Industry 4.0 Technologies and Lean Supply Chain Integration in Manufacturing Industry: A Dynamic Capabilities Perspective
Bibliographic record
Abstract
Over the last decade, the adoption and implementation of Industry 4.0 technologies in manufacturing firms around the world has generated favorable conditions for their application in the supply chain. However, most studies published in the current literature have analyzed each of these concepts separately. Research is still being conducted on how to integrate Industry 4.0 digital technologies into current supply chain practices from a lean thinking perspective. This approach aims to significantly improve the capabilities and performance of the entire supply chain, as well as to provide robust empirical evidence supporting the relationship between these two concepts. Therefore, this empirical study, using a sample of 410 manufacturing firms in Mexico, aims to analyze the relationship between the adoption of Industry 4.0 technologies and the lean supply chain. The results show that the adoption of Industry 4.0 technologies favors the adoption of the lean supply chain in manufacturing firms. Furthermore, based on the results obtained, we recommend that manufacturing company managers jointly adopt and implement I4.0T and LSC practices, as this will allow them to substantially improve the organization's dynamic capabilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".