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The purpose of this paper is to develop a new failure mode and effect analysis (FMEA) framework for evaluation, prioritization and improvement of failure modes.

A hybrid multiple criteria decision-making method combining VIKOR, decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (AHP) is used to rank the risk of the failure modes identified in FMEA. The modified VIKOR method is employed to determine the effects of failure modes on together. Then the DEMATEL technique is used to construct the influential relation map among the failure modes and causes of failures. Finally, the AHP approach based on the DEMATEL is utilized to obtain the influential weights and give the prioritization levels for the failure modes.

A case study of diesel engine’s turbocharger system is provided to illustrate the potential application and benefits of the proposed FMEA approach. Results show that the new risk priority model can be effective in helping analysts find the high risky failure modes and create suitable maintenance strategies.

The proposed FMEA can overcome the shortcomings and improve the effectiveness of the traditional FMEA. Particularly, the dependence and interactions between different failure modes and effects have been addressed by the new failure analysis method.

This paper presents a systemic analytical model for FMEA. It is able to capture the complex interrelationships among various failure modes and effects and provide guidance to analysts by setting the suitable maintenance strategies to improve the safety and reliability of complex systems.

Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to propose a joint model that integrates PM and buffer sizing with consideration of quality loss for a degenerating system, which aims to minimize the average operation cost for a finite horizon. The opportunistic maintenance (OM) policy which could increase the output and decrease the cost of the system is also explored.

A joint PM and buffer size model considering quality loss is proposed. In this model, the time-based PM and the condition-based PM are taken on the upstream and the downstream machine, respectively. Further, the OM policy based on the theory of constraints (TOC) is also considered. An iterative search algorithm with Monte Carlo is developed to solve the non-linear model. A case study is conducted to illustrate the performance of the proposed PM policies.

The superiority of the proposed integrated policies compared with the separate PM policy is demonstrated. Effects of the policies are testified. The advantages of the proposed TOC-based OM policy is highlighted in terms of low-cost and high-output.

Few studies have been carried out to integrate decisions on PM and buffer size when taking the quality loss into consideration for degenerating systems. Most PM models treat machines equally ignoring the various roles of them. A more comprehensive and integrated model based on TOC is proposed, accompanied by an iterative search algorithm with Monte Carlo for solving it. An OM policy to further improve the performance of system is also presented.

Labour relations management, business management, HRM, focusing on the labour relations of Chinese enterprises.

This case is designed for students in schools of business or management, undergraduate MBA or executive MBA classes. Students should already have a basic knowledge about Chinese labour relations, HRM, and organizational development.

In 2004, a deal transformed Anhui Xuanjiu Group from a state-owned enterprise (SOE) to a private company. Li Jian, the Chairman of Xuanjiu Group, focused on creating happiness for employees. Thanks to Li Jian's efforts, Xuanjiu emrged from its crisis which was formed in the planned economy system. After several years of development, the labour relations management of Anhui Xuanjiu Group became a model among private enterprises in China.

Students can gain new insights into labour relations in China. The case provides an example of building friendly labour relations to avoid labour disputes. It provides a set of measures for retaining and motivating workers.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines.

By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset.

By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped.

Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.