IoT-based Inventory Control System Framework for Panelized Construction
Bibliographic record
Abstract
Modular construction and panelized construction have been promoted and recognized globally as advanced construction techniques. Not only have these construction methods been utilized in the oil and gas industry, but they have also successfully been introduced into the residential construction industry. In North America, the panelized construction technique has become popular particularly for wood-frame wall panels. However, although utilizing this advanced construction method can greatly improve the working environment and productivity, the conventional mentality in construction, which overlooks the value of an automated management system to support offsite prefabrication and onsite installation, hinders its potential. An Internet of Things (IoT)-based management system can capture all dynamic data in real time and effectively synthesize it along the supply chain associated with various types of resources. Eventually, with the assistance of a feature-based modeling method, IoT-based information collection can be merged into an Enterprise Resource Planning (ERP) system. Although highly dynamic market demands result in continual changes in the production plan, schedule, and inventory levels, adopting an IoT-based system accounts for the dynamic changes characteristic of this advanced construction method in order to maximize production. Therefore, in this paper, a conceptual framework for an IoT-based inventory control system is proposed in order to enhance the production and satisfy Just-in-Time inventory principle. IoT-based real-time technology is introduced and the development of supportive software is described. Part of the proposed IoT-based inventory control system is implemented as a case study in a panelized construction manufacturing facility, ACQBUILT, Inc., based in Edmonton, Alberta, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".