Assessment of Digital Twins to Reassign Multiskilled Workers in Offsite Construction Based on Lean Thinking
Bibliographic record
Abstract
Offsite construction (OSC) is an innovative approach where building components (e.g., panels or modules) are manufactured in a shop floor environment, then transported to, and installed at the site. Although there are numerous benefits inherent to the OSC approach, practitioners still struggle to provide tailored projects to their clients due to the low level of flexibility in production caused by uncertainty, multiple projects, and variable market demands. Indeed, the lack of production flexibility limits shop floors to manufacture projects efficiently in an ever-changing environment, especially when processes are still labor-intensive and are not leveraged by autonomous systems, such as a digital twin (DT). Hence, this paper proposes the use of a DT to improve production on OSC shop floors by increasing flexibility, i.e., the ability to adapt to uncertainty, through the automated reassignment of multiskilled workers based on data pertaining to production status that are updated in near real-time. The present study presents key metrics adopting a lean thinking approach for waste identification that quantifies the improved production performance attributable to the proposed DT. Using simulation as a surrogate system, this research evaluates the production performance on the shop floor according to different simulated scenarios varying the number of interventions made by the DT and multiskilling configurations. Moreover, this research considers significant aspects of multiskilling such as reduced productivity, increased cost, and the time spent when moving between workstations during reassignment. The primary findings from the system’s practical application indicate a significant improvement in production due to the reduction of waiting waste, total production duration, and total production cost being reduced by 62%, 40%, and 25%, respectively. Finally, the present study presents a novel approach to increasing flexibility on shop floors while also demonstrating the benefits attributable to the use of a DT to manage multiskilled workers in OSC.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".