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The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.

This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.

The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.

This study only adopts simulated data to verify the proposed method. Future research may use other real products' failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.

The purpose of this paper is to propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a new method for predicting the reliability of repairable systems. The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Findings – The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer, the learning rate and momentum of neural network architecture. Research limitations/implications – This study only adopts real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems. Practical implications – Based on the more accurate analytical results achieved by the proposed method, engineers or management authorities can take follow‐up actions to ensure that products meet quality requirements, provide logistical support and correct product design. Originality/value – The proposed method is superior to other prediction techniques in predicting the reliability of repairable systems.

Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems.

This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed.

The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems.

Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.

The vast majority of evidence about knowledge management (KM) is based on studies within a mass‐producing context and at intra‐organizational level. Little has been reported in the complex products and systems (CoPS) literature about KM processes across firm boundaries. To contribute to this gap, this paper reviews the literature of CoPS, inter‐organizational collaboration and KM, discusses some challenges of inter‐organization KM faced by CoPS manufacturers, and explores a research framework which is helpful to provide a preliminary understanding of inter‐organizational KM within a CoPS context.

Literature review and research experience are presented.

The study has identified some challenges faced by CoPS manufacturers when they try to manage knowledge from an innovative network, and developed a framework extending KM processes into an inter‐firm collaborative network for CoPS development. Both, project level collaboration and firm level linkages need to be taken into consideration when attempting to implement KM at inter‐organizational level. Knowledge acquisition from network, knowledge integration, and knowledge sharing in the network are the main inter‐organizational KM activities in a CoPS context.

As KM based on inter‐organizational collaboration has received little attention in prior studies, especially in CoPS conditions, this study is an exploratory discussion of KM issues within a CoPS context, combining the cross‐school theories of inter‐organizational linkages, KM and CoPS innovation. This paper tries to account for knowledge‐related activities involved in CoPS development, identifying some challenges in CoPS innovation.

China's research and development (R&D) internationalization has grown rapidly in the 1990s. However, academic research has not kept pace with it. The purpose of this paper is to study the development characteristics and evolution pattern of China's R&D internationalization, explore its challenges to China's traditional science and technology (S&T) system, and to give some suggestions for the reform of China's S&T system as well.

An in‐depth case study about Huawei is conducted by focusing on its strategy and development pattern of overseas R&D activities. It involves two methods of data collection: comprehensive interviews and administrative records and documents of the company. More information about China's R&D internationalization and S&T system is obtained through literature review, policy documents collection and a previous questionnaire survey of 28 leading Chinese firms.

Based on the literature review, previous survey and study of the strategy and development of R&D internationalization of Huawei, a three‐stage model is proposed to explain the evolution of R&D internationalization in China. The challenges to the traditional S&T system in China are summarized. In the future, R&D internationalization should be considered as an important factor in the reform of China's S&T system. Some suggestions are given as well.

This paper puts forward a three‐stage model to explain the evolution of R&D internationalization in China, which may add both academic and practical value to the field of internationalization of R&D. It brings some valuable implications to the reform of China's S&T system as well. This paper analyzes the challenges and opportunities brought by R&D internationalization to China's S&T system on the basis of case study and policy documents collection. The suggestions of the reform of China's S&T system are presented.

COVID-19’s effects go beyond physical health, including impacts to behavioral health such as documented increases in loneliness, depression, anxiety, and alcohol misuse. Research on other disaster and mass trauma events suggests that behavioral health impacts may persist for many years after the initial onset of the event and could be compounded with other disasters. These impacts have not, and will not, be distributed evenly across the population. Of note, evidence from early in the pandemic suggests that older adults’ (adults aged 65 and older) behavioral health may not be as adversely affected as expected, given past research on age and disasters.

The purpose of this paper is to present the results of the application of various models to estimate the reliability in railway repairable systems.

The methodology proposed by the International Electrotechnical Commission (IEC), using homogeneous Poisson process (HPP) and non-homogeneous Poisson process (NHPP) models, is adopted. Additionally, renewal process (RP) models, not covered by the IEC, are used, with a complementary analysis to characterize the failure intensity thereby obtained.

The findings show the impact of the recurrent failures in the times between failures (TBF) for rejection of the HPP and NHPP models. For systems not exhibiting a trend, RP models are presented, with TBF described by three-parameter lognormal or generalized logistic distributions, together with a methodology for generating clusters.

For those systems that do not exhibit a trend, TBF is assumed to be independent and identically distributed (i.i.d.), and therefore, RP models of “perfect repair” have to be used.

Maintenance managers must refocus their efforts to study the reliability of individual repairable systems and their recurrent failures, instead of collections, in order to customize maintenance to the needs of each system.

The stochastic process models were applied for the first time to electric traction systems in 23 trains and to 40 escalators with ten years of operating data in a railway company. A practical application of the IEC models is presented for the first time.

Based on the perspective of complexity theory, the operation process of property insurance companies can be regarded as a complex dynamic nonlinear chaotic system. This paper aims to measure the operating efficiency of 29 Chinese domestic property and casualty (P&C) companies and 18 foreign-invested P&C companies from 2011 to 2017 and outline the path to achieving high-quality development.

The data were obtained from the Chinese Insurance Yearbook and China Statistical Yearbook 2012–2018. The data envelopment analysis method was used to calculate the technical efficiency of property insurance companies and fuzzy set qualitative comparative analysis is used for configuration analysis of determinants affecting technical efficiency.

This paper founds the average technical efficiency of Chinese domestic P&C insurance companies was 0.914 and that of foreign-invested P&C insurance companies was 0.895. The average total factor productivity of Chinese domestic P&C insurance companies was 1.058 and that of foreign-invested P&C insurance companies was 1.051. There were three modes to improve the company’s technical efficiency, with high loss ratio and low reinsurance ratio, poor employee education and higher leverage ratio and high leverage ratio and low reinsurance ratio as the core conditions.

This study puts forward four applicable, targeted and proven ways to improve the technical efficiency of China’s P&C insurance industry. These configurations were verified by the cases of existing property insurance companies, which can provide practical references for the insurance industry.