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Downtime resulting from equipment failure is a major problem consistently faced in highway construction. Since managing construction equipment is tightly connected to various activities and parties inside as well as outside of the firm, failure to account for this fact invariably causes downtime to be even more severe. Variation in equipment management practices is thus, indeed, a root cause of the dynamics of machine downtime. This study is intended to address key dynamic features of heavy equipment management practices and downtime in small to medium highway contracting firms and propose policies for equipment performance improvement.

Face‐to‐face interviews with equipment managers from five different small to medium highway construction companies in Thailand were conducted. Data were analysed using a system dynamics (SD) simulation approach.

To overcome downtime problems, contractors need to understand the dynamics of downtime as well as its influential factors, and thus manage their equipment as a dynamic process rather than one that is static. Based on the simulation, various policies are proposed to improve the performance of heavy equipment for small to medium highway contractors.

The research is of value in facilitating better understanding on the dynamics of equipment management practices and downtime as well as their interdependency.

Pareto histograms are commonly used to determine maintenance priorities by ranking equipment failure codes according to their relative cost or downtime contribution. However, such histograms do not readily enable identification of the dominant variables influencing downtime and repair costs, namely the failure frequency, mean downtime and mean repair cost associated with each failure code. Advances an alternative method for analysing equipment downtime and repair costs using logarithmic (log) scatterplots. By applying limit values, log scatterplots can be divided into four quadrants enabling failures to be classified according to acute or chronic characteristics and facilitating root cause failure analysis. Log scatterplots permit the identification of frequently occurring failures that consume relatively little repair cost or downtime yet cause frequent operational disturbances leading to production losses. In addition, by graphing the trend of failure data over successive time periods, log scatterplots provide a useful visual means of evaluating the performance of maintenance improvement initiatives. Provides examples of the practical application of log scatterplots by a number of mining companies and mining equipment suppliers in Chile.

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

Wind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction.

Wind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs.

The wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities.

This paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used.

The main objective of this study was to analyse the economics of introducing IT in the maintenance department. The economics in this case was determined by conducting a quantitative analysis on the reduction of operational costs, on increase in productivity and on quality improvement. A comparison was made to analyse company performance in the maintenance before and after the introduction of IT in the maintenance department. The analysis shows that there were reductions of operational and inventory holding costs. Likewise, it was shown that there was also improvement in product quality and productivity.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

The unpredictable failure of submersible pump (SP) in groundwater irrigation systems has considerable negative economic consequences. The purpose of this paper is to develop a total cost minimization model that aims to optimize maintenance actions for SP. It reports on simulation-based stochastic scenario analysis for evaluating total cost of maintenance.

Stochastic simulation modeling has been performed for failure of pump motor and corresponding maintenance. Five alternative scenarios were compared for total cost over 15 years starting with empirical data from a northern Ethiopian site. Downtime probabilities and spare part supply uncertainty have been considered in the mathematical model. The model is also validated using multiple ways.

The scenario comparisons indicate that despite the challenges of accessing SP doing one motor rewinding for each purchased pump system upon failure (preferably with shorter supply lead time and variability) seems to result in lowest overall costs for the time horizon considered.

The model should help to make informed practical decision regarding planning and management of SP failure systems in a developing economy context. This should, therefore, lead to better revenue for smallholder farmers and improved food security in similar context.

There are limited number of publications that consider the life cycle costs with stochastic analysis when it comes to maintenance of SPs. To the best of the authors’ knowledge, no paper has previously directly addressed maintenance cost optimization for SP in irrigation. The study could be used to develop more sophisticated stochastic models with more efficient algorithms and consideration of additional sources of stochasticity for such system.

Maintenance management is a vital strategic task given the increasing demand on sustained availability of machines. Machine performance depends primarily on frequency and downtime; therefore, ranking critical machines based on these two criteria is important to determine the appropriate maintenance strategy. The purpose of this paper is to compare two methods, using case studies, to allocate maintenance strategies while prioritising performance based on frequency and downtime or Mean Time to Repair: the Decision Making Grid (DMG) and Jack-Knife Diagram (JKD).

The literature indicates the need for an approach able to integrate maintenance performance and strategy in order to adapt existing data on equipment failures and to routinely adjust preventive measures. Maintenance strategies are incomparable; one strategy should not be applied to all machines, nor all strategies to the same machine.

Compared to the Pareto histogram, the DMG and JKD provide visual representations of the performance of the worst machines with respect to frequency and downtime, thus allowing maintenance technicians to apply the appropriate maintenance strategy. Each method has its own merits.

This work compares only two methods based on their original conceptualisation. This is due to their similarities in using same input data and their main features. However, there is a scope to compare to other methods or variations of these methods.

This paper highlights how the DMG and JKD can be incorporated in industrial applications to allocate appropriate maintenance strategy and track machine performance over time.

Neither DMG nor JKD have been compared in the literature. Currently, the JKD has been used to rank machines, and the DMG has been used to determine maintenance strategies.

This article addresses purchasing decisions and the use of total cost of ownership (TCO) information. TCO is based on a monetary quantification of nonfinancial attributes and aggregation into a summary measure (such as cost per hour, per wafer, or per kilometer). From an accounting point-of-view, one intricate issue is the accuracy of the monetary quantification and how this affects decision-making. We distinguish three different kinds of inaccurate monetary quantification, and we investigate the weight that decision makers attach to attributes that are inaccurately monetarily quantified and subsequently included in TCO information. Specifically, we investigate whether this weight depends on reflective thinking and experience. This question is relevant beyond TCO, for all decision-making situations that involve monetary quantification of attributes and subsequent aggregation, such as in activity-based costing, net present value calculations for capital budgeting decisions, or cost-benefit analyses in public administration.

We found support for the hypothesis that reflective thinking increases the weight decision makers attach to the attribute that is included as a minimum cost in the TCO-numbers, but not for the hypothesis that reflective thinking would reduce the weight attached to the attribute that is included as a maximum cost in the TCO-numbers. Students and practitioners differed significantly in the weight they attached to an attribute that was excluded from the TCO-numbers, and practitioners gave less weight to such attributes. Together these results suggest that TCO-numbers should be provided with care and possible inaccuracies should be clarified.

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.