Exploratory framework for application of analytics in the construction industry

The complex dynamics inherent to the context of decision-making in the construction industry requires more rigorous application of analytics. However, effective frameworks to facilitate such data-driven decision-making are noticeably lacking in the construction industry. To address this lack, the Purdue Index for Construction (Pi-C) is introduced in this paper as a collaborative effort to facilitate and promote data-driven decision-making in the construction industry. As a preliminary step, a hierarchical definition for health of the construction industry is explored based on the results of a literature review, survey, and interviews. The developed hierarchical definition is then used to propose a framework to benchmark, interpret, and analyze data associated with the status of the health of the industry. The proposed framework is tested with existing publicly-available data to explore its effectiveness in improving decisions made in the form of policies or strategies. The research results highlight the gap in the availability and frequency of data for analytics in the construction industry, the need for benchmarking the dynamics of the industry as a coupled system, and the potential for using analytics. Therefore, topics within the construction industry that require more-rigorous data collection were systematically explored. Policy-makers and strategy developers can apply the proposed framework for data-driven decision-making using their preferred set of data as well as communication of data on trends. Researchers can use this framework to further explore the dynamics of the health of the construction industry on topics such as sustainable development or the diversity of the construction project areas.