Decision Tree Models for Automated Quality Tools Selection

Quality tools have a well-established place in business management. They help diagnose, analyze, and solve quality problems. In manufacturing companies, they are also used in process and product improvement projects. However, only the proper selection of quality tools can bring tangible benefits to an organization. Given their diverse content and methodologies, supporting the selection of these tools becomes a crucial issue. A literature review indicates only a few solutions in this area, implemented as decision support systems. Additionally, the challenges of Quality 4.0 and the demands of modern business reveal a research gap in automating the process of selecting quality tools. This is particularly true for less experienced company employees participating in improvement programs. Our previous research shows how machine learning using neural network models supports the development of an expert system in this area. The aim of this paper is to present the results of research conducted in which classifiers in the form of decision trees were developed. At the same time, attempts were made to demonstrate that decision tree classifiers (on an extended Excellence Toolbox dataset) can automatically recommend qualitative tools with an accuracy better than neural networks, while offering interpretable rules. The decision-tree models achieve strong classification performance, with the best tree reporting 96.75% effectiveness. In contrast, the neural network from previous studies achieved 94.87%.