Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members’ weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.

Failure mode and effect analysis (FMEA) technique has been extensively used as a powerful tool for identifying and assessing potential failures in different phases of the product life cycle. However, the conventional FMEA has been criticised much for its deficiencies in measurement scale, computation of risk priority numbers (RPN), risk factors’ weights, etc. In this work, the authors propose a more reasonable failure evaluation structure using fuzzy weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). To fully reflect the importance of risk factors severity (S), occurrence (O) and detection (D), the authors consider integrating both subjective weights and objective weights, which is not presented in literature before, avoid failure modes from being underestimated or overestimated. For ranking fuzzy TOPSIS is adopted to get the closeness coefficient for each failure mode. Then, all failure modes can be ranked according to the closeness coefficients. A case of nuclear reheat valve system is provided to illustrate the applications and benefits of proposed FMEA method.

This research mainly focuses on the customer requirements’ evaluation in the early development of industrial product-service system (IPS²). IPS² can maintain and enhance manufacturer’s competitiveness by integrating tangible product and intangible service. Accurately capturing and understanding customer requirement are critical for successfully developing the right IPS² for the right customer. However, there are few researches on requirement evaluation in the early planning phase of IPS² development. And IPS² requirement evaluation often involves much subjectivity and vagueness. To solve these problems, a systematic evaluation approach for eliciting and assessing IPS² requirement under vagueness is proposed. The proposed model of industrial customer activity cycle can efficiently support requirement elicitation from the lifecycle perspective. Besides, the integrated rough group analytic hierarchy process method can effectively deal with subjectivity and vagueness in the IPS² requirement evaluation process. To demonstrate the potential of the approach, an application in an air compressors system is also illustrated.