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This article builds on and extends previous research in the area of Human Resource Accounting (HRA). Its purpose is to present a study involving the development and application of a model for measuring the replacement cost of people. Specifically, the study involved the development and application of a human resource replacement cost model for Industrial Engineers in the US Navy.

The purpose of this paper is to present novel recursive expressions for modelling the replacement costs of aircraft engine life-limited parts during shop visits to assist engine operators in both evaluating their decisions regarding the applied life-limited parts management strategies and tracking the replacement costs consistently throughout the life of the engine.

The replacement costs of aircraft engine life-limited parts are modelled analytically in this research, which strives to quantify the costs of used and unused lives of the replaced parts, incurred during engine shop visit events. Inputs for this model include the list price of life-limited parts, the replacement decisions made on all previous shop visits and the number of cycles the engine has operated at different thrust ratings on all previous operating intervals.

The average annual escalation rate of life-limited parts list prices was shown to range from 5% to 7%. The presented model is not only suitable for calculating the costs of used and unused lives of life-limited parts during past engine shop visit events but also for application in the life-limited parts replacement cost forecasting and optimisation models.

Uniquely derived recursive expressions represent the final result of the developed model which, to the authors’ knowledge, had not been studied elsewhere in the academic literature. The analysis of aircraft engine life-limited part list prices carried out to account for the average annual escalation rate enables the prediction of replacement costs during subsequent shop visits.

A new policy for joint optimization of age replacement and spare provisioning has been proposed by incorporating a continuous review (s, S) type inventory policy, where s is the reorder level and S is the maximum stock level. Gives cost formulations for a single operating unit situation and outlines simulation procedure to determine optimal values of the decision variables by minimizing total cost of replacement and inventory. Studies the behaviour of this policy for a large number of case problems and highlights the effects of different cost elements, item failure characteristics and lead time characteristics. Also determines, for all case problems, optimal (s, S) policies to support Barlow‐Proschan age policy. Simulation results clearly indicate that the jointly optimal policy is more cost‐effective than Barlow‐Proschan policy.

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.

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

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.

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.

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.

The purpose of this paper is to develop a risk‐based integrity model for the optimal replacement of offshore process components, based on the likelihood and consequence of failure arising from time‐dependent degradation mechanisms.

Risk is a combination of the probability of failure and its likely consequences. Offshore process component degradation mechanisms are modeled using Bayesian prior‐posterior analysis. The failure consequences are developed in terms of the cost incurred as a result of failure, inspection and maintenance. By combining the cumulative posterior probability of failure and the equivalent cost of degradations, the operational life‐risk curve is produced. The optimal replacement strategy is obtained as the global minimum of the operational risk curve.

The offshore process component degradation mechanisms are random processes. The proposed risk‐based integrity model can be used to model these processes effectively to obtain an optimal replacement strategy. Bayesian analysis can be used to model the uncertainty in the degradation data. The Bayesian posterior estimation using an M‐H algorithm converged to satisfactory results using 10,000 simulations. The computed operational risk curve is observed to be a convex function of the service life. Furthermore, it is observed that the application of this model will reduce the risk of operation close to an ALARP level and consequently will promote the safety of operation.

The developed model is applicable to offshore process components which suffer time‐dependent stochastic degradation mechanisms. Furthermore, this model is developed based on an assumption that the component degradation processes are independent. In reality, the degradation processes may not be independent.

The developed methodology and models will assist asset integrity engineers/managers in estimating optimal replacement intervals for offshore process components. This can reduce operating costs and resources required for inspection and maintenance (IM) tasks.

The frequent replacement of offshore process components involves higher cost and risk. Similarly, the late replacement of components may result in failure and costly breakdown maintenance. The developed model estimates an optimal replacement strategy for offshore process components suffering stochastic degradation. Implementation of the developed model improves component integrity, increases safety, reduces potential shutdown and reduces operational cost.

The costs of physical distribution, like the costs of production or marketing, consist of a number of elements whose prices are frequently determined independently and without apparent reference to each other. In the same way that production costs can be controlled by varying the inputs of capital, raw materials and labour according to their current cost relationships; or the efficiency of a marketing programme maximised through judicious adjustment of the marketing mix, so can the costs of physical distribution be minimised by scientific warehouse location, inventory control, transport mix analysis, containerisation and route planning. Widespread application of operational research techniques, industrial engineering and marketing research mean that the spectacular savings available in the past in marketing and production have been achieved.

The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.

A proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.

The mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.

In practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.

In most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.

The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user‐friendly, farm‐specific, decision‐making tool for dairy producers or their advisers and technology manufacturers.

The basic deterministic model was created in Microsoft Excel (Microsoft, Seattle, Washington). The @Risk add‐in (Palisade Corporation, Ithaca, New York) for Excel was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model was composed of a series of modules, which synergistically provide the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical US dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. In this paper, the model structure and methodology were described in detail.

Examples of the utility of examining the influence of stochastic input and output prices on the costs of culling, days open, and disease were examined. Each of these parameters was highly sensitive to stochastic prices and deterministic inputs.

Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

A cow that fails to conceive must either be kept for a year without revenue or replaced by a bred heifer. This choice is a unique case of comparing investments with different economic lives because the potential replacement asset is just a newer version of the old asset. In this study, a net present value model is developed that eliminates the problem of finding a common timeframe. Results indicate there are often times producers should keep the open cow. Whenever feed costs are low, the price differential between cull cows and replacement heifers is high, or the calf crop value is low, retaining open cows becomes more desirable.

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.

The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.

The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.

A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.