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Project 2: Methodology to Predict Contractors’ Performance Using Price and Non-price Measures
| PhD Candidate |
|---|
 |
| Mr. Kasun Gunasekara |
| Chair Supervisor | Co-Supervisor | Co-Supervisor |
|---|---|---|
 |  |  |
| Prof. Srinath Perera | A/Prof. Mary Hardie | A/Prof. Xiaohua Jin |
This research aims at developing a model to analyse construction contractors’ performance based on past project records using price and non-price measures, as a tool for improving project performance.
The unique characteristics of construction, along with its project-specific, multi-stakeholder based setup add to the complexity in measuring its performance. As the success of a construction project heavily depends on its key player: the head contractor, selecting a well performing one is crucial. Although various criteria are being used for assessing and selecting contractors, a systematic approach is not available. Price based measures are complex, hard to capture and compare across different construction project types, sizes, geographic locations and jurisdictions. Alternatively, non-price measures are often recorded due to administrative or regulatory requirements, hence are readily available.
Initially, dimensions of performance and concepts defining performance were identified, defined and analysed. Through systematic literature review and expert forums, the critical measures of performance that represent the concepts defining performance were identified. Based on these critical measures of performance, project data sets will be obtained and analysed to create a model as a performance index. Benchmarks will be calculated and the model will be further developed using simulation to connect project performance to contractors’ performance. Finally, the model will be tested and validated through a set of project data sets.
The initial outcome of this research will be a Performance Index that can be used to gauge the level of performance of contractors. The other main outcome will be a Decision Support System based on a performance prediction model, that enables the client to manipulate performance preferences. It will utilise a limited set of measures of performance that are easy to measure and readily available. The outcome will enable the client to grade the contractors based on actual past project performance.
Industry Partners
- Meriton Group (Data provider)
Sources of Funding
- Centre for Smart Modern Construction Postgraduate Research Scholarship
Publications
- Gunasekara, K., Perera, S., Hardie, M. and Jin, X., 2019, An exploratory study to identify factors affecting performance of construction projects, In Proceedings of the CIB World Building Congress, Hong Kong SAR, China, 17-21 June 2019. Available at:
https://www.researchgate.net/publication/334163033_An_Exploratory_Study_to_ Identify_Factors_Affecting_Performance_of_Construction_Projects
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