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Despite the proven benefits of high‐performing spare parts logistics, recommendations on how to organize spare parts logistics in China are rather rare. The absence of spare parts logistics concepts for China is surprising, since the spare part business is the profit pool of the capital goods industry: spare parts create about 17 percent of the industry's total revenue. The margins involved in this spare parts revenue are, on average, 25 percent compared to 2‐3 percent of the capital goods. This paper aims to offer recommendations to increase spare parts logistics performance.

The authors conducted an extensive benchmarking project with a variety of firms (focus group and single case study) in order to gain a better understanding of spare parts logistics in China. By reviewing the first benchmarking findings with a single company that struggled to achieve sufficient spare parts logistics performance, additional insight was gained into how spare parts logistics should be organized in China.

The paper attempts to provide a better understanding of the necessary changes for improving logistics performance in the Chinese market. It analyzes the necessary changes to achieve a cutting‐edge logistics solution, and shows how companies can implement the solution.

Research limitations come from the qualitative nature of the research.

Managers can use the results obtained in this study to challenge their current logistics practices and develop a project procedure on how to initiate logistics projects that lead to cutting‐edge logistics performance.

Rather than concentrating on performance benchmarks of the supply chain of spare parts or specific aspects of spare parts management, the paper develops the setting up of a cutting‐edge logistics solution for China and Asia. The cutting‐edge solution is based on two main pillars: companies should try to develop logistics solutions for Asia that consider existing Asian and Chinese constraints instead of taking the logistics practices used in mature markets and trying to adapt them to the Chinese market, and the development of the logistics solution should be in intensive collaboration with the logistics providers.

This article shows the use of benchmarking for the spare parts logistics in the German mechanical industry (investment goods or capital goods). After pointing out the areas of benchmarking application, the use of total function deployment (TFD) for the evaluation of benchmarking metrics is explained. With TFD customer requirements on spare parts supply and the process structure of spare parts logistics are brought together in quality matrices, which are the bases for the metrics derivation. The last paragraph contains the results of a survey in the German mechanical industry. Concrete values of benchmarking metrics from the questioned companies, belonging to metrics on delivery service, costs of logistics and on materials management, are given. All theoretical and empirical results are drawn from a research project conducted in the Institute of Business Administration/Department of Business Management at Darmstadt University of Technology.

The work deals with the topic of spare parts management in a space system. The paper is divided into three parts. The first one is dedicated to the characterization of the system structure and presents the particularities related to the spare‐elements procurement. Modelling is the object of the second part. After having exposed the bases of the problem to be solved, a macro‐model is introduced. Each of the three elements of an orbital system, namely ground, flying and transport, is then described with a Petri net. Operation specificities of every element are then listed and integrated into the model. A concrete application of this modelling is given in the last part. It concerns the Columbus laboratory of the International Space Station. A representative function is selected and several supply strategies are evaluated.

This paper aims to propose a practical method of performing maintainability analysis of an offshore system at operation phase having some improvement trend.

The analysis follows a systematically developed method of analyzing maintenance data, identifying critical factors affecting system performance, and developing suitable downtime distribution model with some applications of statistical analysis techniques and expert opinion.

Improvement in spare part logistics is found significant in reducing downtime thus should be well feedback to design and plant engineers so that it can be incorporated in new offshore system. The downtime models developed based on the steady state analysis and expert input are found to be practical for prediction of the system maintainability performance.

The analysis focuses on the downtime which includes the repair, logistics and administrative delay time. At the operation phase, plant personnel are mostly interested in the availability and downtime performance of the system thus this study provides an excellent example of how the analysis can be done practically and effectively.

Maintainability analysis at operation phase is crucial to assess and predict the maintenance system performance and provide valuable feedback to the design phase and existing plant for further improvement. The methodology developed here is practical hence can assist plant personnel to perform maintainability analysis effectively.

This paper present a generic method of analyzing maintainability at the operation phase. The proposed distribution method via steady state and expert input approaches provides a practical method for formulating downtime distribution model when improvement trend exists.

The purpose of this paper is to study the life cycle cost (LCC) on the operation of power generation. LCC is the total cost of ownership including the cost of the project or asset acquisition, operation and maintenance, and disposal. LCC includes both deterministic costs (such as acquisition costs, improvement costs and disposal costs) and probabilistic (such as the costs of failure, repairs, spare parts, downtime, lost gross margin). Most of the probabilistic costs are associated directly with the reliability and maintenance characteristics of the system.

To be able to analyze failure data using appropriate cost profile in order to represent the fact that each failure has different prices, in different time periods at an economical cycle the new methodology of LCCA Diagram is proposed. Developing criticality ranking of sub‐system, calculating values of Weibull Shape Factor β and Weibull Characteristic Life η for each sub‐system, calculating the time to failure of sub‐system, calculating the mean time to failure of sub‐system using Monte Carlo simulation, determining several alternatives, failure mode and effects analysis and root cause failure analysis are parts of the methodology.

To give a sample of case study, the LCC on the operation of coal fired power plant (CFPP) 330 MW is analyzed. Five alternatives calculation of LCC will be simulated. Graph of cash flow, break‐even graph, and graph of cost/benefit versus risk made for a period of 30 years can be used to asses an effective management program and costs of power plant with a low risk.

The paper suggests that LCC can be used to asses an effective management program and costs of power plant with a low risk.

The purpose of this paper is to discuss operation and maintenance challenges under Arctic conditions and to propose a methodology to assess systems' reliability, maintainability and maintenance costs under the influence of the Arctic operational environment.

A model is suggested for quantifying maintenance costs while taking into account uncertainty due to lack of appropriate data and operational experience using the proportional hazard model and proportional repair model as well as Monte Carlo simulation.

The results show that the operating environment has a considerable influence on the number of failures, the maintenance and repair times and consequently on maintenance cost. Forecasting the maintenance costs based on technical characteristics (e.g. reliability and maintainability) and considering the operational environment, as well as including uncertainty analysis using Monte Carlo simulation, provide more trustworthy information in the decision‐making process.

There are few data and little experience available regarding the operation of offshore oil and gas production systems in the Arctic region. Using the available data collected from similar systems, but in a different operational environment, may result in uncertain or incorrect analysis results. Hence, the method that is used for maintenance cost analysis must be able to quantify the effect of the operating environment on the system reliability and maintainability as well as to quantify the uncertainty.

The paper presents a statistical approach that will be useful in predicting maintenance cost considering the lack of appropriate reliability data from equipment operated in Arctic conditions. The approach presented is valuable for the industrial practitioners in the Arctic region, and may also be adapted to other areas where there is lack of data and operational experience.

Prognostics and health management (PHM) can support product‐service systems (PSS) contracts, especially in the case of high technology products where their condition and performance can be monitored. The purpose of this paper is to investigate how PHM can support effective execution of some PSS contracts and to set out the future research agenda for the development of an online simulation modelling framework that will further harness the interaction between PHM and PSS.

The research methodology commenced by collating facts and figures from the existing body of knowledge, from which a set of key findings is presented from both technical and business perspectives. Analysis of the key findings highlights the current state of PHM‐PSS interaction, the capability of existing tools and techniques and a comprehensive analysis of PSS performances, with and without PHM.

Increased demand for total asset performance from the customers has been the main driver for PSS providers to adopt PHM technology. In the case of high value assets, PHM is used to capture the condition of the assets and to feed this information back to the PSS operations management which, in turn, will be used to plan a maintenance regime, spare parts provision, as well as to mitigate the dynamic behaviour which commonly occurs in PSS. Simulation modelling, driven by asset health condition, shows a considerable potential as an effective tool to control the execution of the PSS contract. In addition to the benefits from the maintenance services, the PHM‐PSS interaction can increase the controllability of the PSS contract execution and allow future modifications to PSS contracts.

The value of this paper lies in the comprehensive analysis of the interaction between PHM and PSS, especially focusing on the interaction during the PSS contract execution. This paper demonstrates the strengths and weaknesses of existing research in the research domain, and highlights the opportunities for future research.

The purpose of this paper is to explore maintenance strategies for manufacturing equipment in Chinese firms.

Data were collected from Chinese companies using a questionnaire administered during face‐to‐face interviews and two established methodologies in strategic management research, exploratory factor analysis and cluster analysis, were used to analyze the data.

The results suggest that despite increasing competitive capabilities of Chinese firms, their maintenance strategies are often restricted to corrective maintenance. Only very few Chinese firms have already implemented predictive maintenance approach, total productive maintenance programs or the strategic outsourcing of maintenance activities.

The research limitations stem from typical issues related to the use of exploratory factor analysis and cluster analysis (for example reliance on the subjective judgment of the researcher or the provision of clusters although no meaningful groups are embedded in the sample).

The findings highlight potential strategies for Chinese firms to improve their maintenance management.

This paper deals with a neglected area of operations management by exploring the maintenance approaches in fast growing Chinese manufacturing industries.

The purpose of this paper is to present a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine.

In total, four years of cost data are collected. Data are analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs.

The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT.

The proposed approach can be used by the decision maker in determining the ERT of production machines which used in mining industry.

The research proposed in this paper provides and develops an optimisation model for ERT of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.

The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.

The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.

The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.

This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.