Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Knowledge management in PPP decision making concerning value for money assessment

Ren, Guoqian 2019. Knowledge management in PPP decision making concerning value for money assessment. PhD Thesis, Cardiff University.
Item availability restricted.

[thumbnail of 2020RenGPhD.pdf]
PDF - Accepted Post-Print Version
Download (8MB) | Preview
[thumbnail of Cardiff University Electronic Publication Form] PDF (Cardiff University Electronic Publication Form) - Supplemental Material
Restricted to Repository staff only

Download (928kB)


Public-private partnership (PPP) is the current procurement model used for largescale public engineering and municipal facilities procurement and is now advocated by governments around the world. Value for money (VfM) assessment is a critical process used to evaluate whether the PPP procurement model applies to a project throughout its lifecycle. VfM assessment aligns with financial capacity assessment and feasibility assessment, providing an essential decision-making reference for project managers and associated with performance measurement (PM) in the ex-post stages, which are linked to the payment model. Therefore, the evaluation of the project through VfM determines the success of the PPP model to a great extent. Through a comprehensive literature review, the dissertation identifies the practical deficiencies for the current VfM assessment process and presents a detailed view of the content of the VfM assessment. Based on that, the dissertation determines the research motivation and formulates detailed research questions and hypotheses to establish a thorough understanding of a knowledge-based management platform to achieve automated processes that support human decision making. By using the deductive approach, this research leverages knowledge engineering principles and developed a core VfM knowledge base to help manage knowledge in the PPP decision-making process concerning VfM assessment. The system development is based on the design science methodology. In order to integrate information about the engineering project management and VfM assessment, the standardized information exchange processes are first used to develop robust information with project versatility. The Building Information Modelling (BIM) application platform, which uses Industry Foundation Classes (IFC) as the engineering project data source, is connected with the developed information schema, thus establishing the basis for implementing automated information exchange. Second, ontology modeling is used to establish a VfM knowledge base. The various factors associated with the PPP project can be stored in a semantic environment for better identification by engineers. Furthermore, automated project assessment is achieved by developing different rules-based functions in the ontology and combining them with the data platform represented by BIM. By using case studies and action research strategies, this research demonstrates the feasibility of the constructed knowledge base. Abstract iv The main outcomes of the research lie on the definition and screening of information for VfM assessment, as well as the establishment and application of a smart knowledge base. The developed components and software tools have been thoroughly tested, validated and calibrated by leveraging knowledge from experienced domain experts, software companies and other industry partners. Due to the generic development principles adopted in the research, the method, tools and framework can be further extended for other related areas where smart decision making is required

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Engineering
Uncontrolled Keywords: Building Information Modelling; Public private partnership; Value for money; Ontology; Procurement decision-making; linked-data.
Date of First Compliant Deposit: 15 April 2020
Last Modified: 15 Apr 2020 08:29

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics