Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects
Within construction, we have become increasingly accustomed to relying on the benefits of digital technologies, such as Building Information Modelling, to improve the performance and productivity of projects. We have, however, overlooked the problems that technology is unable to redress. One such pr...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
ELSEVIER SCI LTD
2022
|
| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP210101281 http://hdl.handle.net/20.500.11937/90139 |
| _version_ | 1848765335680843776 |
|---|---|
| author | Matthews, Jane Love, Peter Porter, Stuart R. Fang, W. |
| author_facet | Matthews, Jane Love, Peter Porter, Stuart R. Fang, W. |
| author_sort | Matthews, Jane |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Within construction, we have become increasingly accustomed to relying on the benefits of digital technologies, such as Building Information Modelling, to improve the performance and productivity of projects. We have, however, overlooked the problems that technology is unable to redress. One such problem is rework, which has become so embedded in practice that technology adoption alone can not resolve the issue without fundamental changes in how information is managed for decision-making. Hence, the motivation of this paper is to bring to the fore the challenges of classifying and creating an ontology for rework that can be used to understand its patterns of occurrence and risks and provide a much-needed structure for decision-making in transport mega-projects. Using an exploratory case study approach, we examine ‘how’ rework information is currently being managed by an alliance that contributes significantly to delivering a multi-billion dollar mega-transport project. We reveal the challenges around location, format, structure, granularity and redundancy hindering the alliance's ability to classify and manage rework data. We use the generative machine learning technique of Correlation Explanation to illustrate how we can make headway toward classifying and then creating an ontology for rework. We propose a theoretical framework utilising a smart data approach to generate an ontology that can effectively use business analytics (i.e., descriptive, predictive and prescriptive) to manage rework risks. |
| first_indexed | 2025-11-14T11:33:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-90139 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:33:37Z |
| publishDate | 2022 |
| publisher | ELSEVIER SCI LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-901392024-04-22T00:58:45Z Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects Matthews, Jane Love, Peter Porter, Stuart R. Fang, W. Science & Technology Technology Information Science & Library Science Business analytics Machine learning Rework Risk Smart data Topic modelling BIG DATA CONSTRUCTION CLASSIFICATION ONTOLOGY PROJECT INFRASTRUCTURE SCIENCE Within construction, we have become increasingly accustomed to relying on the benefits of digital technologies, such as Building Information Modelling, to improve the performance and productivity of projects. We have, however, overlooked the problems that technology is unable to redress. One such problem is rework, which has become so embedded in practice that technology adoption alone can not resolve the issue without fundamental changes in how information is managed for decision-making. Hence, the motivation of this paper is to bring to the fore the challenges of classifying and creating an ontology for rework that can be used to understand its patterns of occurrence and risks and provide a much-needed structure for decision-making in transport mega-projects. Using an exploratory case study approach, we examine ‘how’ rework information is currently being managed by an alliance that contributes significantly to delivering a multi-billion dollar mega-transport project. We reveal the challenges around location, format, structure, granularity and redundancy hindering the alliance's ability to classify and manage rework data. We use the generative machine learning technique of Correlation Explanation to illustrate how we can make headway toward classifying and then creating an ontology for rework. We propose a theoretical framework utilising a smart data approach to generate an ontology that can effectively use business analytics (i.e., descriptive, predictive and prescriptive) to manage rework risks. 2022 Journal Article http://hdl.handle.net/20.500.11937/90139 10.1016/j.ijinfomgt.2022.102495 English http://purl.org/au-research/grants/arc/DP210101281 http://creativecommons.org/licenses/by-nc-nd/4.0/ ELSEVIER SCI LTD fulltext |
| spellingShingle | Science & Technology Technology Information Science & Library Science Business analytics Machine learning Rework Risk Smart data Topic modelling BIG DATA CONSTRUCTION CLASSIFICATION ONTOLOGY PROJECT INFRASTRUCTURE SCIENCE Matthews, Jane Love, Peter Porter, Stuart R. Fang, W. Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects |
| title | Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects |
| title_full | Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects |
| title_fullStr | Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects |
| title_full_unstemmed | Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects |
| title_short | Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects |
| title_sort | smart data and business analytics: a theoretical framework for managing rework risks in mega-projects |
| topic | Science & Technology Technology Information Science & Library Science Business analytics Machine learning Rework Risk Smart data Topic modelling BIG DATA CONSTRUCTION CLASSIFICATION ONTOLOGY PROJECT INFRASTRUCTURE SCIENCE |
| url | http://purl.org/au-research/grants/arc/DP210101281 http://hdl.handle.net/20.500.11937/90139 |