Proving the Business Case for Building Analytics

Kramer, Hannah; Lin, Guanjing; Curtin, Claire; Crowe, Eliot; Granderson, Jessica

doi:10.2172/1695762

Cited by 12 publications

(14 citation statements)

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“…Given the technical and business complexity of Industry 4.0 technologies and its novelty, the external consultants supporting these implementation projects should have even more insights and skills than usual. It is important to establish partnerships with external consultants who are able to serve and support many possible functions such as data management, benchmarking, analytics and network integration (Granderson et al, 2020).…”

Section: Data Drivenmentioning

confidence: 99%

“…There is a high dependency on the external vendors and consultants to support the deployment of technology as well as to upskill internal employees. A suitable partner and a collaboration model with the right expertise and technological systems will go a long way in ensuring success and benefits (Granderson et al, 2020). Additionally, to improve service capabilities, data analytics service providers might further strengthen their processes and enhance resources (Kalaitzi and Tsolakis, 2022).…”

Section: Data Driven Predictive Maintenancementioning

confidence: 99%

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Data driven predictive maintenance for large-scale asset-heavy process industries in Singapore

Karippur,

Balaramachandran,

John

2024

JMTM

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PurposeThis paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.Design/methodology/approachThe TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.FindingsThis research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.Practical implicationsThis paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.Originality/valueThis research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

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Section: Data Drivenmentioning

confidence: 99%

Section: Data Driven Predictive Maintenancementioning

confidence: 99%

Data driven predictive maintenance for large-scale asset-heavy process industries in Singapore

Karippur,

Balaramachandran,

John

2024

JMTM

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“…The analytics layer includes software that analyzes the data collected by the automation layers to provide the user with actionable insight about the building's operation. Two common types of analytics platforms are energy information systems, including those that provide visualizations of energy use, and platforms that provide automated fault detection and diagnosis, which are systems that use logic to identify whether building equipment is running properly (Kramer et al 2020). There are a growing number of analytics companies, many with software that sits on top of most BASs, and there are also vertically integrated control companies that offer products for each layer of the architecture shown in Figure 2.…”

Section: Figure 2 Summary Of Conceptualized Building Automation Syste...mentioning

confidence: 99%

Commercial Building Sensors and Controls Systems - Barriers, Drivers, and Costs

Trenbath

Meyer

Woldekidan

et al. 2022

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“…Especially in large buildings, Building Automation Systems (BAS) are used to control and manage the complex dependencies between many components. While a BAS could in principle help with an early detection of functional and energy related faults, today they are often only designed to raise alarms for the most basic error messages sent from the component level [1]. In addition, the available measurement data is often not further processed or even stored to help on-site personnel in detecting and diagnosing such faults.…”

Section: Introductionmentioning

confidence: 99%

A mobile measurement solution for fault detection and diagnosis in buildings

Rist

Ihlenburg

Réhault

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Buildings are equipped with increasingly complex Heating, Ventilation and Air Conditioning (HVAC) systems whose energy efficiency can degrade over time because of unnoticed faults. Facility managers (FM) often do not have the time and adapted tools to identify and correct issues. Building Automation Systems generate a multitude of measurement data that can be used to identify operational malfunctions at an early stage by applying fault detection and diagnosis (FDD) methods. However, this data itself can be hardly accessible and contain errors for example due to the degradation of the sensor quality over time. These issues limit the application of analytic methods like FDD in existing buildings. In this paper, we present a prototype implementation of a mobile measurement system based on Internet of Things (IoT) technologies that functions together with an automated FDD system to detect operational faults in HVAC systems independently of the existing measurement infrastructure. Additionally, the solution provides a user interface giving a quick overview on found faults and displaying them in spatial relation with the HVAC system through an integrated BIM visualization. Here, we describe the technical details and the required conditions for the use of our prototype and show the results of an exemplary implementation on an air handling unit.

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Proving the Business Case for Building Analytics

Cited by 12 publications

References 0 publications

Data driven predictive maintenance for large-scale asset-heavy process industries in Singapore

Data driven predictive maintenance for large-scale asset-heavy process industries in Singapore

Commercial Building Sensors and Controls Systems - Barriers, Drivers, and Costs

A mobile measurement solution for fault detection and diagnosis in buildings

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