Risk-Based Comparison of Collaborative Delivery Methods in Canadian Construction: Progressive Design Build, Integrated Project Delivery and Project Alliancing
Notice bibliographique
Résumé
In response to an aging infrastructure network, population growth, and the rising complexity of project delivery, Canada's infrastructure procurement landscape has undergone significant transformation in recent years.In light of this, collaborative project delivery methods, including Progressive Design-Build (PDB), Integrated Project Delivery (IPD) and Project Alliancing (PA) have recently gained traction in Canada as alternatives to traditional models by fostering teamwork, aligning commercial outcomes, and promoting collective accountability among stakeholders.This paper provides the results of a targeted review of existing literature on risk management characteristics of PDB, IPD, and PA across four dimensions: risk allocation and mitigation strategies, risk sharing and incentives and flexibility/adaptability in managing risk.The analysis emphasizes how these models address inefficiencies in traditional delivery systems by promoting collaboration and aligning risks and rewards equitably.To analyze the extent of adoption of the aforementioned delivery methods in Canada, this study surveys Canadian projects that have implemented PDB, IPD, or PA.The compilation of these projects forms a foundational database that supports future research on the influence of risk management practices on collaborative project delivery adoption and implementation in the Canadian context.This study identifies distinct risk management approaches across PDB, IPD, and PA, shaped by their underlying contractual frameworks.Preliminary findings from Canadian projects suggest that delivery method selection is influenced by sector-specific risk profiles and the level of risk integration each delivery method supports. of Metropolitan Montreal, 2023).These delivery methods emphasize early stakeholder involvement, shared risk/reward mechanisms, and a commitment to collaboration-elements that are critical in addressing Canada's infrastructure challenges.On the same hand, the Canadian Council for Public-Private Partnerships (CCPPP) has highlighted the growing relevance of these collaborative models as alternatives to traditional procurement methods, particularly in municipal contexts (The Canadian Council for Public-Private Partnerships, 2024).The selection of these three methods as the focus of this research is grounded in their position between traditional and fully privatized procurement methods, indicating their ability to optimize collaboration between public and private sector partners while maintaining sufficient owner control over project outcomes, as depicted in the CCPPP's latest guide for municipalities (The Canadian Council for Public-Private Partnerships, 2024).While most existing research focuses on collaborative project delivery methods in the U.S (Alleman & Tran, 2020; D. D. Gransberg, 2023;Ma et al., 2022;Rashed & Mutis, 2023) and other international contexts (Australian Government & Department of Infrastructure and Regional Development, 2015; Department of Treasury and Finance, 2010), there is limited exploration of how these collaborative models are applied in Canada.Therefore, this research explores Canadian projects that have implemented these project delivery methods, laying the groundwork for a database of case studies, facilitating future research on risk management in collaborative delivery methods.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,009 | 0,016 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».