Adio, Obafemi Adekunle (2022) An automated knowledge-based decision support system for managing non-conformances on australia's large infrastructure projects. PhD thesis, University of Melbourne, Australia.
Abstract
The construction industry in Australia and most of the developed world has underperformed in its response to ongoing challenges posed by globalisation, increased competition, pressure to improve productivity, demand for better project performance, and pressure to deliver profit to stakeholders. A central underperformance area is a failure to deliver a project to meet a set of requirements agreed as part of the contract (non-conformance). The inability to adequately manage non-conformances and their attendant impacts often result in cost and time overrun, among others. The underperformance of the construction industry is also evident in the high rate of recurring non-conformances across projects, which may create ongoing maintenance challenges due to defects arising during the design life of a product. To minimise non-conformances and their impact, construction companies must capture, document, and disseminate knowledge and lessons from previous project non-conformances to translate them into improvements to prevent future recurrence of similar non-conformances. However, non-conformance lessons are currently unstructured and inadequately disseminated in the construction industry, with projects placing a high premium on experience. Different functional areas within projects sometimes operate in silos by circulating information within their immediate groups only. Disseminating knowledge and lessons from previous project non-conformances will result in more efficient and effective construction processes that will deliver projects on time and budget, reduce safety incidents, and meet all stakeholder requirements. Such dissemination will also help construction companies develop organisational intelligence to increase their competitive edge when bidding for new projects. Hence, this research developed an automated knowledge-based decision support system (KBDSS) for managing non-conformances on infrastructure construction projects. The KBDSS is equipped with artificial intelligence to classify non-conformances from across multiple projects in one place and analyse them. Project participants can thereby gain timely access to relevant information and knowledge from past non-conformances to enhance their decision-making. Four main questions underpinned this research: "How do construction companies perceive and rate quality in comparison to other project constraints?", "How important are non-conformance lessons to the corporate strategy for future projects?", "How are non-conformances currently managed on construction projects?", and "How can an automated knowledge-based decision support system (KBDSS) for extracting, analysing, and disseminating non-conformances be developed for Australia's infrastructure projects?" This research adopted the design science research paradigm to understand the theory of non-conformances, how it has been managed, and the existing gaps in theory and practice to develop a KBDSS that meets the expectation of the infrastructure construction projects. A total of 13,940 non-conformance data from 15 infrastructure projects by a tier-one construction company in Australia were accessed, with 11,334 of them thoroughly reviewed. A total of 60 interviews were conducted in the first instance with stakeholders in the infrastructure construction industry and participants from five major infrastructure projects across Australia with a combined value of over AU$15 billion. The roles of respondents included project managers, design managers, quality managers and project engineers. The 11,334 non-conformance data from the selected projects were analysed, revealing a lack of adequate classification and dissemination of non-conformance lessons using existing systems. The data analysis culminated in developing and validating the KBDSS prototype to support decision-making in non-conformance management on infrastructure projects.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Aibinu, Ajibade and Vaz-Serra, Paulo |
| Uncontrolled Keywords: | non-conformance; nonconformance; defect; rework; decision support system; knowledge-based decision support system; knowledge management; infrastructure projects; construction projects; artificial intelligence; project control; project management |
| Index terms: | Australia, productivity, corporate strategy, time overrun, project manager, project engineer, artificial intelligence, project control, project management, infrastructure project, construction industry, project performance, organizational intelligence, placing, paradigm, profit, construction company, project constraint, rework, prototype, decision support, design science research, data analysis, large infrastructure project, interview, design manager, manager, bidding, knowledge management, infrastructure construction, competition, globalization, dissemination, construction project, decision-making, construction process |
| Subjects: | operations management, knowledge management, bidding, economic analysis, organization, practitioner, market analysis, building construction, Geography, production management, infrastructure engineering, modelling and simulation, data analysis and analytics, artificial intelligence, profession, data collection methods, business, control systems, project controls, decision analysis, infrastructure and transport systems, industry analysis, economic factors, education and knowledge transfer, design practice, management, concrete and cementitious materials, civil engineering, knowledge translation, project management theory and practice, professional development |
| Topics: | Site Management, Time Control, Digital Applications, Design Practice, International Construction, Roles and Professions, Governance, Business Strategy, Construction Materials, Information Management, Research Practice, Risk Management, Procurement, Engineering Principles, Project Management, Geographical Context |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here