Synergizing Lean Six Sigma Framework Using Artificial Intelligence, Internet of Things, and Blockchain for Sustainable Manufacturing Excellence
Bibliographic record
Abstract
Purpose: This study delves deep into systematically integrating AI, Blockchain, and IoT within manufacturing, guided by Lean Six Sigma (LSS) philosophy, aiming to promote higher precision, human safety, sustainability, reduced errors, and wastage while maintaining minimal human involvement. Design/methodology/approach: The study rigorously studies cases of the manufacturing industry integrating these concepts into real industrial or experimental setups and discusses their potential implications. It explores the step-by-step integration, control, and regulation of intricate manufacturing aspects. Findings: Artificial Intelligence is beneficial for real-time regulation and prompt corrective measures during manufacturing operations. The Internet of Things provides real-time feedback, ensuring synchronization between teams and departments to maintain flaw detection and correction. Blockchain offers security and transparency in task performance, supply chain management, and payments, resulting in a seamless and efficient manufacturing experience. Originality: This study is not a mere overview or surface-level juxtaposition of Industry 5.0 concepts. It offers a comprehensive analysis of the holistic integration of advanced technologies within manufacturing, linking developments to sustainable manufacturing. It provides new insights and perspectives not discussed in current literature found in databases like Scopus and Web of Science. Research limitations/implications: The study encourages ongoing research and development to meet modern economic needs and environmental challenges. It emphasizes that the integration is not the endpoint, but continuous improvement (kaizen) should prevail. Practical implications: This study serves as an authoritative source of information for the scientific community to further advancements in the field, guiding them to develop evidence-based opinions. Social implications: The findings support the vision of sustainable digital manufacturing, promoting economic efficiency and environmental sustainability, crucial for the current and future industrial landscape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".