Assessing the Sustainability Impacts of Industry 4.0 on Maintenance Policies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maintenance strategies have traditionally been designed with a primary focus on cost reduction and operational efficiency, often overlooking their broader environmental and social impacts. However, in the current context where industries must align with European carbon neutrality 2050 objectives and the United Nations Sustainable Development Goals (SDGs), maintenance is recognized as a key lever for enhancing the three pillars of sustainability in industries: economic, social, and environmental. In addition, recent studies have shown that the ongoing digital transformation of industry through Industry 4.0 technologies such as artificial intelligence, Internet of Things, digital twins, and big data analytics, offers new opportunities to improve maintenance strategies. These developments have given rise to the concept of Maintenance 4.0, which opens new perspectives for aligning maintenance practices with broader sustainability objectives.To better understand the impact of these technologies on maintenance sustainability, as well as the existing assessment initiatives in the current state of research, this paper conducts a systematic literature review (SLR). A total of 31 relevant studies were analyzed and classified into literature reviews, conceptual frameworks, and evaluation models. The review reveals that while economic and environmental benefits are increasingly supported by measurable indicators, the social dimension remains underexplored and lacks standardized metrics. In addition, most studies focus on short-term operational gains and do not address life cycle-wide perspective, including manufacturing and end-of-life stages.Based on these findings, this paper (i) clarifies the current maturity of research and its exploratory nature; (ii) identifies major gaps which is the lack of lifecycle-based assessments and operational social indicators; (iii) highlights the weak operationalization of circular economy principles in maintenance 4.0 strategies; and (iv) proposes future research directions to develop holistic, life cycle-oriented, human-centric, and practically validated frameworks. These contributions aim to support the transition toward more sustainable maintenance practices, in alignment with sustainability goals.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it