Anticipating What May Go Wrong: Implications for Managing Schedule Risk
The probability of schedule overruns on construction projects can be ascertained using a 'best fit' probability distribution from an empirical distribution. The statistical characteristics of schedule overruns occurring in 276 Australian construction and engineering projects were analysed....
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
RICS 2012
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/21091 |
| _version_ | 1848750493731389440 |
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| author | Love, Peter Sing, Michael Wang, Xiangyu Yung, Ping Odeyinka, H. |
| author2 | Paul Chynoweth |
| author_facet | Paul Chynoweth Love, Peter Sing, Michael Wang, Xiangyu Yung, Ping Odeyinka, H. |
| author_sort | Love, Peter |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The probability of schedule overruns on construction projects can be ascertained using a 'best fit' probability distribution from an empirical distribution. The statistical characteristics of schedule overruns occurring in 276 Australian construction and engineering projects were analysed. Skewness and kurtosis values reveal that schedule overruns are non-Gaussian. Theoretical probability distributions are then fitted to the schedule overrun data; including the Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared non-parametric tests to determine the 'Goodness of Fit.' A Four Parameter Burr (4P) probability function best describes the behaviour of schedule overruns, provides the best overall distribution fit and is used to calculate the probability of their occurrence. Implications for 1nanaging schedule risk are discussed. |
| first_indexed | 2025-11-14T07:37:43Z |
| format | Conference Paper |
| id | curtin-20.500.11937-21091 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:37:43Z |
| publishDate | 2012 |
| publisher | RICS 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-210912017-01-30T12:23:09Z Anticipating What May Go Wrong: Implications for Managing Schedule Risk Love, Peter Sing, Michael Wang, Xiangyu Yung, Ping Odeyinka, H. Paul Chynoweth The probability of schedule overruns on construction projects can be ascertained using a 'best fit' probability distribution from an empirical distribution. The statistical characteristics of schedule overruns occurring in 276 Australian construction and engineering projects were analysed. Skewness and kurtosis values reveal that schedule overruns are non-Gaussian. Theoretical probability distributions are then fitted to the schedule overrun data; including the Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared non-parametric tests to determine the 'Goodness of Fit.' A Four Parameter Burr (4P) probability function best describes the behaviour of schedule overruns, provides the best overall distribution fit and is used to calculate the probability of their occurrence. Implications for 1nanaging schedule risk are discussed. 2012 Conference Paper http://hdl.handle.net/20.500.11937/21091 RICS 2012 restricted |
| spellingShingle | Love, Peter Sing, Michael Wang, Xiangyu Yung, Ping Odeyinka, H. Anticipating What May Go Wrong: Implications for Managing Schedule Risk |
| title | Anticipating What May Go Wrong: Implications for Managing Schedule Risk |
| title_full | Anticipating What May Go Wrong: Implications for Managing Schedule Risk |
| title_fullStr | Anticipating What May Go Wrong: Implications for Managing Schedule Risk |
| title_full_unstemmed | Anticipating What May Go Wrong: Implications for Managing Schedule Risk |
| title_short | Anticipating What May Go Wrong: Implications for Managing Schedule Risk |
| title_sort | anticipating what may go wrong: implications for managing schedule risk |
| url | http://hdl.handle.net/20.500.11937/21091 |