Toward Intelligent Sustainability: A Structured Integration of Artificial Intelligence and Lean Six Sigma in Life Cycle Assessment
Bibliographic record
Abstract
The manufacturing industry is a lifeline, essential for producing goods that sustain human life and support modern economies, yet it carries significant environmental burdens, depletes valuable resources, and can pose human hazards. Lean Six Sigma (LSS) is a predominantly data-driven methodology designed to reduce waste and enhance efficiency, but its reliance on manual data collection and after-the-fact analysis can slow implementation and leave blind spots in real-time inspection. Integrating Artificial Intelligence (AI) addresses this gap by enabling real-time monitoring and predictive interpretation of process data, facilitating predictive maintenance, and improving operational efficiency. In the context of Industry 4.0 and 5.0, controlling the pitfalls of modern technologies, ensuring high accuracy and minimal waste, is a key challenge. LSS provides a structured framework to identify areas for improvement, while AI enables rapid analysis and immediate corrective action. This narrative conceptual review synthesizes literature on AI, LSS, and Life Cycle Assessment (LCA) to examine how AI-enabled LSS can enhance the manufacturing stage of LCA. By linking shopfloor data to LCI and LCIA, the proposed AI-LSS integrated framework makes LCA more actionable, helping mitigate trade-offs such as pollution and resource consumption, and supporting long-term sustainability and product longevity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".