BIG DATA ANALYTICS FOR PREVENTIVE MAINTENANCE MANAGEMENT

Authors

  • Muhammad Najib Razali Faculty of Built Environment and Surveying, UNIVERSITI TEKNOLOGI MALAYSIA
  • Siti Hajar Othman Faculty of Built Environment and Surveying, UNIVERSITI TEKNOLOGI MALAYSIA
  • Ain Farhana Jamaludin Faculty of Built Environment and Surveying, UNIVERSITI TEKNOLOGI MALAYSIA
  • Nurul Hana Adi Maimun Faculty of Built Environment and Surveying, UNIVERSITI TEKNOLOGI MALAYSIA
  • Rohaya Abdul Jalil Faculty of Built Environment and Surveying, UNIVERSITI TEKNOLOGI MALAYSIA
  • Yasmin Mohd. Adnan Faculty of Built Environment UNIVERSITI MALAYA
  • Siti Hafsah Zulkarnain Faculty of Architecture Planning and Surveying, UNIVERSITI TEKNOLOGI MARA

DOI:

https://doi.org/10.21837/pm.v19i17.1019

Keywords:

Big data, analytics, maintenance, forecasting, Malaysia

Abstract

Maintenance data for government buildings in Putrajaya, Malaysia, consists of a vast volume of data that is divided into different classes based on the functions of the maintenance tasks. As a result, multiple interactions from stakeholders and customers are required. This necessitates the collection of data that is specific to the stakeholders and customers. Big data can also forecast for predictive maintenance purposes in maintenance management. The current data practise relies solely on well-structured statistical data, resulting in static analysis and findings. Predictive maintenance under the Big Data idea will also use non-visible data such as social media and web search queries, which is a novel way to use Big Data analytics. The metamodel technique will be used in this study to evaluate the predictive maintenance model and faulty events in order to verify that the asset, facilities, and buildings are in excellent working order utilising systematic maintenance analytics. The metamodel method proposed a predictive maintenance procedure in Putrajaya by utilising the big data idea for maintenance management data.

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References

Aruldoss, M., Lakshmi Travis, M., & Prasanna Venkatesan, V. (2014). A survey on recent research in business intelligence. Journal of Enterprise Information Management, 27(6), 831-866.

Beebe, N. H. (2019). A Bibliography of O’ Reilly & Associates and O’Reilly Media. Inc. Publishers.

Boyd, D., & Crawford, K. (2012). Critical questions for big data:

Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.

CBRE (2018) Global Viewpoint: Urban Big Data and Real Estate, retrieved from https://www.cbre.com/research-and-reports/Global-Viewpoint-Urban-Big-Data-and-Real-Estate-Markets

Desouza, K. C., & Jacob, B. (2017). Big data in the public sector: Lessons for practitioners and scholars. Administration & Society, 49(7), 1043-1064.

Dixon, R. (2007). The management task. Routledge.

Homer, R. M. W., El-Haram, M. A., & Munns, A. K. (1997). Building maintenance strategy: a new management approach. Journal of quality in maintenance engineering, 3(4), 273-280.

Jawatankuasa Pengurusan Aset Kerajaan (JPAK) (2014), Mesyuarat JP PATA,Jabatan Perdana Menteri

Kim, G. (2014). Why BIG DATA for the Smart City? CIO of Seoul Secretary General of WeGO, Seoul, Korea

Othman, S. H. (2013). Supporting domain ontology through a metamodel: A disaster management case study. In Ontology-Based Applications for Enterprise Systems and Knowledge Management (pp. 191-209). IGI Global.

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".

Razak Bin Ibrahim, A., Roy, M. H., Ahmed, Z., & Imtiaz, G. (2010). An investigation of the status of the Malaysian construction industry. Benchmarking: An International Journal, 17(2), 294-308.

Razali, M,N. , M., & Juanil, D. M. (2011). A study on knowledge management implementation in property management companies in Malaysia. Facilities, 29(9/10), 368-390.

Sprinkle, J., Rossi, M., Gray, J., & Tolvanen, J. P. (2014, October). DSM'14: the 14th workshop on domain-specific modeling. In Proceedings of the companion publication of the 2014 ACM

Susi, A., Perini, A., Mylopoulos, J., & Gi, P. (2005). The tropos metamodel and its use. Informatica, 29(4).

Thompson, O., Business Intelligence Success, Lessons Learned, 2004, Retrieved from www.technologyevaluation.com

Volter, M., Stahl, T., Bettin, J., Haase, A., & Helsen, S. (2013). Model-driven software development:technology, engineering, management. John Wiley & Sons.

Von Alan, R. H., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS quarterly, 28(1), 75-105.

Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist:

Big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34(4), 249-252.

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Published

2021-10-17

How to Cite

Razali, M. N., Othman, S. H., Jamaludin, A. F., Adi Maimun, N. H., Abdul Jalil, R., Mohd. Adnan, Y., & Zulkarnain, S. H. (2021). BIG DATA ANALYTICS FOR PREVENTIVE MAINTENANCE MANAGEMENT. PLANNING MALAYSIA, 19(17). https://doi.org/10.21837/pm.v19i17.1019

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