A delay risk management framework for construction projects: Learning from past experiences

Despite efforts made by professional and academic bodies to keep construction projects on track, those projects are still being delayed. One of the main reasons for this situation is that the construction companies are not effective in using their historical information and lessons learned in delay risk identification, analysis and treatment. The existing studies to connect knowledge and risk management areas have mostly sufficed in creating searchable databases and knowledge repositories which are lacking context and are affected by the subjective judgement of the users. This research aims to facilitate using historical information for delay risk management. The first issue that emerged and was addressed to enable this is the lack of a standard terminology and taxonomy as a common language to refer to the delay risks. Without such a standard terminology, it is very difficult and sometimes impossible to collect and analyse the delay risk management data across multiple projects or organisations. On that note, this research systematically reviewed the literature to formulate a delay risk terminology and taxonomy. During the systematic literature review, forty-six articles were identified using the search criteria, and the top ten delay risks reported in each article were coded and analysed. As a result, twenty-six individual delay risks were identified. By applying a word frequency analysis, terminology was developed to describe each delay risk. Furthermore, the risks were cross tabulated to risk categories to develop a risk taxonomy in the form of a risk breakdown structure. The next step was to evaluate the generalisability of the delay risk taxonomy across the regional contexts. The analysis showed that some of the delay risks are generic across all geographical locations studied, while others are highly dependent on the country in which the project is being executed. The risk breakdown structure (RBS) developed via the systematic literature review was then validated and refined through focus groups, which eventually resulted in a list of thirty-three delay risks. The main delay risks were grouped under ten sub-categories and three main categories of Stakeholders, Resources, and Processes. The next step was identifying the contextual factors that may influence the presence or impact of the delay risks. This step was also achieved through focus groups. Fourteen contextual factors forming the context of delay risks were identified and grouped under three main categories of Project, Organisational, and External factors. Findings of the focus groups were used to design and conduct a questionnaire survey to collect quantitative data which were used to identify the most impactful construction delay risks in Australia. The delay risks do not happen in an isolated environment, and emergence of one delay risk may affect the presence of impact of other delay risks. Therefore, a comprehensive study of delay risks is not limited to identification of the risk titles, but also includes exploring the impact, associations amongst, and timing of the delay risks. On that note, this research for the first time has investigated the impact of delay risks, associations among them, and project phases in which they are likely to happen for the Australian construction industry. It is achieved by the collection and analysis of data from 118 construction projects completed with delays in Australia. The data was collected via questionnaire survey from project professionals involved in those projects. The Australian construction industry was compared against twenty-six countries, and it was found that Australia represents a relatively unique context in terms of delay risks. This research proposed a data-driven delay risk management framework to support extraction of delay risk knowledge from historical data. The survey data was used to demonstrate how the framework could support expert judgement in delay risk identification and analysis, and to show how the framework can facilitate data mining. The proposed framework was then validated throu h a focus group where the subject matter experts found the framework very useful in regard to delay risk management purposes. The experts’ feedback on advantages, barriers and implementation of the framework was also sought. It was argued in this thesis that the proposed framework should not replace but complement the traditional risk management process.