Search Results (220)

Failure Mode and Effects Analysis (FMEA) is a proactive management technique widely used to improve the reliability of products and processes across various business sectors. Due to rapid changes stemming from uncertain environments, numerous studies have proposed different approaches to enhance the effectiveness of the FMEA method. However, there is a lack of systematic literature reviews and classification of research on this topic. The purpose of this paper is to systematically review the literature on the integration of FMEA with Multi-Attribute Decision-Making (MADM) theories and various mathematical models. This study analyses a total of 68 papers published between 2015 and 2024, selected from 51 peer-reviewed journals indexed in Scopus and/or Web of Science. Furthermore, a bibliometric analysis was conducted based on the frequency of different mathematical theories used to model existing uncertainties, methods for determining the weighting vectors of risk factors (RFs), the use of MADM theories extended with uncertain numbers for weighting RFs and prioritizing identified failure modes, publication years, journals, and application domains. This research aims to support both researchers and practitioners in effectively adopting uncertain MADM methods to address the limitations of traditional FMEA and provide insight into the current state of the art in this field. Full article

The reliability and safety of five-axis CNC abrasive water jet machining are critical for many industries. This study employs Failure Mode and Effects Analysis (FMEA) to identify and mitigate potential failures in this machining system. Traditional FMEA, which relies on crisp numerical values, often struggles with handling uncertainty in risk assessment. To address this limitation, this paper introduces an Interval-Valued Spherical Fuzzy FMEA (IVSF-FMEA) approach, which enhances risk evaluation by incorporating membership, non-membership, and hesitancy degrees. The IVSF-FMEA method leverages the inherent rotational symmetry of interval-valued spherical fuzzy sets and the permutation symmetry among severity, occurrence, and detectability criteria, resulting in a transformation-invariant and unbiased risk assessment framework. Applying IVSF-FMEA to seven periodic failure (PF) modes in five-axis CNC water jet machining achieves a more precise prioritization of risks, leading to improved decision-making and resource allocation. The findings highlight improper fixturing of the workpiece (PF6) as the most critical failure mode, with the highest RPN value of −0.54, followed by mechanical vibrations (PF2) and tool wear and breakage (PF1). This indicates that ensuring proper fixturing stability is essential for maintaining machining accuracy and preventing defects. Comparative analysis with traditional FMEA demonstrates the superiority of the proposed fuzzy-based approach in handling subjective assessments and reducing ambiguity. The findings highlight improper fixturing, mechanical vibrations, and tool wear as the most critical failure modes, necessitating targeted risk mitigation strategies. This research contributes to advancing risk assessment methodologies in complex manufacturing environments. Full article

The failure mode and effect analysis (FMEA) method, which estimates the risk levels of systems or components solely based on the multiplication of simple risk rating indices, faces several limitations. These include the risk of inaccurate risk level judgment and the potential for misjudgments due to human factors, both of which pose significant threats to the safe operation of aircraft. Therefore, a Probabilistic Language based on a cumulative prospect theory (Probabilistic Language, PL) risk assessment strategy was proposed, combining the technique for order preference with similarity to an ideal solution (TOPSIS). The probabilistic language term value and probability value were fused in the method through the cumulative prospect theory, and a new PL measure function was introduced. The comprehensive weights of evaluation strategies were determined by calculating the relevant weights of various indicators through the subjective expert weight and objective entropy weight synthesis. So, a weighted decision matrix was constructed to determine the ranking order close to the ideal scheme. Finally, the risk level of each failure mode was ranked according to its close degree to the ideal situation. Through case validation, the consistency of risk ranking was improved by 23.95% compared to the traditional FMEA method. The rationality of weight allocation was increased by 18.2%. Robustness was also enhanced to some extent. Compared with the traditional FMEA method, the proposed method has better rationality, application, and effectiveness. It can provide technical support for formulating a new generation of airworthiness documents for the risk level assessment of civil aircraft and its subsystem components. Full article

Failure Mode and Effect Analysis (FMEA) is crucial for identifying risk reduction opportunities in design. This study aims to aid in the design of sophisticated medical devices by setting guidelines and addressing weaknesses in data collection and risk priority numbers (RPNs). This is achieved by developing an FMEA framework with potential efficiency and efficacy benefits for design engineers, surgeons and patients. The FMEA framework covered risk analysis and risk evaluation by integrating a cascading Delphi method to address data collection and Multi-Criteria Decision-Making (MCDM) technique to address RPN calculations. This study involved the design of a flexible fetoscope for minimally invasive fetal intervention, analyzing and evaluating risks. The cascading FMEA framework had two stages for data collection, namely risk identification by individual interview and risk evaluation by individual email. The cascading Delphi FMEA framework with MCDM identified the potential risks for the mother at the tip (risk score = 0.927) and subsequent risks such as debris loss (risk score = 0.896), material degradation (risk score = 0.896), and glue dislodging (risk score = 0.896) as critical issues. By identifying failure modes early, medical device designers can better mitigate risks during the initial design stages. Full article

The optimization of business processes is essential for enhancing efficiency and competitiveness in today’s dynamic industrial environment. This study presents a novel approach that integrates Artificial Intelligence (AI) with Failure Mode and Effects Analysis (FMEA) to automate risk assessment and enable predictive maintenance. Using real-world data from a glass manufacturing company (SK), we implemented machine learning models—Random Forest and XGBoost—to predict Risk Priority Number (RPN) values and classify failure risks. The Random Forest Regressor achieved an Coeficient of Determination (R²) of 0.999, Mean Absolute Error (MAE) of 2.18, and Root Mean Square Error (RMSE) of 2.65, indicating high accuracy in risk prediction. The Random Forest Classifier reached a classification accuracy of 82.9%, with strong recall (92.4%) for high-risk cases, making it practical for industrial deployment. The proposed AI-enhanced FMEA system supports dynamic risk management, reduces downtime, and improves maintenance planning. Our findings demonstrate that AI can significantly strengthen traditional FMEA practices and serve as a foundation for intelligent, data-driven decision-making in industrial environments. Full article

With the evolution of data collection technologies, sensor-generated data have become the norm. However, decades of manually recorded maintenance data still hold untapped value. Natural Language Processing (NLP) offers new ways to extract insights from these historical records, especially from short, unstructured maintenance texts often accompanying structured database fields. While NLP has shown promise in this area, technical texts pose unique challenges, particularly in preprocessing and manual annotation. This study proposes a novel methodology combining Failure Mode and Effect Analysis (FMEA), a reliability engineering tool, into the NLP pipeline to enhance Named Entity Recognition (NER) in maintenance records. By leveraging the structured and domain-specific knowledge encapsulated in FMEAs, the annotation process becomes more systematic, reducing the need for exhaustive manual effort. A case study using real-world data from a major electrical utility demonstrates the effectiveness of this approach. The custom NER model, trained using FMEA-informed annotations, achieves high precision, recall, and F1 scores, successfully identifying key reliability elements in maintenance text. The integration of FMEA not only improves data quality but also supports more informed asset management decisions. This research introduces a novel cross-disciplinary framework combining reliability engineering and NLP. It highlights how domain expertise can be used to streamline annotation, improve model accuracy, and unlock actionable insights from legacy maintenance data. Full article

Background/Objectives: Quality by Design (QbD) has revolutionized pharmaceutical development by transitioning from reactive quality testing to proactive, science-driven methodologies. Rooted in ICH Q8–Q11 guidelines, QbD emphasizes defining Critical Quality Attributes (CQAs), establishing design spaces, and integrating risk management to enhance product robustness and regulatory flexibility. This review critically examines QbD’s theoretical frameworks, implementation workflows, and industrial applications, aiming to bridge academic research and commercial practices while addressing emerging challenges in biologics, advanced therapies, and personalized medicine. Methods: The review synthesizes regulatory guidelines, case studies, and multidisciplinary tools, including Design of Experiments (DoE), Failure Mode Effects Analysis (FMEA), Process Analytical Technology (PAT), and multivariate modeling. It evaluates QbD workflows—from Quality Target Product Profile (QTPP) definition to control strategies—and explores advanced technologies like AI-driven predictive modeling, digital twins, and continuous manufacturing. Results: QbD implementation reduces batch failures by 40%, optimizes dissolution profiles, and enhances process robustness through real-time monitoring (PAT) and adaptive control. However, technical barriers, such as nonlinear parameter interactions in complex systems, and regulatory disparities between agencies hinder broader adoption. Conclusions: QbD significantly advances pharmaceutical quality and efficiency, yet requires harmonized regulatory standards, lifecycle validation protocols, and cultural shifts toward interdisciplinary collaboration. Emerging trends, including AI-integrated design space exploration and 3D-printed personalized medicines, promise to address scalability and patient-centric needs. By fostering innovation and compliance, QbD remains pivotal in achieving sustainable, patient-focused drug development. Full article

The topic of hydrogen as an energy vector is widely discussed in the present literature, being one of the crucial technologies aimed at human carbon footprint reduction. There are different hydrogen production methods. In particular, this paper focuses on Steam Methane Reforming (SMR), which requires a source of high-temperature heat (around 900 °C) to trigger the chemical reaction between steam and CH₄. This paper examines a plant in which the reforming heat is supplied through a helium-cooled high-temperature nuclear reactor (HTR). After a review of the recent literature, this paper provides a description of the plant and its main components, with a central focus on the safety and reliability features of the combined nuclear and chemical system. The main aspect emphasized in this paper is the assessment of the hydrogen production reliability, carried out through Failure Modes and Effects Analysis (FMEA) with the aid of simulation software able to determine the quantity and origin of plant stops based on its operational tree. The analysis covers a time span of 20 years, and the results provide a breakdown of all the failures that occurred, together with proposals aimed at improving reliability. Full article

With the acceleration of urbanization process, the number of elevators in China has surged. Concurrently, the prevalence of older elevators has increased, leading to a rise in frequent malfunctions. In recent years, there has been a troubling frequency of elevator accidents resulting in casualties, which has had a negative social impact. The elevator braking system is crucial for ensuring the safe operation of the elevator, and brake failure is a significant contributor to elevator accidents. The failure modes of elevator brakes are complex and diverse, and the failure risk factors are mixed, correlated and unknown. Therefore, this paper is based on the Failure Mode and Effects Analysis (FMEA), focusing on the structural characteristics of the elevator brake to determine the equipment failure risk factors. Based on the accident prevention theory model (24Model) for comprehensive analysis of internal and external causes, this study identifies the comprehensive failure risk factors for elevator brakes. The study employs affiliation function to build the failure risk factor indicator system, the use of the Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) methods to analyze the hierarchical structure and internal relationship between the factors. Based on the research results, the factors contributing to the failure of elevator drum brakes can be identified and the interrelationships among these factors can be systematically elucidated. This analysis can serve as a valuable tool in pinpointing critical areas for routine elevator maintenance and upkeep, with the aim of minimizing the likelihood of drum brake malfunctions. Furthermore, the insights gained can inform the design and implementation of elevator monitoring and management systems, enabling a clearer focus on pertinent factors. Ultimately, this study furnishes a theoretical framework for the prevention and mitigation of such accidents. Full article

The problem addressed in this paper is the identification and management of risks associated with the implementation and operation of green hydrogen in the Spanish port system. The growing demand for clean energy and environmental regulations are driving the adoption of green hydrogen as a viable solution to decarbonize shipping. However, this transition comes with significant challenges, including safety, infrastructure, and hydrogen handling risks. In the existing literature, several authors have used methodologies such as qualitative and quantitative risk analysis, techniques such as FMEA (Failure Modes and Effects Analysis), and the evaluation of impacts and probabilities of occurrence to identify and manage risks in similar projects. These approaches have made it possible to identify potential threats and propose effective mitigation measures. In this work, a combined methodology is proposed that includes the identification of threats, risk assessment through risk matrices, and classification of these risks for their proper management. The SWIFT method (Structured What-If Technique) and the use of impact-probability matrices are applied. The main conclusion of the work is that, although green hydrogen has great potential for the decarbonization of the port sector, its implementation requires careful management of the risks identified. The proposed mitigation measures are essential to ensure the safety and viability of green hydrogen projects in Spanish ports. Full article

The increasing demand for sustainable business practices has highlighted the need for a robust and risk-aware green supplier selection framework. Traditional supplier evaluation methods primarily focus on cost, quality, and delivery performance but often fail to incorporate sustainability and risk factors effectively. This study proposes an improved AHP-FMEA method that integrates the Analytic Hierarchy Process (AHP), entropy weight method, and Failure Mode and Effects Analysis (FMEA) to enhance decision-making in green supplier selection. The AHP method determines subjective criteria weights, while the entropy method adjusts these weights objectively to reduce bias. The FMEA approach incorporates risk assessment by identifying and quantifying potential supplier failures, ensuring a more comprehensive evaluation. A case study is conducted to validate the proposed model, comparing it with classical AHP and AHP-Entropy methods. The results show that incorporating risk factors significantly influences supplier ranking, demonstrating the model’s ability to provide a more scientific, objective, and risk-conscious evaluation. The proposed approach enhances the accuracy and reliability of green supplier selection, making it a valuable tool for sustainable supply chain management. Full article

This study proposes a Bayesian failure mode and effects analysis (FMEA)-based method for identifying critical faults and guiding maintenance decisions in stacker-automated stereoscopic warehouses, addressing the limited research on whole-machine systems and the interactions among fault modes. First, the hesitant fuzzy evaluation method was utilized to assess the influences of risk factors and fault modes in a stacker-automated stereoscopic warehouse. A hesitant fuzzy design structure matrix (DSM) was then constructed to quantify their interaction strengths. Second, leveraging the interaction strengths and causal relationships between severity, detection, risk factors, and fault modes, a Bayesian network model was developed to compute the probabilities of fault modes under varying severity and detection levels. FMEA was subsequently applied to evaluate fault risks based on severity and detection scores. Following this, fault risk ranking was conducted to identify critical fault modes and formulate targeted maintenance strategies. The proposed method was validated through a case study of Company A’s stacker-automated stereoscopic warehouse. The results demonstrate that the proposed approach can more objectively identify critical fault modes and develop more precise maintenance strategies. Furthermore, the Bayesian FMEA method provides a more objective and accurate reflection of fault risk rankings. Full article

Industrial symbiosis (IS) fosters collaboration between industries to exchange materials, energy, water, and by-products. It contributes to environmental and economic sustainability by reducing resource consumption, decreasing greenhouse gas emissions, and generating economic benefits. However, managing risks in these exchanges presents challenges, particularly as materials like waste and by-products fall outside traditional supply chain practices. This paper introduces the Industrial Collaborative Risk Management (ICRM) Methodology, an extended Failure Mode and Effect Analysis (FMEA) approach specifically designed for collaborative industrial ecosystems. The ICRM methodology provides a systematic approach to identifying, assessing, prioritizing risks, and implementing corrective actions, enabling the reliable implementation of IS. By effectively managing risks, this methodology minimizes disruptions in material and energy exchanges, strengthens the resilience of industrial ecosystems, and enhances their environmental ambitions. The methodology supports cross-sectoral communication, facilitates knowledge exchange, and promotes trust among stakeholders. A real IS case study demonstrates the ICRM methodology’s ability to document interrelations, standardize risk evaluation, and propose mitigation strategies. This work provides IS facilitators with a practical tool for effective risk management in complex industrial environments and lays the foundation for future applications in diverse ecosystems. Full article

User experience (UX) is crucial for interactive system design. To improve UX, one method is to identify failure modes related to UX and then take action on the high-priority failure modes to decrease their negative impacts. For the UX of interactive system design, the failure modes under consideration are human errors or difficulties, and thus the risk factors concerning failure modes are subjective and even subconscious. Existing methods are not sufficient to deal with these issues. In this paper, a fuzzy failure mode and effect analysis (FMEA)-based hybrid approach is proposed to improve the UX of interactive system design. First, hierarchical task analysis (HTA) and systematic human error reduction and prediction approach (SHERPA) are combined to identify potential failure modes concerning UX. Subsequently, fuzzy linguistic variables are employed to assess the risk parameters of the failure modes, and the similarity aggregation method (SAM) is adopted to aggregate the fuzzy opinions. Then, on the basis of the aggregation results, fuzzy logic is adopted to compute the fuzzy risk priority numbers that can prioritize the failure modes. Finally, the failure modes with high priorities are considered for corrective actions. An in-vehicle information system was employed as a case study to illustrate the proposed approach. The findings indicate that, compared with other methods, our approach can provide more accurate results for prioritizing failure modes related to UX, and can successfully deal with the subjective and even subconscious nature of the risk factors associated with failure modes. This approach can be universally utilized to enhance the UX of interactive system design. Full article

Causal domain knowledge is commonly documented using natural language either in unstructured or semi-structured forms. This study aims to increase the usability of causal domain knowledge in industrial documents by transforming the information into a more structured format. The paper presents our work on developing automated methods for causal information extraction from real-world industrial documents in the semiconductor manufacturing industry, including presentation slides and FMEA (Failure Mode and Effects Analysis) documents. Specifically, we evaluate two types of causal information extraction methods: single-stage sequence tagging (SST) and multi-stage sequence tagging (MST). The presented case study showcases that the proposed MST methods for extracting causal information from industrial documents are suitable for practical applications, especially for semi-structured documents such as FMEAs, with a 93% F1 score. Additionally, the study shows that extracting causal information from presentation slides is more challenging. The study highlights the importance of choosing a language model that is more aligned with the domain and in-domain pre-training. Full article