A Roadmap to Integrate the Sustainable Impact of Industry 4.0 Technologies in Maintenance Policies
Bibliographic record
Abstract
Maintenance decision-making has traditionally focused on economic criteria, yet the growing demand for carbon neutrality highlights the need to address all three dimensions of sustainability (economic, environmental, and social) within manufacturing industries.Although Industry 4.0 (I4.0) enabling technologies are widely recognized for their potential benefits, their full sustainability impacts remain poorly understood.Existing studies often emphasize their positive contributions but lack precise quantification of both their positive and negative effects.Moreover, these analyses tend to focus exclusively on the use phase, neglecting impacts during manufacturing and end-of-life stages.This article proposes a structured roadmap for evaluating the lifecycle impact of I4.0 technologies on maintenance policies.By considering multiple scenarios, this approach quantifies their effects across all dimensions of sustainability, ensuring that the benefits realized during use outweigh the negative impacts from manufacturing and disposal.To illustrate its applicability, a preliminary use case is presented using a vibration test bench equipped with IoT sensors.Looking ahead, these sensors are set to generate fault data under varying conditions, which will be used to test maintenance scenarios.Additionally, as outlined in the roadmap, a life cycle assessment (LCA) is planned for the sensor to provide a comprehensive assessment of its sustainability impact.This case study serves to demonstrate the roadmap's relevance and its potential to support sustainable maintenance decision-making, laying the foundation for integrating I4.0 enabling technologies into maintenance strategies while avoiding undesirable rebound effects that could compromise sustainability goals.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".