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A capital budgeting decision procedure appropriate for choosing the continuance, replacement, or abandonment of an asset‐in‐place is examined. The optimal replacement decision on an asset already in service requires simultaneous consideration of project life, project chaining, and possible abandonment points for both the asset‐in‐place and the replacement asset. The additional information required for the suggested procedure over the traditional replacement procedure is generally manageable and a practical solution procedure is feasible.

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is a unique case of comparing investments with different economic lives because the potential replacement asset is just a newer version of the old asset.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.

Wear life of mill liners is an important parameter concerning maintenance decision for mill liners. Variations in process parameters such as different ore properties due to the use of multiple ore types influence the wear life of mill liners whereas random order of processing, processing time and monetary value of different ore types leads to variation in mill profitability. The purpose of the present paper is to develop an economic decision model considering the variations in process parameters and maintenance parameters for making more cost‐effective maintenance decisions.

Correlation studies, experimental results and experience of industry experts are used for wear life modeling whereas simulation is used for maximizing mill profit to develop economic decision model. The weighting approach and simulation have been considered to emphasize the contribution of parameters such as ore value and processing time of a specific ore type to a final result.

A model for estimating lifetime of mill liners has been developed based on ore properties. The lifetime model is combined with a replacement interval model to determine the optimum replacement interval for the mill liners which considers process parameters of multiple ore types. The finding of the combined model results leads to a significant improvement in mill profit. The proposed combined model also shows that an optimum maintenance policy can not only reduce the downtime costs, but also affect the process performance, which leads to significant improvement in the savings of the ore dressing mill.

The proposed economic decision model is practically feasible and can be implemented within the ore dressing mill industries. Using the model, the cost‐effective maintenance decision can increase the profit of the organization significantly.

The novelty is that the new combined model is applicable and useful in replacement decision making for grinding mill liners, in complex environment, e.g. processing multiple ore types, different monetary value of the ore type and random order of ore processing.

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.

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 cotton industry has seen many technological advances throughout history that have greatly decreased the number of labor hours required to produce a bale of cotton. The latest advancement is a harvesting system that replaces the harvester, boll buggy, and module builder with a single machine. This is an asset replacement decision where there are multiple assets being replaced but the old technology (the defender assets) may all have different remaining lives and optimal lifespans. The purpose of this paper is to find the optimal time to replace the multiple defender assets with a single challenger asset (the improved technology). The goal is to determine if the ages of the boll buggy and the module builder affect the replacement age of the conventional picker.

The paper extends the Perrin model to allow for multiple defender assets.

The paper finds that the supporting assets do sometimes affect the decision to replace a conventional cotton picker. If the supporting assets are newer, then the replacement decision may be delayed and if the supporting assets are older then the replacement decision may be accelerated. Field efficiency can affect the decision as well.

While the Perrin model has been used extensively, the authors believe the application to a multiple asset defender is unique. Although this type of replacement decision is not common, there could be other applications as new technology is introduced on the farm.

Unlike point of purchase behavior, not much is known about how payment method impacts post-purchase behavior, especially for durable goods where user experience can last over long periods. The purpose of this paper is to link two strands of literature for the first time by uncovering systematic linkages between the payment method (upfront cash vs loan) used for purchase of durable goods and the replacement timings for the same.

The authors predict that cash purchases are more likely to have shorter replacement horizons compared to loan purchases and propose a psychological mechanism that accounts for the same. Their arguments are based on how the strength of coupling, which is the degree of psychological association between payment and consumption, depends on the payment method and differentially influences the consumption experience and consequently leads to different replacement horizons. They conduct a field study to test their predictions and find support for their model.

The authors find that individuals who financed their durable goods purchases using loan, expressed their intentions to replace the goods after longer period than those who financed their durable goods with cash down payment. As loan installments remind people of painful thoughts of payment, they tend to reduce the dissonance by positively evaluating both retrospective and anticipated usage experiences. This dissonance reduction mechanism eventually leads to reduced willingness to let go of the durable.

Marketers are faced with a tradeoff between increasing purchase likelihood versus ensuring long-term post-purchase satisfaction. In this paper, the authors uncover the psychological mechanisms that can explain how payment method chosen to pay for a durable can have direct effect on post-purchase consumption experiences and subsequently in the replacement intentions. This finding is crucial for marketers who are interested in planning the product line launches and other post-purchase engagement strategies such as buy-back scheme and upgrades.

Understanding the psychological mechanisms that explain individual’s likelihood to replace their durable goods allows policymakers to design appropriate interventions to induce more sustainable and efficient use of durable goods in the market. While on one hand, marketers might be interested in increasing sales of their product line by inducing faster replacement of older product versions, environmentalists nudge towards the opposite. This paper provides a possible way to achieve the dual objectives.

While past research on downstream effects of payment methods on behavioral outcomes focused only on consumables, the authors focus on durable goods. Further, they identify the effect of payment method on both psychological and behavioral outcomes.

This paper proposes a decision support model that can be used to help decide the destination of defective products, for mass production industries. The objective of this model is to reduce the cost of the defect, and consequently reduce the total quality costs.

The decision model was developed based on the theory of quality costs and decision-making models, considering the practical aspects of reality through data collection, observation and experience in Industrial Pole of Manaus (Brazil) industries. A decision model adjusted to reality assists in the construction of the decision process, indicating the facts, data collection and the planning of the actions to choose the best alternative.

The specific contributions of this research are: (1) define a sequential structure of actions, effects and costs associated with defective items; (2) allow a comprehensive approach to failure costs, including various elements of lost opportunity costs; (3) minimize failure costs, and consequently reduce total quality costs, without necessarily investing in prevention and assessment; (4) describe the use and application of the built theory; (5) identify the quality cost elements most representative in existence of defective items; and (6) identify improvement points in the management of possible future defective items.

Much of the work of implementation of quality cost models do not emphasize the analysis of the destination of defective items. Also, there are no studies that use decision models with identification, accounting and evaluation of effects and criteria of quality, productivity and cost to define the destination of manufacture defective items.

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.

The purpose of this paper is to assess life cycle costing (LCC) of the equipment in a more realistic, precise, and applicable manner, and to apply it to a real industrial problem.

Based on the failure rates of the components of a machine, the LCC is assessed, mathematically modeled, and incorporated to the parallel machine replacement problem with capacity expansion consideration. The problem is modeled as mixed integer programming which intends to minimize the total costs incurred during a planning horizon of several periods for the machines of the same type with different ages. The decision variables are the number of machines to be purchased/salvaged in each period. A genetic algorithm (GA) is developed for solving the problem and its efficiency is verified.

In conventional models presented for calculation of LCC, corrective maintenance (CM) costs of the machines are incorporated to the model as a whole which may result in inaccurate calculations. Obtaining this value is also very difficult and it can be different for machines with different ages. By calculating the CM costs of a machine based on the failure rates of its components, the LCC can be properly estimated in a realistic and precise manner. The presented GA is also proven to be efficient for solving problems of almost any size with different number of machines, components, and planning periods.

The presented model and GA are applied to a real case of a construction company that needs to determine a purchase/salvage schedule for its loaders in the next ten years. Results of the calculated schedule imply that employing new loaders rather than maintaining the aged ones generally results in the minimum LCC.

This paper presents a novel approach for precise, meaningful, and practical LCC calculation. The mathematical model and its solving method can be utilized by both the manufacturers and buyers of equipment as a tool which determines a parallel machine purchase/salvage schedule for a planning horizon of several periods which incurs minimum overall cost. The presented material can be also applied to other industrial problems and cases.