This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, GPT-4) to generate synthetic supply chain scenarios.

Purpose: The integration of digital technologies into quality management systems has led to the emergence of Quality 4.0 (Q4.0), a transformative paradigm aligned with the principles of Industry 4.0. This study presents a systematic literature review of 102 peer-reviewed articles indexed in the Scopus database between 1993 and 2025, offering a comprehensive and structured overview of the field. Design/methodology/approach: By employing a systematic review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework and a cluster analysis, the research identifies four key thematic areas: (1) integration of total quality management and Quality 4.0; (2) digital transformation of Quality 4.0 processes; (3) practical and theoretical applications of Quality 4.0 and (4) implementation readiness, success factors and barriers to Quality 4.0 implementation. Findings: Despite Quality 4.0’s growing relevance, the findings reveal significant research gaps, including limited empirical validations, an overemphasis on technological tools at the expense of organizational and human factors, scarce sector-specific applications and an emergent shift toward Q5.0, highlighting a nascent move toward human-centric and proactive quality control. This paper contributes to both academia and industry by synthesizing fragmented knowledge, proposing a future research agenda and offering actionable insights for the successful implementation of Quality 4.0. Originality/value: This review offers substantial contributions to both academia and industry. For researchers, it synthesizes fragmented knowledge and provides a clear roadmap for future research, urging a shift towards empirical studies and a more holistic view of Q4.0. For practitioners and policymakers, the paper presents actionable insights and a strategic guide to navigate the challenges of digital transformation, emphasizing the importance of human capital development, organizational culture and risk management in achieving successful Q4.0 implementation. This work bridges the gap between theoretical frameworks and practical application, serving as a critical resource for all stakeholders.

This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts.