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Inefficiency in FM, can BIM help?

Inefficiency in FM, can BIM help?

Carbonari, Giulia and Stravoravdis, Spyridon (2016) Inefficiency in FM, can BIM help? In: Research papers for EuroFM's 15th research symposium at EFMC 2016. Polyteknisk Forlag, pp. 229-239. ISBN 9788750211020

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Abstract

Purpose:
The Digital Built Britain (BIM Level 3) aims to extend BIM into operation, by promoting a life cycle approach for buildings through an integrated digital environment. Nevertheless the main focus of both BIM level 2 and level 3 is mainly on design, construction and hand over, therefore the current understanding and use of BIM for facilities management is still limited. Current literature and research focusing on BIM and FM show only a marginal use of the technology, especially in terms of how BIM can be used beside maintenance. The purpose of this research is to determine which tasks, among the ones performed by facilities managers are perceived to be more inefficient. By identifying the data needed to improve the performance of tasks, the study aims to establish if building models can retain this information and increase the efficiency of FM practices.

Findings:
This paper presents the results of an online questionnaire survey aimed to ascertain the level of perceived inefficiencies in facilities management. Through the analysis of Industry Foundation Classes (IFC) data models, the research identifies the data set needed to improve inefficiencies and indicates the benefits of implementing BIM within the FM industry.

Item Type: Conference Proceedings
Title of Proceedings: Research papers for EuroFM's 15th research symposium at EFMC 2016
Uncontrolled Keywords: Facilities Management, Building Information Modelling, Industry Foundation Classes, Inefficiency.
Subjects: T Technology > TH Building construction
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Department of Built Environment
Faculty of Liberal Arts & Sciences > Sustainable Built Environment Research Group (SBERG)
Last Modified: 03 Jul 2020 14:10
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/19184

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