Business Intelligence to support management in civil construction: a systematic literature review
DOI:
https://doi.org/10.5380/atoz.v9i1.72574Keywords:
Business Intelligence, Computational Tools, Data Analysis, Building Works, Systematic Literature Review.Abstract
Introduction: Management is essential for meeting the requirements of a project and the computational tools of Business Intelligence (BI) have great potential to contribution, providing management information about the business. Tools of this type are used in several sectors of the industry, but in civil construction, the focus of this work, the scenario is different, with much to aadvance. Therefore, a survey of BI tools applicable to the construction sector and its uses ar presented. Method: It conducts a systematic review of the literature, which analyzed 595 articles from six databases (ACM, Engineering Village, IEEE, Material Science Engineering, Science Direct, Scopus and Web of Science). Results: It identifies12 different applications, mainly in cost management, budget and job security. In the applications, uses of Data Warehouse and OLAP technologies were evidenced. In addition, it was found that most BI tools were developed for each company to the detriment of commercial software. Conclusion: There are several BI tools for civil construction, with different purposes. Most softwares were developed for each case studied due to the unique characteristics of the construction sector. It is believed that the large-scale adoption of the tools involves cooperation between companies, class entities and universities. Limitations were found in the research regarding the characterization of companies, due to the absence of this data in the analyzed articles. Finally, it is suggested that the challenges of implementing the technologies and the verified limitations can be addressed in future studies.
References
Ahmad, I., Azhar, S., & Lukauskis, P. (2004). Development of a decision support system using data warehousing to assist builders/developers in site selection. Automation in Construction, 13(4), 525–542. doi: 10.1016/j.autcon.2004.03.001.
Cao, Y., Chau, K. W., Anson, M., & Zhang, J. (2002). An Intelligent Decision Support System in Construction Management by Data Warehousing Technique. 360–369. doi: 10.1007/3-540-45785-2_28.
Chau, K. W., Anson, M., Ying, C., & Jianping, Z. (2003). Integration of data warehouse into knowledge-based system on construction management decision making. HKIE Transactions Hong Kong Institution of Engineers, 10(1), 8–13. doi: 10.1080/1023697X.2003.10667895.
Chau, K. W., Anson, M., & Zhang, J. P. (2005). 4D dynamic construction management and visualization software: 1. Development. Automation in Construction, 14(4), 512–524. doi: 10.1016/j.autcon.20.11.002.
Chau, K. W., Cao, Y., Anson, M., & Zhang, J. (2003). Application of data warehouse and decision support system in construction management. Automation in Construction, 12(2), 213–224. doi: 10.1016/S0926-5805(02)00087-0.
Chen, H., Chiang, R., & Storey, V. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36, 1165–1188. doi: 10.2307/41703503.
Cheng, C. W., Lin, C. C., & Leu, S. S. (2010). Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry. Safety Science, 48(4), 436–444. doi: 10.1016/j.ssci.2009.12.005.
Chong, H. Y., & Phuah, T. H. (2013). Incorporation of database approach into contractual issues: Methodology and practical guide for organizations. Automation in Construction, 31, 149–157. doi: 10.1016/j.autcon.2012.10.007.
Gajendran, T., & Brewer, G. (2012). Cultural consciousness and the effective implementation of information and communication technology. Construction Innovation, 12(2), 179–197. doi: 10.1108/14714171211215930.
Girsang, A. S., Isa, S. M., Saputra, H., Nuriawan, M. A., Ghozali, R. P., & Kaburuan, E. R. (2018). Business Intelligence for Construction Company Acknowledgement Reporting System. Proceedings of 1st 2018 Indonesian Association for Pattern Recognition International Conference, INAPR 2018, 113–122. doi: 10.1109/INAPR.2018.8627012.
Gowthami, S., & Venkatakrishnakumar, S. (2016). Impact of Smartphone : A pilot study on positive and negative effects. International Journal of Scientific Engineering and Applied Science, 3(2), 2395–3470.
Hammad, A., AbouRizk, S., & Mohamed, Y. (2014). Application of KDD Techniques to Extract Useful Knowledge from Labor Resources Data in Industrial Construction Projects. Journal of Management in Engineering, 30(6), 05014011. doi: 10.1061/(asce)me.1943-5479.0000280.
IBGE. (2020). Instituro Brasileiro de Geografia e Estatística - Contas nacionais trimestrais de 2019. Rio de Janeiro: IBGE.
Kitchenham, B. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Biomedical and Environmental Sciences, 13(1), 37–43. doi: 10.1145/1134285.1134500.
Konikov, A. (2018). A selective study of Information technologies to improve operations efficiency in construction. MATEC Web of Conferences, 170, 01110. doi: 10.1051/matecconf/201817001110.
Konikov, A., Kulikova, E., & Stifeeva, O. (2018). Research of the possibilities of application of the Data Warehouse in the construction area. MATEC Web of Conferences, 251, 03062. doi: 10.1051/matecconf/201825103062.
Li, Y., & Zhang, X. (2013). Web-based construction waste estimation system for building construction projects. Automation in Construction, 35, 142–156. doi: 10.1016/j.autcon.2013.05.002.
Lu, Y., Li, Y., Skibniewski, M., Wu, Z., Wang, R., & Le, Y. (2014). Information and communication technology applications in architecture, engineering, and construction organizations: A 15-year review. Journal of Management in Engineering, 31(1), 1–19. doi: 10.1061/(ASCE)ME.1943-5479.0000319.
Ma, L., Luo, H. Bin, & Chen, H. R. (2013). Safety risk analysis based on a geotechnical instrumentation data warehouse in metro tunnel project. Automation in Construction, 34, 75–84. doi: 10.1016/j.autcon.2012.10.009.
Ma, Z., Lu, N., & Wu, S. (2011). Identification and representation of information resources for construction firms. Advanced Engineering Informatics, 25(4), 612–624. https://doi.org/10.1016/j.aei.2011.08.008.
Martínez-Rojas, M., Marin, N., & Amparo Vila, M. (2012). The Role of Information Technologies to Address Data Handling in Construction Project Management. Journal of Computing in Civil Engineering, 30(4), 1–11. doi: 10.1061/(ASCE)CP.1943-5487.
Martínez-Rojas, M., Marín, N., & Miranda, M. A. V. (2016). An intelligent system for the acquisition and management of information from bill of quantities in building projects. Expert Systems with Applications, 63, 284–294. doi: 10.1016/j.eswa.2016.07.011.
Montaser, A. & Montaser, A. (2017). Web Based Project Integrated Controls System. 2017 Proceedings of the 34rd ISARC. Taipei, Taiwan.
Moon, S. W., Kim, J. S., & Kwon, K. N. (2007). Effectiveness of OLAP-based cost data management in construction cost estimate. Automation in Construction, 16(3), 336–344. doi: 10.1016/j.autcon.2006.07.008.
Muntean, M., & Surcel, T. (2013). Agile BI: The Future of BI. Informatica Economica, 17(3), 114–124. doi: 10.12948/issn14531305/17.3.2013.10.
Negash, S., & Gray, P. (2008). Business Intelligence. In F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems 2 (pp. 175-193). Berlin: Springer.
Rezaei, A. R., Çelik, T., & Baalousha, Y. (2011). Performance measurement in a quality management system. Scientia Iranica, 18(3 E), 742–752. doi: 10.1016/j.scient.2011.05.021.
Rezgui, Y. (2001). Review of information and the state of the art of knowledge management practices in the construction industry. Knowledge Engineering Review, 16(3), 241–254. doi: 10.1017/S026988890100008X.
Rujirayanyong, T., & Shi, J. J. (2005). Company-Wide Project Data Integration for a Construction Organization (pp.1–10). doi: 10.1061/40754(183)85.
Rujirayanyong, T., & Shi, J. J. (2006). A project-oriented data warehouse for construction. Automation in Construction, 15(6), 800–807. doi: 10.1016/j.autcon.2005.11.001.
Sapateiro, C., & Rui, B. (2019). Bringing Human Factor to Business Intelligence. 11th Ineka Conference. Verona.
Szelka, J., & Wrona, Z. (2010). Application of Analytic Databases to Support Decision Making in Structural Engineering / Zastosowanie Analitycznych Baz Danych Przy Podejmowaniu Decyzji W Obszarze Budownictwa Ladowego. Archives of Civil Engineering, 56(2), 173–192. doi: 10.2478/v.10169-010-0009-6.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14, 207–222.
Vuori, V. (2007). Business intelligence activities in construction companies in Finland-A series of case studies. Proceedings of the European Conference on Knowledge Management, ECKM, (November), 1086–1092.
Wang, Q., Xi, L., & Gao, K. (2009). Application of Business Intelligence in the information development of Construction Enterprise. 5th International Conference on Natural Computation, ICNC 2009, 6(3), 212–215. doi: .10.1109/ICNC.2009.674.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future : Writing a literature review R. MIS Quarterly, 26(2), 13–23. doi: 10.2307/4132319.
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