Role of smart technologies for implementing industry 4.0 environment in product lifetime extension towards circular economy: A qualitative research
Bibliographic record
Abstract
Since sustainability has grown in prominence over the past two decades, the impetus for improving the lifetime of durable and semi-durable manufactured goods has never been stronger. In order to decrease the level of natural resources extraction, as well as the increasing amounts of residuals and solid waste, product lifetime extension (PLE) strategies, such as improved design, maintenance, redistribution, access, and recovery, show great potential, especially if paired with smart technologies in the Industry 4.0 (I4.0) environment. Much research has investigated the contribution of I4.0 technologies to sustainability and the circular economy. Yet, only a few studies have focused on exploring the contribution of smart technologies to the specific area of PLE. This paper advances knowledge regarding the impact of four specific types of smart technologies on PLE strategies: Additive Manufacturing, the Internet of Things, Big Data, and Artificial Intelligence. This study consists of exploratory qualitative research to explore the mechanism underlying the implementation of these I4.0 technologies in PLE for a circular economy. Qualitative data is collected through twenty semi-directed, in-depth interviews with business leaders and executives involved in product development and research and development (R&D) in Quebec, Canada. An analytical approach parented to grounded theory and consisting of open, axial, and selective coding led to identifying four emerging themes explaining how focal smart technologies contribute to PLE. These include (1) empowerment and acceleration of R&D (improvement of prototypes, prototype validation), (2) smarter production (tooling assistance, manufacturing assistance), (3) automation of managerial and operational processes (automation of management, automation of production), and (4) help with decision-making (anticipation, identification, and solving of problems). These findings have broad relevance for sustainability theory and practice by highlighting the specific mechanisms through which technology contributes to product sustainability.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".