Search Results (9)

The paper presents the Failure Mode and Effects Analysis (FMEA) method applied to a process-based case study, together with an approach for implementing the AIAG & VDA harmonized FMEA standard by using modern digital tools. While classical FMEA is widely used in the industry, risk assessment based on the Risk Priority Number (RPN) often leads to the inconsistent ranking of failures and unclear prioritization of corrective actions. This paper explores the shift from the traditional Risk Priority Number (RPN) approach to the Action Priority (AP) concept introduced in the AIAG & VDA FMEA Handbook and explains why this change leads to clearer, more consistent risk-based decisions. Rather than focusing only on the methodological differences, the paper also outlines a practical framework for full implementation, showing how Industry 4.0 technologies can strengthen traceability, improve response time, and ensure greater consistency in PFMEA development. It also examines how Artificial Intelligence (AI) and Large Language Models (LLMs) can support engineers in everyday practice—for example, by helping identify potential failure modes, standardizing documentation, and guiding the definition of prevention and detection controls. In parallel, IoT-based monitoring and real-time data collection can provide valuable feedback to validate occurrence and detection ratings. Over time, this data-driven feedback loop can improve the accuracy and reliability of risk assessments. The proposed framework contributes to improved responsiveness in process optimization activities, reduces the probability of recurring failures, and supports continuous quality improvement in manufacturing organizations. The solution is discussed in relation to classical FMEA practices and recent trends in the digital transformation of quality management systems. Full article

In the automotive industry, the implementation of Process Failure Mode and Effect Analysis (PFMEA) is conducted by a PFMEA team comprising employees who are connected to the production process or a specific product. Core PFMEA team members are actively engaged in PFMEA execution through meetings, analysis, and the implementation of corrective actions. Although the current handbook provides guidelines on the potential composition of the PFMEA team, it does not strictly define its members, allowing companies the flexibility to determine the team structure independently. This study aims to identify the core PFMEA team members by adhering to criteria based on the recommended knowledge and competencies outlined in the current handbook. By applying the RAnking based on the Distances and Range (RADAR) approach, extended with Interval-Valued Pythagorean Fuzzy Numbers (IVPFNs), a ranking of potential candidates was conducted. A case study was performed in a Tier-1 supplier company within the automotive supply chain. Full article

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability. Full article

This study introduces an integrated method for managing process risks in a business process reengineering (BPR) project using robust data envelopment analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA (severity, occurrence and detection (S-O-D)) and incorporating two additional factors (breakdown cost and breakdown duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine-learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an automotive sector company, and the results showed the impact of uncertainty on values by comparing different approaches, such as RPN, PFMEA-DEA and PFMEA-RDEA. A new reduced risk categorization was achieved, which allowed for decision makers to focus on the necessary actions for reengineering. Full article

Cylindrical lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage applications. However, safety risks due to thermal runaway-induced fire and explosions have prompted the need for safety analysis methodologies. Though cylindrical batteries often incorporate safety devices, the safety of the battery also depends on its design and manufacturing processes. This study conducts a design and process failure mode and effect analysis (DFMEA and PFMEA) for the design and manufacturing of cylindrical lithium-ion batteries, with a focus on battery safety. Full article

The aim of the paper is to examine the requirements of producers in post-communist countries with lower economic level. The first requirement was how to overcome the limitations of conventional PFMEA to propose measures effectively. The second requirement solved the economic effect of failure modes. The aim of the paper was to create a new hybrid PFMEA–DEMATEL–ERPN model to manage failure modes to resolve the requirements. The DEMATEL model overcame the limitations of PFMEA. DEMATEL data were used to estimate the functionality of the proposed models. Criteria such as the occurrence of defective products and the probability of their occurrence (O and RPN) were monitored. ERPN also overcame the limitations of PFMEA. Internal and external costs arise as effects of failure modes. The costs were included in the economic evaluation of the models. We validated the models in a transfer pressing process. The estimation of models’ functionality proved to be correct. The economic evaluation refined the research results and resolved the second requirement of the manufacturers. The DEMATEL and ERPN models (compared to PFMEA) proved their validity when the use of PFMEA was limited. By using DEMATEL, we registered the lowest number of defective products and the lowest costs. Full article

Failure mode and effects analysis (FMEA) is the most commonly used risk evaluation tool in industry and academia. After four revisions, the US Automotive Industry Action Groups (AIAG) and German Association of the Automotive Industry (VDA) issued the latest FMEA manual, called AIAG and VDA FMEA Handbook Edition 1, in June 2019. Risk priority number (RPN) in the old-edition FMEA is replaced with action priority (AP), where the numerical evaluation of severity (S), occurrence (O), and detection (D) are referred to in the AP form for judging high (H), medium (M), and low (L) priority in order to ensure appropriate actions for improving prevention or detection control. When evaluating design (D) or process (P) in FMEA, the FMEA team has to refer to the evaluation criteria for S, O, and D, so as to reduce the difference in the evaluation reference and fairness. Since the criteria evaluation form is the qualitative rating standard with semantic judgment, evaluation errors are likely to occur when the team judges S, O, and D. The FMEA cases in this study are preceded by the confidence level (CL) of the S, O, and D evaluation standards and the setting of a confidence interval (CI) for the actual evaluation events. With discrete nonuniform distribution as the simulation setting, Monte Carlo simulation is applied several times to evaluate the probability before and after the evaluation, which is compared with the AP form to confirm the probability values of high, medium, and low priority. It provides reference for the FMEA cross-functional team, improving the originally non-AP events. Finally, the AP calculated in the simulation is compared and analyzed with the RPN sequence to verify the judgment of better actions with AP. Full article

The purpose of this paper is to propose a novel process failure mode and effect analysis (PFMEA) approach for the reliable design of assembly activities to prevent product defects due to errors during the assembly of complex products. PFMEA is approached as an integrated method that, in addition to implementing recommended actions, supports the design of worksheets, equipment, and layout of the assembly lines of complex systems, early in the design phase of the product. As a result, the innovative design-job element sheets (D-JESs), which report work instructions to the operator for assembly cycles, are defined before the design of the production and assembly process. The modification of the PFMEA structure, the implementation of proper recommended actions, and the designs of D-JESs, equipment, and assembly layout, early in the design phase of the product, are the novel contributions of the paper. The integrated method assures to effectively design the assembly process directly during the product design to avoid errors that could promote dissatisfaction of the end-users. It is practical to use and does not require large investments, implementation of new technologies, or complex additional training. Its practical application is demonstrated using a case study concerning a manufacturer of train wagons via manual assembly lines. Full article

Fault diagnosis presents a considerable difficulty to human operators in supervisory control of manufacturing systems. Implementing Internet of Things (IoT) technologies in existing manufacturing facilities implies an investment, since it requires upgrading them with sensors, connectivity capabilities, and IoT software platforms. Aligned with the technological vision of Industry 4.0 and based on currently existing information databases in the industry, this work proposes a lower-investment alternative solution for fault diagnosis and problem solving. This paper presents the details of the information and communication models of an application prototype oriented to production. It aims at assisting shop-floor actors during a Manufacturing Problem Solving (MPS) process. It captures and shares knowledge, taking existing Process Failure Mode and Effect Analysis (PFMEA) documents as an initial source of information related to potential manufacturing problems. It uses a Product Lifecycle Management (PLM) system as source of manufacturing context information related to the problems under investigation and integrates Case-Based Reasoning (CBR) technology to provide information about similar manufacturing problems. Full article