Decision support in the field of quality assurance using digital data analysis

Abstract

The article describes approaches to the implementation of the situational-ontological methodology for developing decision support systems in the field of quality assurance using tools for digital analysis and data processing. The research was conducted to confirm the possibility of implementing the methodology using popular tools for digital data analysis. The relevance of the research is associated with high requirements for personnel in the field of quality assurance in terms of the breadth of knowledge about internal business processes, regulatory requirements for them, various instrumental and heuristic methods, which requires the adoption of a large number of heterogeneous decisions that require a large amount of diverse knowledge. In such conditions, a decision support system is a good way to make a decision. The scientific innovation of the proposed solution is connected both with the novelty of the most used situational-ontological methodology for developing decision support systems, and with the novelty of using the combination of the well-known decision tree method and the Knime Analytics Platform software package when forming a decision support system in the field of quality assurance. In the process of the research, it was possible to demonstrate the fundamental feasibility of the situational-ontological methodology for developing decision support systems in the field of quality assurance. Also a model for digital processing of data on a defect was built. The model provides the formation of an unambiguous decision on further actions with the cause of this defect based on the requirements of the current regulations of the organization.