QUALIBOT: AI assistant for implementing quality management systems in the transition to Enterprise 4.0 and Quality 4.0

Purpose The purpose of this research is to analyze and assess the contributions and obstacles between quality function and Enterprise 4.0, proposing principles and advice for a relevant articulation between these two approaches. It also aims to outline the new tasks of the quality manager in the context of Quality 4.0 and present QUALIBOT, a conceptual explainable AI tool designed to facilitate quality management in the Enterprise 4.0. Design/methodology/approach The cross-analysis of scientific literature and feedback from the field from around twenty industrial companies made it possible to situate the project in the state of the art and to highlight existing practices, their limitations and the main scientific and operational obstacles. The authors also propose a conceptual model, QUALIBOT, which integrates principles of explainable AI to support quality management in an Enterprise 4.0 environment. Findings The research highlights that integrating Quality with technologies of industry 4.0, such as AI, IoT and digital twins, offers significant potential for enhancing performance, flexibility and competitiveness. However, this integration also presents challenges, including the need for a redefined role for quality managers and the adaptation of traditional quality management systems. QUALIBOT is introduced as a tool to address these challenges by providing transparency and efficiency in quality management processes. Research limitations/implications The study is conceptual in nature and primarily focused on proposing principles and frameworks. Empirical validation and testing of the QUALIBOT model and the proposed recommendations are needed to assess their practical effectiveness and scalability. Practical implications The research provides actionable insights for companies transitioning to Enterprise 4.0 by offering guidance on aligning quality management systems with advanced technologies. It underscores the importance of redefining the role of quality managers and leveraging explainable AI tools like QUALIBOT to enhance quality oversight and decision-making in digitally transformed organizations. Originality/value This study contributes to the emerging discourse on Quality 4.0 by exploring the integration of traditional quality management principles with cutting-edge technologies of Enterprise 4.0. It introduces the innovative concept of QUALIBOT, an explainable AI framework tailored to quality management, thus adding a novel dimension to the field.

Authors

Institutions

Publication Details

Journal
International Journal of Quality & Reliability Management
Published
2026-04-01
DOI
https://doi.org/10.1108/ijqrm-01-2025-0033
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

QUALIBOT: AI assistant for implementing quality management systems in the transition to Enterprise 4.0 and Quality 4.0

Marie-Hélène Gentil, Catherine Merle
International Journal of Quality & Reliability Management
Digital Transformation in Industry
article

QUALIBOT: AI assistant for implementing quality management systems in the transition to Enterprise 4.0 and Quality 4.0

Marie-Hélène Gentil, Catherine Merle
article en

Abstract

Purpose The purpose of this research is to analyze and assess the contributions and obstacles between quality function and Enterprise 4.0, proposing principles and advice for a relevant articulation between these two approaches. It also aims to outline the new tasks of the quality manager in the context of Quality 4.0 and present QUALIBOT, a conceptual explainable AI tool designed to facilitate quality management in the Enterprise 4.0. Design/methodology/approach The cross-analysis of scientific literature and feedback from the field from around twenty industrial companies made it possible to situate the project in the state of the art and to highlight existing practices, their limitations and the main scientific and operational obstacles. The authors also propose a conceptual model, QUALIBOT, which integrates principles of explainable AI to support quality management in an Enterprise 4.0 environment. Findings The research highlights that integrating Quality with technologies of industry 4.0, such as AI, IoT and digital twins, offers significant potential for enhancing performance, flexibility and competitiveness. However, this integration also presents challenges, including the need for a redefined role for quality managers and the adaptation of traditional quality management systems. QUALIBOT is introduced as a tool to address these challenges by providing transparency and efficiency in quality management processes. Research limitations/implications The study is conceptual in nature and primarily focused on proposing principles and frameworks. Empirical validation and testing of the QUALIBOT model and the proposed recommendations are needed to assess their practical effectiveness and scalability. Practical implications The research provides actionable insights for companies transitioning to Enterprise 4.0 by offering guidance on aligning quality management systems with advanced technologies. It underscores the importance of redefining the role of quality managers and leveraging explainable AI tools like QUALIBOT to enhance quality oversight and decision-making in digitally transformed organizations. Originality/value This study contributes to the emerging discourse on Quality 4.0 by exploring the integration of traditional quality management principles with cutting-edge technologies of Enterprise 4.0. It introduces the innovative concept of QUALIBOT, an explainable AI framework tailored to quality management, thus adding a novel dimension to the field.

International Journal of Quality & Reliability Management
Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), Institut Polytechnique de Bordeaux (FR)
Industry, innovation and infrastructure
Openalex Percentile: Top 27%
Digital Transformation in Industry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.