Corporate Safety Performance Benchmarking in Construction

Dai, Jiukun; Mulva, Stephen P.; Chanmeka, Arpamart; Suk, Sung Joon; Yun, Sungmin

doi:10.1061/9780784412329.144

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Yeung et al (2013) developed a benchmarking model for construction projects in Hong Kong using qualitative weights obtained through a questionnaire survey and analyzed using reliability interval method. Du et al (2014) created a building information modeling (BIM) cloud score for benchmarking BIM performance, Dai et al (2012) provided comprehensive benchmarks for corporate safety performance in construction, and Labarre and El-adaway (2014) benchmarked projects performance by collecting qualitative ratings as a tool for mitigating contractual claims. Finally, He et al (2021) developed a list of key performance indicators (KPIs) for the success of construction megaprojects without synthesizing these KPIs into a benchmarking methodology.…”

Section: Introductionmentioning

confidence: 99%

“…Du et al. (2014) created a building information modeling (BIM) cloud score for benchmarking BIM performance, Dai et al. (2012) provided comprehensive benchmarks for corporate safety performance in construction, and Labarre and El-adaway (2014) benchmarked projects performance by collecting qualitative ratings as a tool for mitigating contractual claims.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking of construction projects performance for comparative assessment and performance improvement: a statistical quantitative approach

Aboseif

Hanna²

2023

ECAM

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PurposeThe exact process of construction projects performance assessment and benchmarking still remains subjective relying on qualitative techniques, which does not allow stakeholders to address the issues and the drawbacks of their respective projects as effectively as possible for performance improvement purposes. Hence, this research aims to establish a unified project performance score (PPS) for assessing and comparing projects performance.Design/methodology/approachData were collected from Construction Industry Institute (CII) members and through University of Wisconsin active research projects. Exploratory data analysis was done to investigate the calculated performance metrics and the collected data characteristics. Data were converted into six performance metrics which were used as the independent variables in creating the PPS model. Logistic regression model was developed to generate the unified PPS equation in order to explain the variables that significantly affect construction projects successful post-completion performance. The PPS model was then applied on the collected dataset to benchmark projects in terms of project delivery systems, compensation types and project types in order to showcase the PPS capabilities and possible applications.FindingsThe model revealed that construction cost and schedule growth are the most important metrics in assessing projects performance, while RFIs’ processing time and change orders per million dollars were the features with the least effect on the PPS value. The authors found that integrated project delivery (IPD) and target value (TV) projects outperformed all other project delivery and compensation types. While, industrial projects showed the worst performance, as compared to commercial or institutional projects.Originality/valueThe PPS model can be used to assess the performance of any pool of executed projects, and introducing a novel addition to the field of construction business analytics which is a supplementary tool to successful decision making and performance improvement. Additionally, the bidding selection system can be revolutionized from a cost-based to a performance based one using the PPS model to improve the outcomes of the buyout process.

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