Lean Tools in the Context of Industry 4.0: Literature Review, Implementation and Trends
Bibliographic record
Abstract
With the evolution of Industry 4.0, some problems related to inefficient digitalization become clearer in organizations. To minimize these problems, implementation of the lean philosophy is needed in the digital environment. However, before Lean can start to solve the digitalization problems, there is a need to digitalize its tools so that they can comprehend the Industry 4.0 dynamics and become more effective. The aim of this study is to contribute to the theoretical development of Lean tools in the context of Industry 4.0, promoting directions for the industrial sector from the evolution, difficulties, benefits, implementation and trends of Lean 4.0 tools. To achieve this objective, this study performs a systematic literature review and content analysis of 53 papers from 35 journals. The main results of the research show: (i) the characterization of the Lean 4.0 tools; (ii) the evolution of the Lean tools after the integration with digital technologies; (iii) the main trends of Lean 4.0; (iv) the proposal of a Lean 4.0 theoretical framework. From these results, this paper seeks to promote insights for studies in the area of Lean 4.0, as well as for companies to implement and use the Lean 4.0 tools for better improvement in their digital processes and avoid the digital waste.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".