IoT-based Inventory Control System Framework for Panelized Construction
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
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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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 | 0.000 | 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 | 0.001 |
| 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