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The Future of Big Data Analysis in Facility Management - A Study of Implementation areas

Malcolm Granberg ; Daniel He
Göteborg : Chalmers tekniska högskola, 2018. 49 s.
[Examensarbete på avancerad nivå]

The Facility Management (FM) sector is known to be conservative and have for a long time been struggling with digitalizing their operations. Meanwhile, other sectors have seen it necessary to digitalize and implement different technologies such as Internet of Things (IoT) and Big Data Analysis (BDA) to support streamlining, growth and innovation that are required to stay competitive. BDA is an emerging technology that enables analysis of great volumes, varieties and velocity of data that an ordinary human could not manage within a feasible timeframe. The FM sector have for some time been utilizing sensors to monitor different aspects in a facility, but the data have rarely been used for any evaluation or real time analysis. However, the sector has not adopted these new technologies at the same pace as other sectors. Therefore, the purpose of this thesis was to investigate how BDA can be implemented in FM and what challenges it is facing. This was achieved by conducting interviews with companies in Sweden that are either developing areas of implementation or are working with project related to BDA in FM and was then compared with existing literature. Despite discovering contrasting opinions, the thesis has found several implementation areas where BDA could improve the FM sector, the four major areas are: 1) Energy optimization, 2) Better indoor climate, 3) Space optimization and 4) More efficient maintenance. These areas are possible to implement if the development solves several challenges of which the five major ones have been identified as: 1) Hard to calculate returns, 2) Lack of competence, 3) Lack of standards, 4) Maintaining the digital twin, and 5) Ethical and regulatory questions.

Nyckelord: Internet of Things (IoT), Big Data Analysis (BDA), Facilities Management (FM), Information, Data, Artificial Intelligence (AI), Machine Learning, industry 4.0, Implementation, Energy optimization, Space management, Indoor climate, Effective maintenance



Publikationen registrerades 2018-09-10. Den ändrades senast 2018-09-10

CPL ID: 255834

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