Key performance indicators to measure design performance in construction
Bibliographic record
Abstract
The performance of the design activities for a construction project can have a significant impact on the overall performance and efficiency of the project. Design activities need to be monitored to measure performance of the design process. Performance indicators can be used in this process. The Indicators can: i) measure the degree of success of a project or organization; ii) predict, control and measure the performance of design processes; iii) benchmark performances of different projects within the same- company or with other firms; iv) track and demonstrate long-term development and improvement, thereby decreasing design and construction cost and time and increasing the quality of the design product. In the context of Canada, specific construction performance indicators to assess construction project performance across project phases have yet to be formulated and documented. Therefore, there is a need to develop such indicators for the Canadian consulting engineering. From this perspective, the present research introduces practical framework and describes a model that measure the performance of the design activities for Canadian construction projects. The main objectives of this research are the following: i) to identify key indicators that affect the design performance of construction projects; ii) to develop a model for Key Performance Indicators (KPIs) to measure the performance of design activities in the Canadian construction industry; and iii) to examine the possibility of their use in the construction industry. The methodology adopted for this research is based on review of the existing literature on design processes, review of the existing literature on design performance indicators, questionnaire surveys, interviews with practitioners, and case studies. The questionnaires along with the interviews with designers and managers from the Canadian consulting engineering are mainly conducted to explore and indentify indicators affecting the design performance. The case studies are used to validate and amend the use of these indicators in measuring project performance at the design stage. A web-based questionnaire aimed at design and construction firms was constructed. The significance and the quantification of design performance indicators are determined using a statistical package. The results from the questionnaire were used to develop a generic set of nine groups of design performance indicators for the Canadian consulting engineering. However, this research focuses on the heavy construction sector. The nine groups of indicators have been compared in pairs to identify their level of importance to each other. Experts from heavy construction participated in the pairwise comparisons task. Built on the results of the survey and experts judgment, a Model for Design Performance Measurement (MDPM) is introduced. The MDPM uses the standard Analytic Hierarchy Process (AHP) method to assign weights to the scores of the selected indicators, to measure a project performance and to compare projects. The MDPM is tested for small scale heavy constructions. The developed design performance measurement model can 1) predict, track, and control future performance, 2) highlight area/s for future improvement, 3) enable companies to benchmark the performance of different projects from the same or different companies, and 4) document all design performance data
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".