Risk management framework - an analysis of risk management process in SME's of the UK construction industry
Year: 2024
Type: dissertation
Abstract: It is crucial to counter the multifaceted threats associated within construction project lifecycle, in particular for SME’s in the UK construction industry, not only to secure work but also to make a profit. This research seeks to identify and evaluate these risks and their effective mitigation measures and to develop a risk management framework for the SME’s in the UK construction industry, which the investors/ developers/ contractors can adopt when contracting construction work or during the project lifecycle. Furthermore, the current status of risk management and organizational culture is also evaluated. Two surveys were conducted, and thirty-two critical risks were identified, categorized into six hierarchical risk levels subcategories, and evaluated and ranked their criticality. For the top 10 the identified risks, practical mitigation measures have also been proposed. The survey respondents have perceived almost all mitigation measures as effective. Findings after the extraction of results were, that there are no active Risk Management Frameworks for the SME’s in the UK construction industry. Considering all the factors identified which include the expertise, knowledge, resources and need, an easy to understand risk management framework is proposed which has 5 step procedures which is beneficial at any stage of the project cycle, and does not require special qualifications or knowledge to adopt and implement. All the guidelines are provided in the appendix for successful implementation of risk management framework. Based on the findings and results from the questionnaire surveys and literature review, a qualitative risk management framework was finally proposed with its complete implementation guidelines provided, which will benefit the risk management of SME’s in the UK construction industry.
Author: Ahmed, Shayan
Institution: University of the West of Scotland
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6949415141
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