skip to main content
10.1145/3468013.3468304acmotherconferencesArticle/Chapter ViewAbstractPublication PagesapcoriseConference Proceedingsconference-collections
research-article

Identification of E-Maintenance Dominant Factor that Affect Maintenance Performance of High Rise Buildings

Published:27 November 2022Publication History

ABSTRACT

In order to improve maintenance performance, several buildings have been implemented e-maintenance system. E-maintenance system itself is a combination of two elements of building maintenance, there are (a) management maintenance consisting of policy, guidelines, and Work Breakdown Structure (WBS), and (b) technology maintenance consisting of information system and Building Information Modeling (BIM). However, during its operational period, currently the implementation of e-maintenance system for high-rise building is considered not to be carried out effectively. In order to adress this concern, it is necessary to find out the factors of e-maintenance system of high rise building that affect the results of maintenance. This study proposes identification of e-maintenance system factors that affect maintenance performance of high-rise building and find out the dominant factor. This study combines the optimization factors of each of the e-maintenance components. The analytical method used is descriptive analysis which produces 26 factors which then tabulated and categorized to determine the dominant factors. The research resulted in five factor categories of e-maintenance system for high rise buildings that affect its maintenance performance, those are risk factor, operational factor, resources factor, corporate culture factor, and technology factor. The result of this study is suggested that the dominant factors of e-maintenance system for high rise buildings that affect the performance of its maintenance is risk factor.

References

  1. T. Han and B. S. Yang, “Development of an e-maintenance system integrating advanced techniques,” Comput. Ind., vol. 57, no. 6, pp. 569–580, 2006, doi: 10.1016/j.compind.2006.02.009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. L. Yan, C. Lu, and N. Y. C. Andrew, “An agent-based platform for web-enabled equipment predictive maintenance,” Proc. - 2005 IEEE/WIC/ACM Int. Conf. Intell. Agent Technol. IAT’05, vol. 2005, pp. 132–135, 2005, doi: 10.1109/IAT.2005.38.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Shah Ali, “Cost decision making in building maintenance practice in Malaysia,” J. Facil. Manag., vol. 7, no. 4, pp. 298–306, 2009, doi: 10.1108/14725960910990044.Google ScholarGoogle ScholarCross RefCross Ref
  4. N. I. Wardahni, L. S. Riantini, Y. Latief, and R. A. Machfudiyanto, “Identification of e-maintenance elements and indicators that affect maintenance performance of high rise building: A literature review,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1007, no. 1, 2020, doi: 10.1088/1757-899X/1007/1/012021.Google ScholarGoogle Scholar
  5. I. M. Shohet, “Key performance indicators for maintenance of health-care facilities,” Facilities, vol. 21, no. August, pp. 5–12, 2003, doi: 10.1108/02632770310460496.Google ScholarGoogle ScholarCross RefCross Ref
  6. P. Muchiri, L. Pintelon, L. Gelders, and H. Martin, “Development of maintenance function performance measurement framework and indicators,” Int. J. Prod. Econ., vol. 131, no. 1, pp. 295–302, 2011, doi: 10.1016/j.ijpe.2010.04.039.Google ScholarGoogle ScholarCross RefCross Ref
  7. J. D. Campbell, “Strategies for Excellence in Maintenance Management Section 3 Planning and Scheduling Resources,” 2015.Google ScholarGoogle Scholar
  8. A. Garg and S. G. Deshmukh, “Maintenance management: Literature review and directions,” J. Qual. Maint. Eng., vol. 12, no. 3, pp. 205–238, 2006, doi: 10.1108/13552510610685075.Google ScholarGoogle ScholarCross RefCross Ref
  9. S. J. L. Chua, N. B. Zubbir, A. S. Ali, and C. P. Au-Yong, “Maintenance of high-rise residential buildings,” Int. J. Build. Pathol. Adapt., vol. 36, no. 2, pp. 137–151, 2018, doi: 10.1108/IJBPA-09-2017-0038.Google ScholarGoogle ScholarCross RefCross Ref
  10. S. H. Ding and S. Kamaruddin, “Maintenance policy optimization—literature review and directions,” Int. J. Adv. Manuf. Technol., vol. 76, no. 5–8, pp. 1263–1283, 2015, doi: 10.1007/s00170-014-6341-2.Google ScholarGoogle ScholarCross RefCross Ref
  11. M. Florkowski and J. Li, Condition Monitoring and Diagnostics, vol. 27, no. 6. 2020.Google ScholarGoogle Scholar
  12. R. S. Velmurugan and T. Dhingra, Maintenance strategy selection and its impact in maintenance function: A conceptual framework, vol. 35, no. 12. 2015.Google ScholarGoogle Scholar
  13. D. Oprea and G. Mesnita, “The information systems documentation-another problem for project management,” Manag. Inf. Digit. Econ. Issues Solut. - Proc. 6th Int. Bus. Inf. Manag. Assoc. Conf. IBIMA 2006, pp. 332–338, 2006.Google ScholarGoogle Scholar
  14. Rifky Febrihanuddin Azis, “Manajemen Risiko Kebijakan Infrastruktur Pembangunan Di Institut Teknologi Sumatera,” Journalbalitbangdalampung.Org, vol. 8, 2020, [Online]. Available: https://journalbalitbangdalampung.org/index.php/jip/article/view/180/139.Google ScholarGoogle Scholar
  15. A. Cerezo-Narváez, A. Pastor-Fernández, M. Otero-Mateo, and P. Ballesteros-Pérez, Integration of cost and work breakdown structures in the management of construction projects, vol. 10, no. 4. 2020.Google ScholarGoogle Scholar
  16. J. M. Simões, C. F. Gomes, and M. M. Yasin, “Changing role of maintenance in business organisations: Measurement versus strategic orientation,” Int. J. Prod. Res., vol. 54, no. 11, pp. 3329–3346, 2016, doi: 10.1080/00207543.2015.1106611.Google ScholarGoogle ScholarCross RefCross Ref
  17. V. MEDAKOVIĆ and B. MARIĆ, “A model of management information system for technical system maintenance.,” Acta Tech. Corvininesis - Bull. Eng., vol. 11, no. 3, pp. 85–90, 2018, [Online]. Available: http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=131028257&lang=es&site=ehost-live.Google ScholarGoogle Scholar
  18. K. C. Laudon and J. P. Laudon, Management Information Systems Managing The digital Firm Thirteen Edition Global Edition (SIB). 2014.Google ScholarGoogle Scholar
  19. J. W. Ross, C. M. Beath, and D. L. Goodhue, “Develop long-term competitiveness through IT assets,” IEEE Eng. Manag. Rev., vol. 26, no. 2, pp. 37–47, 1998.Google ScholarGoogle Scholar
  20. D. Ahn and H. Cha, “Integration of Building Maintenance Data in Application of Building Information Modeling (BIM),” J. Build. Constr. Plan. Res., vol. 02, no. 02, pp. 166–172, 2014, doi: 10.4236/jbcpr.2014.22015.Google ScholarGoogle Scholar
  21. S. Liu, B. Xie, L. Tivendal, and C. Liu, “Critical Barriers to BIM Implementation in the AEC Industry,” Int. J. Mark. Stud., vol. 7, no. 6, p. 162, 2015, doi: 10.5539/ijms.v7n6p162.Google ScholarGoogle ScholarCross RefCross Ref
  22. T. R. Devi and V. S. Reddy, “Work Breakdown Structure of the Project,” Int. J. Eng. Res. Appl., vol. 2, no. 2, pp. 683–686, 2012.Google ScholarGoogle Scholar

Index Terms

  1. Identification of E-Maintenance Dominant Factor that Affect Maintenance Performance of High Rise Buildings

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      APCORISE '21: Proceedings of the 4th Asia Pacific Conference on Research in Industrial and Systems Engineering
      May 2021
      672 pages
      ISBN:9781450390385
      DOI:10.1145/3468013

      Copyright © 2021 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 November 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate68of110submissions,62%
    • Article Metrics

      • Downloads (Last 12 months)8
      • Downloads (Last 6 weeks)0

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format