Factor Uncertainty

et al. 2017

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“…Adkins and Paxson [1] justify postponement due to obsolescence. However if the system in use is replaced by a non-identical system (with say enhanced functionality or lower operating costs or both), the times between successive replacements are no longer identically distributed random variables and then the theory of renewal-reward processes cannot be used.…”

Section: Finite Horizon Modelmentioning

confidence: 99%

A study of postponed replacement in a delay time model

Berrade

Scarf

Cavalcante

2017

Reliability Engineering & System Safety

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We develop a delay time model for a one component system with postponed replacement to analyze situations in which maintenance might not be executed immediately upon discovery of a defect in the system. Reasons for postponement are numerous: to avoid production disruption or unnecessary or ineffective replacement; to prepare for replacement; to extend component life; to wait for an opportunity. This paper explores conditions that make postponement cost-effective. We are interested in modelling the reality in which a maintainer either prioritizes functional continuity or is not confident of the inspection test indicating a defective state. In some cases more frequent inspection and a longer time limit for postponement are recommended to take advantage of maintenance opportunities, characterized by their low cost, arising after a positive inspection. However, when the cost of failure increases, a significant reduction in the time limit of postponement interval is observed. The examples reveal that both the time to defect arrival and delay time have a significant effect upon the cost-effectiveness of maintenance at the limit of postponement. Also, more simply, we find that opportunities must occur frequently enough and inspection should be a high quality procedure to risk postponement.

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“…We provide a format for some of these types of technological innovation, considering stochastic technological progress and also the real replacement value implied by various models, in contrast to Adkins and Paxson (, ). We allow technological advances to involve different successor initial operating cost levels compared to like‐for‐like equipment replacements; consider the possible volatility and time drifts of technological progress; and consider the correlation of technological progress with both the evolution of revenues produced by the incumbent technology, and the current operating costs of the existing technology.…”

Section: Introductionmentioning

confidence: 99%

“…Armada, Kryzanowski and Pereira (2011) show the implications for investment when there may be hidden rivals. Adkins and Paxson (2012) (2011) formulate a two-factor, real-option replacement model for an asset that is subject to uncertainty in the magnitude of the input and output decay but do not allow for technological progress. Adkins and Paxson (2012) allow for technological progress, but not for revenue, cost or technological progress uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Equipment Capital Budgeting with Technological Progress

Adkins

Paxson

2013

Euro Fin Management

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We provide multi‐factor real option models (and quasi‐analytical solutions) for equipment capital budgeting under uncertainty, when there is either unexpected, or anticipated, or uncertain (volatile) technological progress. We calculate the threshold level of revenues and operating costs using the incumbent equipment that would justify replacement. Replacement is deferred for lower revenue thresholds. If progress is anticipated or highly uncertain, alert financial managers should wait longer before replacing equipment. Replacement deferral increases with decreases in the expected correlation between revenue and operating costs, and with increases in the revenue and/or operating cost volatility. Uncertain technological progress increases the real option value of waiting. The best approach for equipment suppliers is to reduce the expected revenue and/or cost volatility, and/or reduce the expected uncertainty of technological innovations, since then an incentive exists for the early replacement of old equipment when a technologically advanced version is launched.

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Deterministic models for premature and postponed replacement

Cited by 21 publications

References 45 publications

Investment Decisions with Two-Factor Uncertainty

Investment Decisions with Two-Factor Uncertainty

A study of postponed replacement in a delay time model

Stochastic Equipment Capital Budgeting with Technological Progress

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