Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance &amp; Service Innovation

Lee, Jay; Ardakani, Hossein Davari; Yang, Shanhu; Bagheri, Behrad

doi:10.1016/j.procir.2015.08.026

Cited by 338 publications

(172 citation statements)

References 6 publications

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“…Taking the example of customer and non‐customer needs, identifications literature has documented that strong BDACs can allow companies to understand the behaviours, interactions, experiences and emerging patterns that consumers have with their products or services (Kwon, Lee and Shin, ), monitor in real time their sentiment and affect about the firm itself or specific products, services or marketing campaigns (Jang et al ., ), develop a more fine‐grained understanding of who their customers are and what they need (Fan, Lau and Zhao, ) and even help create personalized products and services (Sagiroglu and Sinanc, ). Similar cases are noted in improving operations and business processes, where strong BDACs can be leveraged to identify bottlenecks in supply chains (G. Wang et al ., ), predict maintenance times for equipment with much greater accuracy (J. Lee et al ., ) and forecast demand and sales to allow better inventory management and production planning (Lim, Alpan and Penz, ).…”

Section: Research Modelmentioning

confidence: 99%

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

Mikalef

Boura

Lekakos

et al. 2019

British J of Management

500

454

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With big data analytics growing rapidly in popularity, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. Drawing on the resource‐based view, the dynamic capabilities view, and on recent literature on big data analytics, this study examines the indirect relationship between a big data analytics capability (BDAC) and two types of innovation capabilities: incremental and radical. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which in turn positively impact incremental and radical innovation capabilities. To test their proposed research model, the authors used survey data from 175 chief information officers and IT managers working in Greek firms. By means of partial least squares structural equation modelling, the results confirm the authors’ assumptions regarding the indirect effect that BDACs have on innovation capabilities. Specifically, they find that dynamic capabilities fully mediate the effect on both incremental and radical innovation capabilities. In addition, under conditions of high environmental heterogeneity, the impact of BDACs on dynamic capabilities and, in sequence, incremental innovation capability is enhanced, while under conditions of high environmental dynamism the effect of dynamic capabilities on incremental innovation capabilities is amplified.

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Section: Research Modelmentioning

confidence: 99%

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

Mikalef

Boura

Lekakos

et al. 2019

British J of Management

500

454

View full text Add to dashboard Cite

show abstract

“…The use of MEC in factory automation should enable processing of vast amount of data, complex orchestration of cyber-physical systems, and coordination of computation as well as communication resources in real-time [50]. For our work, MEC represents a promising approach to achieve the low latencies required for many industrial applications.…”

Section: Multi-access Edge Computing (Mec) Is a New Official Name Formentioning

confidence: 99%

6DoF Object Tracking based on 3D Scans for Augmented Reality Remote Live Support

et al. 2018

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Abstract:Tracking the 6DoF pose of arbitrary 3D objects is a fundamental topic in Augmented Reality (AR) research, having received a large amount of interest in the last decades. The necessity of accurate and computationally efficient object tracking is evident for a broad base of today's AR applications. In this work we present a fully comprehensive pipeline for 6DoF Object Tracking based on 3D scans of objects, covering object registration, initialization and frame to frame tracking, implemented to optimize the user experience and to perform well in all typical challenging conditions such as fast motion, occlusions and illumination changes. Furthermore, we present the deployment of our tracking system in a Remote Live Support AR application with 3D object-aware registration of annotations and remote execution for delay and performance optimization. Experimental results demonstrate the tracking quality, real-time capability and the advantages of remote execution for computationally less powerful mobile devices.

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“…The maintenance plan is constantly adapted according to the machine status and work schedule. Lee et al [99] are showing the impact of industrial big data analytics and CPS for the future maintenance and service innovation. Moreover, during execution of maintenance operations, information from the big database assists the maintenance workers.…”

Section: Future Of Continuous Maintenance Within the Industry 40 Conmentioning

confidence: 99%

Continuous maintenance and the future – Foundations and technological challenges

et al. 2016

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Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation

Cited by 338 publications

References 6 publications

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

6DoF Object Tracking based on 3D Scans for Augmented Reality Remote Live Support

Continuous maintenance and the future – Foundations and technological challenges

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