Key performance indicators to measure design performance in construction
Notice bibliographique
Résumé
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
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».