Framework for Using LPS in Design on IPD Projects
Bibliographic record
Abstract
Design in nature is an iterative and interdependent process.Previous research shows that in some projects, 50% of this process contains waste.The Last Planner System (LPS) proved its efficiency in planning and controlling the execution phase.However, due to the nature of the design process, implementing LPS at this stage contains many constraints.Results show that the Integrated Project Delivery (IPD) and LPS together can significantly improve design workflow, still some issues remain that do not let the IPD project achieve the full potential of LPS in managing a design process.In this research the main constraints are studied and divided into five categories.Recently, many researchers studied the benefits of implementing LPS and how to optimize this method, especially in the execution phase, but there is no integrated framework that contains the available tools and techniques for overcoming constraints in using LPS at the design process.This study indicates that multiple strategies need to be adopted for increasing the applicability of LPS at the design process of a construction project.This paper proposes an integrated framework for addressing design constraints and optimizing the applicability of LPS in the design process on IPD projects.
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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 | 0.000 | 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 | 0.000 |
| 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 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".