Title
Decision Tree Models for Automated Quality Tools Selection
Authors
[ 1 ] Instytut Technologii Materiałów, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2026
Published in
Article type
scientific article
Publication language
english
Keywords
Abstract
EN 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%.
Date of online publication
02.01.2026
Pages (from - to)
472-1 - 472-15
Comments
Article Number: 472
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final published version
Date of Open Access to the publication
at the time of publication
Full text of article
Access level to full text
public
Ministry points / journal
100
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