Supply chain risk assessment approach for process quality risks

Purpose- The purpose of the paper is to proactively analyse and mitigate root causes of the process quality risks. The case study approach examines the effectiveness of the fuzzy logic approach for assessing the product and process related failure modes within global supply chain context. Design/Methodology/approach- The case study of a printed circuit board company in China is used as a platform for conducting the research. Using data triangulation, the data is collected and analysed through interviews, questionnaires, expert opinions and quantitative modelling for drawing useful insights. Findings- The fuzzy logic approach to FMEA provides a structured approach for understanding complex behaviour of failure modes and their associated risks for products and processes. Supply Chain Managers should conduct robust risk assessment during the design stage to avoid product safety and security risks. Research Limitations/implications- The research is based on a single case study. Multiple cases from different industry sectors may support in generalising the findings. Originality/Value- The study attempts to mitigate the root causes of product and processes using fuzzy approach to FMEA in supply chain network

A DMAIC integrated fuzzy FMEA model: A case study in the automotive industry

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.publishersversionpublishe

A Fuzzy-FMEA Risk Assessment Approach for Offshore Wind Turbines

Failure Mode and Effects Analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for risk and reliability analysis. However, several limitations are associated with its implementation in offshore wind farms: (i) the failure data gathered from SCADA system is often missing or unreliable, and hence, the assessment information of the three risk factors (i.e., severity, occurrence, and fault detection) are mainly based on experts’ knowledge; (ii) it is rather difficult for experts to precisely evaluate the risk factors; (iii) the relative importance among the risk factors is not taken into consideration, and hence, the results may not necessarily represent the true risk priorities; and etc. To overcome these drawbacks and improve the effectiveness of the traditional FMEA, we develop a fuzzy-FMEA approach for risk and failure mode analysis in offshore wind turbine systems. The information obtained from the experts is expressed using fuzzy linguistics terms, and a grey theory analysis is proposed to incorporate the relative importance of the risk factors into the determination of risk priority of failure modes. The proposed approach is applied to an offshore wind turbine system with sixteen mechanical, electrical and auxiliary assemblies, and the results are compared with the traditional FMEA

Modified Fuzzy FMEA Application in the Reduction of Defective Poultry Products

Failure mode and effects analysis (FMEA) consists of the famous qualitative management methods used for improvements in management processes. This paper aims to determine the factors of defective products in the processing of poultry products in the industry. The causes of problems have been analyzed by systematic brainstorming of specialist consensus in the evaluation of problems to achieve unanimity on the violence level. The FMEA method uses the risk priority number (RPN), which indicates the priorities of risk problems and can evaluate three components: severity, occurrence and detection. Sometimes, this risk assessment leads to the wrong priorities. Therefore, we propose fuzzy FMEA methods for priority ranking of RPN and efficiently reducing poultry product defects, which are established based on fuzzy systems followed by comparison with conventional FMEA. The results indicate that the fuzzy FMEA method can efficiently and feasibly reduce poultry product defects

Risk Evaluation: Brief Review and Innovation Model Based on Fuzzy Logic and MCDM

The risk assessment of engineering systems represents an important part of the quality of service and dependability. The existing methods for risk evaluation use crisp sets for rating partial indicators' proposition and their cumulative products as an overall indicator. In this paper, existing FMEA and FMECA methods have been improved using the fuzzy expert system for calculating the risk priority number. The application of fuzzy logic allows the use of linguistic descriptions for risk analysis. In this way, the state of the system in terms of risks and consequences is better described. The settings of the fuzzy systems are based on the application of two multi-criteria decision-making methods. The AHP method was used to define the mutual relationship of the impact of partial indicators (occurrence, severity, and detectability) on risk. In this way, subjectivity in risk assessment is reduced. In the composition of the fuzzy model, the TOPSIS method is introduced to reduce the dissipation of results, which contributes to the accuracy of the outcome. This contributes to the accuracy of the results. The results were verified through a case study of a complex engineering system-bucket-wheel excavators. The risk was observed from the aspect of the danger of damage and the danger of downtime. The initial information for weak points of ES is defined according to historical damage events and statistics of downtime. Expert knowledge was used for weak points grading in the model. Additional model verification was performed using similar methods, using the same input data. The innovative model, presented in the paper, shows that it is possible to correct different weights of risk indicators. The obtained results show less dispersion compared with other existing methods. Weak points with increased risk have been located, and an algorithm has been proposed for risk-based maintenance application and implementation

Fuzzy FMECA Process Analysis for Managing the Risks in the Lifecycle of a CBCT Scanner

The Failure Mode, Effects, and Criticality Analysis (FMECA) is one of the risk analysis techniques proposed by the ISO 14971 Standard. This analysis allows to identify and assess the consequences of faults that affect each component of a complex system. The FMECA is a forward-type technique used for highlighting critical points and classifying them by priority. It also makes it possible to evaluate the extent of failures by means of numerical indices. It can be applied to a product or to a work process. In the latter case we talk about Process-FMECA. The application of the Process-FMECA to bioengineering is of particular interest because this procedure provides an analysis related to risk management during all the different phases of the medical device life cycle. However, practical applications of this method have revealed some shortcomings that can lead to inaccuracies and inconsistencies regarding the risk analysis and consequent risk prioritization. This paper presents an example of application of a Fuzzy Process-FMECA, an improved Process-FMECA based on fuzzy logic, to a small computerized tomography (CT) device prototype designed for studying the extremities of the human body. This prototype is a CT device that uses the Cone Beam CT (CBCT) technology. The Fuzzy Process-FMECA analysis has made it possible to produce a table of risks, that are quantified according to the specifications of the method. The analysis has shown that each phase or activity is fundamental to guarantee a correct functioning of the device. The methodology applied to this specific device can be paradigmatic for analyzing the process risks for any other medical device

A review of applications of fuzzy sets to safety and reliability engineering

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering