This paper presents the application of the 8D method to a case study in the automotive industry, combined with the development of a pilot framework that integrates Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), and advanced IT tools. Unlike traditional 8D implementations, the proposed solution introduces several innovations: (i) AI-based complaint analysis with automated keyword detection and classification and localization of batches in the warehouses achieving a 100% accuracy rate in categorizing root causes; (ii) IoT-enabled non-invasive monitoring of production parameters for tests and logs, reducing fault rate to 0 for current case study; (iii) digital cataloging and storage of corrective and preventive actions, improving retrieval speed of past solutions by 300 times (from 300 min to less than 1 min); and (iv) automated reporting tools, cutting the report preparation time from hours to minutes. Through these contributions, the system enhances both responsiveness and prevention, leading to an observed 100% reduction in fault recurrence associated with current case study and an estimated 75% decrease in customer complaints regarding defects occurring throughout the production sector where the pilot was implemented. In addition to the practical improvements, this work extends the literature on digitalized quality management by demonstrating how the 8D methodology can be effectively re-engineered with Industry 4.0 components. Comparative analysis against the enterprise’s conventional 8D system and against recent alternative approaches confirms that our framework provides measurable benefits in the speed, accuracy, and sustainability of problem-solving processes.

The main purpose of the article is to demonstrate how large language models (LLMs) can enhance and automate the Failure Modes and Effects Analysis (FMEA) method to improve the identification, assessment, and management of operational risk in modern technological systems. The study aims to show that integrating AI into FMEA increases the efficiency, precision, and reliability of detecting potential failures and evaluating their consequences, provided that expert supervision and model transparency are maintained. The research combines a literature review with a case study using OpenAI’s model to generate an automated FMEA for a manufacturing process. The methodology defines process components, identifies potential failure modes, and evaluates their risk impact. Five specialized libraries—structure, function, failure, risk, and optimization—serve as structured inputs for the LLM. A feedback mechanism allows the system to learn from previous analyses, improving future risk assessments and supporting continuous process optimization. The developed platform enables engineers to initiate projects, input data, generate and validate AI-based FMEA reports, and export results. Overall, the study demonstrates that the integration of LLMs into FMEA can transform operational risk management, making it more intelligent, adaptive, and data-driven.

Laser cutting is a computer numerically controlled process widely used in the automotive industry for cutting metal materials needed to manufacture parts used in automobile production. The production process represents all the conscious actions of a company's employees carried out with the help of various machines, equipment, or installations on raw materials, materials, or other components to transform them into products or services with a certain market value. The Ishikawa diagram, also known as the cause-and-effect diagram, is often used in industrial process analysis, defect prevention, and product design processes. The paper presents the results of the research conducted on the use of the Ishikawa diagram in analyzing the causes that lead to negative effects in the laser cutting process of S235JRH structural steel pipes. After defining the problem, by applying the "WHO, WHAT, WHERE, WHEN, HOW MUCH, HOW, WHY" method, all possible causes were identified using the "5 Whys" method. The analysis identified 11 probable causes, and through further analysis, it was concluded that 3 of them were already present in the process, influencing the laser cutting of the pipes and generating quality problems. All the identified causes built the Ishikawa diagram consisting of 7Ms.

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.

Failure Mode and Effects Analysis (FMEA) is a method commonly used in activities with the main objective of finding measures to improve manufacturing processes or products, by identifying actual and potential ways in which components fail or processes malfunction. The paper presents the practical application of the FMEA method for the process of obtaining an important component of the resistance system of a motor vehicle, namely a chassis. Some general information is presented on the importance of the FMEA method, the product analyzed, followed by the practical application of the method, starting from the determination of the probable causes of failure and their effects on: the organization, the customer and the end user. The processes carried out to obtain the chassis are identified and for a category of processes the Severity (S), Occurrence (O), Detection (D) and Risk Priority Number (RPN) indicators are calculated. Also, the preventive control methods and the control methods to be applied to reduce the occurrence of the defect are identified and indicated.