Organizations are expected to follow trends and adapt their management to market conditions. In an era of rapid change and an environment that is difficult to predict, this is an extremely complicated task. In order to effectively respond to the needs coming from the market, companies implement various methods and adapt available concepts, translating them into the language of practice. Contemporary quality management methods and tools seem to combine the need to skilfully respond to customer expectations and ensure the appropriate level of sustainability issues in the organization’s activities. The main purpose of this article is to analyze the possibilities of using contemporary quality management methods and tools for the sustainable development of the organization. The main method in this article was a literature review that covered the area of the sustainable development of enterprises and the search for good practices in the field of quality management instruments in this area. The other methods are observation and direct interview. Twenty quality methods and tools were selected and divided according to their functions. The research identified benefits and good practices in their use, which can support action for sustainable development.

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.

Theoretical background: The evolution of the instrumentation of quality management makes the list of methods and tools constantly open and inexhaustible. The problems of dividing and classifying quality management instruments are very often discussed in the literature. The need to have a variety of methods and tools is due to the fact that the development of modern competitive strategies, affecting the strengthening of market position, the need for rapid decision-making, as well as progress, mainly in the field of information technology and continuous changes in markets, pose qualitatively new challenges to managers. Purpose of the article: The purpose of this article is to present selected quality management instrumentation by process group. Research methods: In this article, literature review was the starting point for empirical research. Finally, a set of fifty new and classic quality management methods and tools in relation to their main functions was analysed. Based on this, data tables were generated. Main findings: In a large collection of methods, tools and solutions, new concepts appear that are multi-disciplinary and require interdisciplinary knowledge. The choice of appropriate quality instruments is determined by the specificity of the organization and many other variables. The correct use of quality management instruments contributes to solving problems at the management and executive levels of production enterprises, while enriching knowledge about processes.