Enabling Technologies for Industry 4.0 Manufacturing and Supply Chain: Concepts, Current Status, and Adoption Challenges

Raut, Rakesh D.; Gotmare, Ashwini; Narkhede, Balkrishna E.; Govindarajan, Usharani Hareesh; Bokade, Sanjay U.

doi:10.1109/emr.2020.2987884

Cited by 62 publications

(34 citation statements)

References 100 publications

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“…Others choose Big Data Analytics because of the speed in detecting failures with a better customer service and reduction of preventive maintenance. Furthermore, some companies rely on Blockchain to improve the traceability and transparency, which increases trust with stakeholders [ 57 ]. Several of these technologies such as Robotics, Automated Guided Vehicles, or Additive Manufacturing help reduce wasted resources and emissions by setting up a collaborative program.…”

Section: Resultsmentioning

confidence: 99%

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

et al. 2020

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The search for sustainability in the Supply Chain (SC) is one of the tasks that most concerns business leaders in all manufacturing sectors because of the importance that the Supply Chain has as a transversal tool and due to the leading role that it has been playing lately. Of all the manufacturing sectors, this study focuses on the aerospace, shipbuilding, and automotive sectors identified as transport. The present study carries out a descriptive review of existing publications in these three sectors in relation to the sustainability of the Supply Chain in its 4.0 adaptation as an update in matters that are in constant evolution. Among the results obtained, Lean practices are common to the three sectors, as well as different technologies focused on sustainability. Furthermore, the results show that the automotive sector is the one that makes the greatest contribution in this sense through collaborative programs that can be very useful to the other two sectors, thus benefiting from the consequent applicable advantages. Meanwhile, the Aerospace and Shipbuilding sectors do not seem to be working on promoting a sustainable culture in the management of the Supply Chain or on including training programs for their personnel in matters related to Industry 4.0.

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Section: Resultsmentioning

confidence: 99%

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

et al. 2020

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“…The UCSD dataset consists of 98 videos, each of which has 200 frames. Besides the abnormal behaviours of crowds, there is another study [86] conducting an anomaly detection on abnormal vehicle-moving patterns on top of the U-turn dataset 6 . The work [87] also summarizes other datasets for anomaly detection based on visual analysis.…”

Section: A Pointmentioning

confidence: 99%

“…The Industrial Internet of Things (IIoT) is an emerging paradigm that facilitates the process of industry digital transformation [4], [5]. It allows the networked interconnection of sensors, instruments, and other Internet of Things (IoT) devices to enable data collection, data analytics and automated control in an industry environment [6]- [9]. IIoT can in turn improve the productivity and efficiency of the business in the industry, as well as the resulting economic benefits.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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“…The decreased cost of sensors and connectivity [1], along with the development of the Internet of Things, Cloud Computing, Big Data Analytics, and Blockchain technologies [2] have enabled an increasing digitalization of manufacturing and the introduction of new paradigms, such as Cyber-Physical Systems (CPS) [3,4], and Digital Twins (DTs) [5][6][7]. Moreover, they bring extensive added value to the Industry 4.0 [8], enabling more effective operations, cost saving, and better product quality [9].…”

Section: Introductionmentioning

confidence: 99%

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Zajec¹,

Rožanec²,

Trajkova³

et al. 2021

Preprint

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This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

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Enabling Technologies for Industry 4.0 Manufacturing and Supply Chain: Concepts, Current Status, and Adoption Challenges

Cited by 62 publications

References 100 publications

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

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