Advanced planning with supplier selection for modular construction supply chain performance improvement
Bibliographic record
Abstract
Purpose This study aims to address project delays and cost overruns in modular construction (MC) caused by poor supplier performance and lack of stakeholder collaboration. This study introduced a decision-support model to help designers and project managers evaluate various modularity and supplier scenarios, enabling the selection of the most cost-effective and time-efficient solutions for MC projects. Design/methodology/approach A generalized mixed-integer linear programming model was developed to optimize supplier selection and activity planning during the project planning phase. The model evaluates multiple modularity and supplier scenarios to identify optimal solutions and is validated through a numerical example to assess its practical applicability and effectiveness. Findings The findings demonstrate that the proposed model significantly enhances the supply chain performance in MC projects. The model mitigates delays and cost overruns by optimizing supplier selection and project activity planning. Experimentation confirms that strategic decisions informed by the model lead to improved efficiency, cost savings and project outcomes. Moreover, the model facilitates enhanced collaboration and decision-making among project managers and designers by enabling scenario evaluations. Originality/value This study presents a novel optimization framework that integrates a transition matrix approach to improve supply chain performance during the transition to MC. By aligning strategic planning with supply chain decisions, the model provides a comprehensive tool for evaluating modularity and supplier options, fostering smoother integration and better project outcomes in MC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".