Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.

Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes and services. However, the conventional FMEA method has been criticized for some inherent drawbacks which limit its effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members' weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison with some existing methods.