Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Konanahalli, Ashwini; Marinelli, Marina; Oyedele, Lukumon O.

doi:10.1109/tem.2019.2959914

Cited by 32 publications

(25 citation statements)

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“…While confirming most of the challenges found earlier, Karadayi-Usta [33] identified relationships between those challenges and investigated how each factor triggers other factors as the basis for root-cause analysis of barriers. In a similar context, Konanahalli et al [34] identified the skills gap and inadequate preparedness as significant barriers to the implementation of big data. Finally, Frank et al [35] suggested that the current advances in Industry 4.0 predominantly focus on front-end technologies while the back-end technologies are still challenging to organizations.…”

Section: Challenges and Barriers To Industry 40mentioning

confidence: 99%

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Adebanjo

Laosirihongthong

Samaranayake

et al. 2023

IEEE Trans. Eng. Manage.

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Organizations in both developed and developing economies are paying great attention to the Industry 4.0 revolution and associated uses of technologies due to its potential benefits to the manufacturing industry. However, there are a limited number of empirical studies due to its early stage of adoption around the world, especially regarding the key technological factors that are necessary. This study addresses this research gap by identifying the factors that enable successful Industry 4.0 technologies adoption in an emerging economy country, grouping them, and ranking the groups based on priorities for adoption. The study adopts a mixed-method research methodology. Q-Sort technique and Analytic Hierarchy Process (AHP) respectively were used to group enabling factors and prioritize the groups for Industry 4.0 technologies adoption. Thereafter, semi-structured interviews of key stakeholders in the manufacturing sector in Thailand were carried out to validate and support findings from the quantitative analysis. Five industry experts from automotive and electronic parts/components manufacturers were interviewed. The results show that human capital is the most important readiness dimension for Industry 4.0 technologies implementation.Interoperability and data handling were found to be the next in importance. On the contrary, hardware and technology systems such as data security and technological infrastructure were identified as the least important of the technology readiness dimensions. These findings provide a different perspective to extant studies that posited that technology-based factors as the most important for Industry 4.0 success. Managerial Relevance StatementThe rapid development of Industry 4.0 has significantly influenced manufacturing organizations' operations and decisions. As manufacturers increasingly seek to adopt Industry 4.0, it is important that they know where to direct their efforts and resources. This study emphasizes that human development issues are critical to the success of Industry 4.0. The implication for managers is that human capital must be developed in tandem with technology. The study also highlights the importance of interoperability and therefore, managers must choose Industry 4.0 systems and technology with the ability to be interoperable with other systems and/or organizations in their ecosystems. Finally, the research encourages managers to consider production flexibility and process stability as the most important performance measures for Industry 4.0 implementation.

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Section: Challenges and Barriers To Industry 40mentioning

confidence: 99%

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Adebanjo

Laosirihongthong

Samaranayake

et al. 2023

IEEE Trans. Eng. Manage.

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“…This revolution in digital transformation has revealed new developments in different generations of FM sector while there is a clear lack of tools and references. and Konanahalli et al (2018) state that because of the advancements in technologies such as big data, IoT, cloud computing, and cyber-physical systems, these developments have gained momentum. Besides, the strengths of these advancements lie in interoperability, controlling, monitoring, real-time information processing, and self-optimization processing Konanahalli, Oyedele, Marinelli, & Selim, 2018).…”

Section: Digital Transformation In Facility Managementmentioning

confidence: 99%

“…and Konanahalli et al (2018) state that because of the advancements in technologies such as big data, IoT, cloud computing, and cyber-physical systems, these developments have gained momentum. Besides, the strengths of these advancements lie in interoperability, controlling, monitoring, real-time information processing, and self-optimization processing Konanahalli, Oyedele, Marinelli, & Selim, 2018). Indeed, when discussing about digitalization revolution, it is essential to acknowledge that it is not only a technological change in the operational level; but also, it involves the creation of new probabilities for a widespread monitoring of appropriate parameters (Talamo, Atta, Martani, & Paganin, 2016).…”

Section: Digital Transformation In Facility Managementmentioning

confidence: 99%

“…Ashworth et al (2019) state that the main effort of digital transformation in the construction sector is to increase the sector's efficiency, which will be achieved by the spread of BIM (Ashworth, Tucker, & Druhmann, 2019). New market demands require companies to be flexible and innovative, while forcing them to relocate from the traditional culture to novel strategies aimed at forecasting future directions (Konanahalli, Oyedele, Marinelli, & Selim, 2018).…”

Section: Digital Transformation In Facility Managementmentioning

confidence: 99%

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A Review of Embedding Artificial Intelligence in Internet of Things and Building Information Modelling for Healthcare Facility Maintenance Management

Orooje¹,

Latifi²

2021

EER

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Latest innovations in Internet of Things (IoT) technologies as well as the new paradigms in Artificial Intelligence systems are opening up opportunities to create smart computing infrastructures for the Healthcare Facility Management. However, the current scenario of hospital buildings maintenance management is strongly characterized by slow, redundant, and not integrated processes, which lead to loss of money, resources, and time. On the other hand, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Maintenance Management. Consequently, optimization of data collection process and management is required. In this light, this paper presents a review of embedding AI (Artificial Intelligence) in BIM-IoT integration for the process of healthcare Facility Maintenance Management (FMM) in order to conquer the current challenges. The first challenge in front of integrating IoT– BIM, is the lack of information; the second challenge is BIM’s sematic information that has not been able to display indoor conditions’ elements which should be reconsidered; and the third challenge is the data size which is stored in systems as well as the eligibility of individuals to apply the related data. Additionally, some emerging trends in IoT are reviewed such as the combination of Machine Learning and Artificial Intelligence in order to exploit their advantages and complement their limitations, which enable new promising IoT applications.

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“…Companies that obtain higher technological innovation efficiency are reported to be among those that achieve the highest growth rates (Martínez-Alonso et al , 2019). As maintenance of the built environment is concerned, it is bolstering the energy efficiency of buildings, systems and devices through telemetry based on the Internet of Things and data analytics that is one of the leading technology-related trends nowadays (Konanahalli et al , 2018). It not only equips an interested company with instruments necessary to expand further in the era of rising energy carriers’ prices through eliminating energy waste; it also exerts a positive impact on the environment that cannot be brought down to positive publicity.…”

Section: Introductionmentioning

confidence: 99%

Data-driven business model development – insights from the facility management industry

Marcinkowski

Gawin

2020

JFM

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Purpose One of the leading factors that shape product and service delivery are data collected in databases and other repositories maintained by companies. The transformation of such data into knowledge and wisdom may constitute a new source of income. This paper aims to explore how small/medium-sized enterprises (SMEs) advance their business models (BMs) around data to handle data-driven products and how this contributes to their innovativeness and performance. Design/methodology/approach To investigate the phenomenon, the as-is BM of a multinational SME was mapped and its limitations were revealed through a qualitative study. The BM canvas was used. Then the data-driven approach was innovated within the facility management (FM) industry, where a high volume of operational and sensor-based data being collected creates added value in terms of new data-based products. Findings A data-driven business model (DDBM) blueprint for the FM industry that supports the need to complement service-driven operations with the data-driven approach is delivered. Enhanced BM equips a facility manager with additional managerial tools that enable decreasing property utilization costs and opens up new opportunities for generating revenue. This paper drafts the way to evolve from service to data-driven business and point out the attitudes that managers should adopt to promote and implement DDBM. Practical implications The DDBM constitutes a guideline that supports FM organizations in focusing their activities and resources on generating business value from data and monetizing data-driven products. Originality/value The research expands knowledge regarding BMs and their evolution. The gap regarding the DDBM innovation within the FM industry is filled.

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Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Cited by 32 publications

References 50 publications

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

A Review of Embedding Artificial Intelligence in Internet of Things and Building Information Modelling for Healthcare Facility Maintenance Management

Data-driven business model development – insights from the facility management industry

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