The proposed method supports the determination of severity (S), occurrence (O), and detection (D) indices of Failure Modes and Effects Analysis (FMEA). Previously evaluated and previously not studied risks are compared in pairwise comparison. The analysis of the resulted pairwise comparison matrix provides information about the consistency of the risk evaluations and allows the estimation of the indices of the previously not evaluated risks. The advantages of the method include: The pairwise comparison facilities the identification of risks that are otherwise difficult to evaluate The inconsistency of existing FMEA studies can be highlighted and systematically reduced The method can be generalized about a wide range of grading problems

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Understanding customers' needs and developing a product which meets expectations is a multi-criteria decision problem, and requires methods for solving complex tasks. The purpose of the present study is to apply network science for prioritization of customers' needs and extend the applicability of the Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD) methods. These two methods can be used jointly in the customers' needs prioritization. The customer's needs are prioritized by using the AHP technique, and the technical requirements are ranked by using the QFD method. Although network analysis is a widely used method in several disciplines, it is not yet widespread in customer needs assessment. This study aims to fill this gap, replace and combine the AHP and QFD methods by one to facilitate the application. Preference and correlation networks are presented and evaluated in detail. By using a preference network, the importance of the customer needs is definable by calculating an in-degree or a PageRank value. In a correlation network, the effects of the technical requirements on each other can be evaluated by specifying their weights and directions.

This article introduces a newly elaborated monitoring method for projects and processes involving repetitive activities. The FAR model structures monitoring indicators according to three perspectives: 1) Focus dimension -describes if indicator is for inputs (potential), activities (efficiency) or outputs (effectiveness) 2) Attribute dimensiondescribes if indicator reflects quality, timing or financial characteristics of units measured; 3) Role dimension -describes if actual value of indicator is measured (measurement), calculated (differentiation) or estimated (prediction). The FAR model can be considered as a special combination of tools and principles of Balanced Scorecard, Earned ValueManagement and Six Sigma Business Process Management System methodologies. In this work, we present our model and how it can be applied in an operation development project. We found that the FAR model is able to alert management about events with negative effects and give the chance to implement corrections in time.

This study introduces the NTS network as a new way of analysing, verifying and improving quality-related risk assessment. NTS is based on a network science approach that models complex systems by graphs. By using N, T and S-Graphs as the elements of NTS, risk events that play special role in the risk management system can be identified. Based on their characteristics, the strength of their potential causal connections can be recalculated, providing more precise predictions of the occurrence frequencies of events.