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This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

This study comprehensively reviews the Fourth Industrial Revolution, which refers to Industry 4.0 (IR 4.0) applications in small and medium enterprises (SMEs). Multinational companies and big corporations have the capacity and resources to implement IR 4.0, but SMEs are limited due to financial constraints, expertise and lack of resources. Even so, IR 4.0 is required as technologies evolve and market demand has changed how firms do business.

To uncover the potential of IR 4.0 and critical determinants of SMEs’ adoption of IR 4.0, this study presents a bibliometric analysis to evaluate the current research streams in IR 4.0 adoption among SMEs through bibliographic coupling. Furthermore, this review provides a glimpse of the future by analyzing prospective trends on IR 4.0 in SMEs.

Bibliographic coupling produces five clusters: (1) challenges and barriers in IR 4.0 implementation among SMEs, (2) technological adoption of IR 4.0, (3) opportunities and benefits of IR 4.0, (4) business model innovation and (5) implication of IR 4.0 on SMEs technologies. On the contrary, co-word analysis produces three clusters: (1) technologies in IR 4.0, (2) strategy and management of IR 4.0 among SMEs and (3) IR 4.0 model for SMEs.

Implications are directly related to business owners, policymakers and technology developers meeting the needs of the industry and SMEs, which are the focus of this review.

The findings contribute significantly to the body of knowledge by presenting a state-of-the-art science mapping approach to uncover the knowledge structure and intellectual linkage of IR 4.0 adoption within SMEs.