LPS Performance Diagnosis Model Using Fuzzy Inference System
Bibliographic record
Abstract
The Last Planner System (LPS) has long been used in construction projects to promote reliable planning and enhance productivity.However, despite various attempts to evaluate LPS implementation efforts, the human aspect of the evaluation attempts has not been given enough attention.This issue may be tackled through Fuzzy Inference Systems (FIS) to capture more information regarding the gradual and intricate changes in scoring systems.Therefore, this paper aims to offer a standardized diagnosis model for LPS performance in construction projects.This model employs an FIS that analyzes the results of an LPS implementation for a more accurate investigation of the implementation.First, a thorough literature review is conducted to select the most prominent factors influencing the LPS implementation process, followed by expert panel questionnaire development and distribution among LPS experts to rank the selected factors.The obtained questionnaire results are then used to develop the FIS.The objective of this paper is hereby twofold: (1) to allow assessing expected LPS benefits through the qualitative assessment of the performance in the four LPS phases, and (2) to facilitate comparing past, current, and future performances throughout the organization's LPS implementation process to ensure continuous improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".