A knowledge discovery in databases approach for industrial microgrid planning

Gamarra, Carlos; Guerrero, Josep M.; Montero, Eduardo A.

doi:10.1016/j.rser.2016.01.091

Cited by 40 publications

(18 citation statements)

References 126 publications

(96 reference statements)

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“…The goal of the smart factory is to connect all smart devices with higher decision making (Dutta & Bose, 2015). This connectivity from the device level to the organizations' decision making-level connection involves connecting smart factory devices to manufacturing execution systems (MESs), energy management (Gamarra, Guerrero, & Montero, 2016). If KM in the period of its creation proceeded on the assumption that there is a benefit to knowledge upgrading, all that is needed is to capture, decode, and share.…”

Section: Iot In the Framework Of Kmmentioning

confidence: 99%

A Complex View of Industry 4.0

Roblek¹,

2016

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This article is focused on the importance and influence of Industry 4.0 and consequently the Internet-connected technologies for the creation of value added for organizations and society. The contribution of the article is mainly conceptual. With the development of the Internet, the Internet of things that is central to the new industrial revolution has led to "Industry 4.0." The aim of this article is to synthesize the known theory and practices of Industry 4.0, and to investigate the changes that will result from Industry 4.0 and with the development of the Internet of things.

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Section: Iot In the Framework Of Kmmentioning

confidence: 99%

A Complex View of Industry 4.0

Roblek¹,

2016

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“…Machines and equipment can influence processes improvement through their optimization and autonomous decision making (Roblek, Meško, & Krapež, 2016), which is enabled by connection of all smart devices. This device connectivity leads to a degree of connectivity that enables decision making at the organizational level (Gamarra, Guerrero, & Montero, 2016). Radziwon et al (2014) emphasize that smart factory is a production solution that enables both flexible and adaptable production processes that will solve problems that arise from the production in dynamic and rapidly changing boundary conditions in a world of increasing complexity.…”

Section: Concept Of Smart Factory Systemmentioning

confidence: 99%

Transformation towards smart factory system: Examining new job profiles and competencies

2019

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This paper increases understanding of the concept of smart factories system and its implementation in the automotive industry in terms of addressing necessary changes in human aspect of system, namely, employees' competencies, as well as to new job profiles. A qualitative research approach was used by utilizing a case study of the automotive industry in Slovenia. For the purpose of data collection, we used a semistructured interview on a sample of experts from governmental, educational, and private automotive sectors. In order to analyse gathered data, we used the method of content analysis. Research results indicate that significant changes are essential in the transformation from traditional to a smart factory system, especially regarding organizational mindset, the architecture of system and its processes, and the inclusion of Industry 4.0 technologies at all levels. Also, results indicate changes are essential regarding future job profiles and competencies. Although there are different perceptions about future jobs and competencies needed for Industry 4.0‐induced changes, depending on the sector in which the expert is employed, there is an overall agreement that job profiles related to programming, mechatronics, robotics, data analysis, Internet of Things, design, and maintenance of smart systems, process analysis, and bionics are the new job profiles needed in smart factory systems. In addition to the high emphasis on technical skill and knowledge, soft competencies are considered important, as well as competencies of continues learning, flexibility, creativity, problem solving, critical, and analytical thinking. Our findings possess important implications for conceptualizing job profiles and competencies in smart factories systems and practical implications for managers concerned with human resource management activities. We provide an outlook to the future job profiles that are underresearched, especially in the field of smart manufacturing. As well, our paper provides insight into the importance of different competencies recognized as crucial for complete functioning and optimal performance of smart factories system. This study provides a starting point for further research regarding human resource management issues in smart factories in the automotive sector, especially related to future job profiles and employee competencies, and their role in system performance.

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“…Massive data production in IIoT systems is evident due to feature-rich sensory and large-scale deployment of IIoTs in SFS [74]. Therefore, manufacturing and environmental data, along with energy consumption data, can lead towards optimised energy utilisation in SFS.…”

Section: Industrial Microgridsmentioning

confidence: 99%

The role of big data analytics in industrial Internet of Things

Rehman

Yaqoob

Salah

et al. 2019

Future Generation Computer Systems

287

117

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Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational-and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.

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A knowledge discovery in databases approach for industrial microgrid planning

Cited by 40 publications

References 126 publications

A Complex View of Industry 4.0

A Complex View of Industry 4.0

Transformation towards smart factory system: Examining new job profiles and competencies

The role of big data analytics in industrial Internet of Things

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