Exploratory Framework for Application of Analytics in the Construction Industry Nader Naderpajouh, M.ASCE 1 ; Juyeong Choi 2 ; and Makarand Hastak, M.ASCE 3 Abstract: 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. DOI: 10.1061/(ASCE)ME.1943-5479.0000409. © 2015 American Society of Civil Engineers. Author keywords: Purdue index for construction; Data analysis; Policies; Data communication; Sustainable development; Economic factors; Social factors. Introduction The complexity of the construction industry as a loosely-coupled system (Dubois and Gadde 2002) is apparent in the diversity of the actors and their interactions as well as the diverse specialties involved and the nonrepetitive nature of construction projects (Pries and Janszen 1995; Baccarini 1996; Fernandez Solis 2008). The complexity of the construction industry is coupled with its signifi- cance in the global economy. Past studies suggested the importance of the construction industry to economic development, albeit more significant in impact in the developing rather than developed contexts (Turin 1978; Wells 1985; Bon 1992; Raftery et al. 1998; Ruddock and Lopes 2006; Giang and Pheng 2011). In the United States alone, the construction industry accounted for approximately 3.82% of the gross domestic product (GDP) (BEA 2014) and pro- vided 9.27 million jobs in 2013 (BLS 2014a). Regardless of the magnitude of its contribution to the GDP, the importance of the construction industry is highlighted by the vitality of its output to address market dynamics in events such as recessions (Gregori and Pietroforte 2015). Observing the dynamics of this coupled system using a multifaceted approach could address the need of the indus- try to better understand its complex nature. Data-driven analytics has gained considerable attention in recent years due to their ability to enable identification of trends and patterns of dynamics for business intelligence. The major goal of this surge is to promote data-driven strategy development and policy-making (i.e., informed decisions based on the analysis of data on trends and patterns of complex dynamics). Data-driven decision-making substantially improves strategies and policies, enables informed decisions, minimizes risks, and reveals hidden valuable insights (Manyika et al. 2011), especially in complex con- texts such as the construction industry. However, trend analysis in construction has traditionally focused on financial dynamics and cost trends with special emphasis on project-level analysis. Several studies have explored the costs, associated variations, and trends in the construction industry (Hwang 2009; Ashuri and Lu 2010; Xu and Moon 2011; Cao et al. 2015) specifically focused on project budget management while numerous other research studies have focused on financial trends (Yee and Cheah 2006; Jung et al. 2012b; Zilke and Taylor 2015; Yoon et al. 2015; Chiang et al. 2015). The focus on financial and cost issues may not be sufficient to develop long-term and comprehensive strategies (as sequences of actions) or policies (as directions of actions). Long- term policies and strategies should include broader issues such as competitiveness and productivity and thereby expand the analysis to the division of labor and specialties as suggested by classical economists; increasing the emphasis on physical capacity invest- ments as suggested by neoclassical economists; and capacity build- ing through education, training, and technological progress (Momaya and Selby 2009; Jung et al. 2012a; Deng et al. 2013; Schwab 2013). A prerequisite to data-driven decision-making is 1 Visiting Assistant Professor, Lyles School of Civil Engineering, Purdue Univ., West Lafayette, IN 47907-2051 (corresponding author). E-mail: nnp@purdue.edu 2 Ph.D. Student, Lyles School of Civil Engineering, Purdue Univ., West Lafayette, IN 47907-2051. E-mail: choi287@purdue.edu 3 Professor and Head, Division of Construction Engineering and Management; and Professor, Lyles School of Civil Engineering, Purdue Univ., West Lafayette, IN 47907-2051. E-mail: hastak@purdue.edu Note. This manuscript was submitted on April 30, 2015; approved on August 7, 2015; published online on October 13, 2015. Discussion period open until March 13, 2016; separate discussions must be submitted for individual papers. This paper is part of the Journal of Management in Engineering, © ASCE, ISSN 0742-597X. © ASCE 04015047-1 J. Manage. Eng. J. Manage. Eng., 2016, 32(2): 04015047 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 11/08/17. Copyright ASCE. For personal use only; all rights reserved.