scholarly journals Pengaruh Era Revolusi Industri 4.0 terhadap Kompetensi Sumber Daya Manusia

2018 ◽  
Vol 6 (1) ◽  
pp. 114-136 ◽  
Author(s):  
Leni Rohida
Keyword(s):  
Internet Of Things ◽  
Smart Factories

Penelitian ini akan menjelaskan pengaruh era revolusi industri 4.0 terhadap kompetensi sumber daya manusia. Sumber daya manusia yang dibutuhkan oleh industri saat ini adalah yang memiliki kompetensi dalam pemanfaatan teknologi digital. Kompetensi ini untuk mewujudkan pabrik cerdas (smart factories), seperti salah satunya Internet of Things (IoT). Era revolusi industri 4.0 membuka kesempatan bagi sumber daya manusia (SDM) untuk memiliki keahlian yang sesuai dengan perkembangan teknologi terkini. Untuk itu, diperlukan pelaksanaan program peningkatan keterampilan (up-skilling) atau pembaruan keterampilan (reskilling) para sumber daya manusia berdasarkan kebutuhan dunia industri saat ini, salah satu yang kompetensi yang dibutuhkan adalah sumber daya manusia yang memiliki talent, dikarenakan talent menjadi kunci atau faktor penting untuk kesuksesan implementasi industri 4.0 Penelitian ini menggunakan metode penelitian kualitatif partisipatoris, yang menekankan pada kedalaman hasil penelitian dimana peneliti terlibat langsung sebagai instrumen penelitian. Data diperleh mealui studi literatur, wawancara dan praktek langsung. Hasil dari Penelitian ini akan menjelaskan upaya didalam mempersiapkan dan memetakan kompetensi sumber daya manusia dalam era revolusi industri 4.0. dalam menciptakan sumber daya manusia yang kompeten. Apalagi di era bonus demografi yang sudah di depan mata. Ini penting jika bangsa kita tetap ingin berdaya di zaman revolusi industri keempat  yang serba otomatis, robotik, dan tentu teknologi termutakhir.   Kata Kunci : kompetensi; sumber daya manusia, upskill, reskilling

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

scholarly journals Internet of Things in Asset Management

2018 ◽  
Vol 9 (2) ◽  
pp. 104-119 ◽  
Author(s):  
Sini-Kaisu Kinnunen ◽  
Antti Ylä-Kujala ◽  
Salla Marttonen-Arola ◽  
Timo Kärri ◽  
David Baglee
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Asset Management ◽  
Spare Parts ◽  
Future Research ◽  
Frequency Identification ◽  
Preliminary Study ◽  
Smart Factories ◽  
Factories Of The Future

The emerging Internet of Things (IoT) technologies could rationalize data processes from acquisition to decision making if future research is focused on the exact needs of industry. This article contributes to this field by examining and categorizing the applications available through IoT technologies in the management of industrial asset groups. Previous literature and a number of industrial professionals and academic experts are used to identify the feasibility of IoT technologies in asset management. This article describes a preliminary study, which highlights the research potential of specific IoT technologies, for further research related to smart factories of the future. Based on the results of literature review and empirical panels IoT technologies have significant potential to be applied widely in the management of different asset groups. For example, RFID (Radio Frequency Identification) technologies are recognized to be potential in the management of inventories, sensor technologies in the management of machinery, equipment and buildings, and the naming technologies are potential in the management of spare parts.

scholarly journals A Framework for Off-Line Operation of Smart and Traditional Devices of IoT Services

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6012 ◽  
Author(s):  
Chung-Yen Wu ◽  
Kuo-Hsuan Huang
Keyword(s):  
Internet Of Things ◽  
Living Environment ◽  
Smart Devices ◽  
System Operation ◽  
Remote Areas ◽  
Building Information ◽  
Smart Living ◽  
Smart Factories ◽  
Installation Cost

Recently, with the continuous evolution of information technology, various products such as Building Information, Internet of Things (IoT), Big Data, Cloud Computing and Machine Learning have been developed and have created a lifestyle change. A smart Internet of Things (IoT) system is formed by combining the communication capabilities of the internet with control, monitoring and identification services to integrate people, things and objects. However, in some IoT environments that have a weak signal, such as remote areas, warehouses or basements, the network may become unstable, meaning that the IoT system is unable to provide efficient services. This paper therefore presents a framework that ensures the reliability of IoT system services so that even if the IoT system cannot connect to the network, the system can provide the services offline. To avoid increasing the installation cost or replacing existing traditional devices with modern smart devices, this framework can also be used to control traditional devices. The system operation is convenient because users can operate all their smart and traditional devices under the IoT system through voice commands and/or a handheld microcontroller, thus reducing the manual operation of the user. The framework proposed in this paper can be applied to various smart scenarios, including smart warehouses, smart restaurants, smart homes, smart farms and smart factories, to improve people’s quality of life and convenience, and create a humane and comfortable smart living environment.

scholarly journals Models for Internet of Things Environments—A Survey

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 487
Author(s):  
Ana Cristina Franco da Silva ◽  
Pascal Hirmer
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Smart Cities ◽  
Self Organization ◽  
Context Aware ◽  
Sensors And Actuators ◽  
Relational Models ◽  
Comparative Survey ◽  
Smart Factories ◽  
The Internet Of Things

Today, the Internet of Things (IoT) is an emerging topic in research and industry. Famous examples of IoT applications are smart homes, smart cities, and smart factories. Through highly interconnected devices, equipped with sensors and actuators, context-aware approaches can be developed to enable, e.g., monitoring and self-organization. To achieve context-awareness, a large amount of environment models have been developed for the IoT that contain information about the devices of an environment, their attached sensors and actuators, as well as their interconnection. However, these models highly differ in their content, the format being used, for example ontologies or relational models, and the domain to which they are applied. In this article, we present a comparative survey of models for IoT environments. By doing so, we describe and compare the selected models based on a deep literature research. The result is a comparative overview of existing state-of-the-art IoT environment models.

Preparation of Raspberry Pi for IoT-Enabled Applications

2018 ◽  
pp. 264-308 ◽  
Author(s):  
Manoj Devare
Keyword(s):  
Internet Of Things ◽  
Low Power ◽  
Data Exchange ◽  
Raspberry Pi ◽  
The Internet ◽  
Virtual Agents ◽  
Digital Twins ◽  
Step Procedure ◽  
Smart Factories ◽  
The Internet Of Things

This chapter shares the experiences in systematic, well-tested, and executed step-by-step procedure for the preparation of the Raspberry Pi single board computer (SBC) for the internet of things (IoT)-enabled applications. This chapter is useful for beginners and professionals working for automation of smart factories with the help of IoT and Cloud. Moreover, interesting data exchange techniques like low power wireless alternatives ZigBee, LORA, BLE, 6LowPAN, SigFox, and multi-queue telemetry transport (MQTT) are also stated. The related IoT preceding and succeeding technologies, like machine-to-machine(M2M), cyber-physical-systems (CPS), web of things (WoT), SCADA are also the part of insights. Various supporting technologies for the success of IoT like commercial and open source IoT cloud platforms, virtual agents(VA), and digital twins are also discussed.

Internet of Things in Asset Management

2019 ◽  
pp. 1490-1507
Author(s):  
Sini-Kaisu Kinnunen ◽  
Antti Ylä-Kujala ◽  
Salla Marttonen-Arola ◽  
Timo Kärri ◽  
David Baglee
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Asset Management ◽  
Spare Parts ◽  
Future Research ◽  
Frequency Identification ◽  
Preliminary Study ◽  
Smart Factories ◽  
Factories Of The Future

The emerging Internet of Things (IoT) technologies could rationalize data processes from acquisition to decision making if future research is focused on the exact needs of industry. This article contributes to this field by examining and categorizing the applications available through IoT technologies in the management of industrial asset groups. Previous literature and a number of industrial professionals and academic experts are used to identify the feasibility of IoT technologies in asset management. This article describes a preliminary study, which highlights the research potential of specific IoT technologies, for further research related to smart factories of the future. Based on the results of literature review and empirical panels IoT technologies have significant potential to be applied widely in the management of different asset groups. For example, RFID (Radio Frequency Identification) technologies are recognized to be potential in the management of inventories, sensor technologies in the management of machinery, equipment and buildings, and the naming technologies are potential in the management of spare parts.

scholarly journals Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771987937
Author(s):  
Sangwoo Park ◽  
Kim Changgyun ◽  
Sekyoung Youm
Keyword(s):  
Data Analysis ◽  
Internet Of Things ◽  
Die Casting ◽  
Smart Factory ◽  
Analysis Model ◽  
Data Mining Technique ◽  
Casting Parameter ◽  
The Government ◽  
Casting Industry ◽  
Smart Factories

In this research, an Internet of things–based smart factory was established for a die-casting company that produces automobile parts, and the effect of casting parameters on quality was analyzed using data collected from the system. Most of the die-casting industry in Korea consists of small- and medium-sized enterprises with inferior finances and skeptical views about the establishment of a smart factory. In response, the Korean government is providing various types of support to spread the implementation of smart factories for small- and medium-sized enterprises. Although small- and medium-sized enterprises have become more active in establishing smart factories according to the government policies, the effect of smart factories requires real-time monitoring. A monitoring system has been built but the data collected are not being utilized properly. Therefore, it is necessary to establish a system suitable for the die-casting environment and data analysis purposes and to utilize it to enable the analysis of data. To this end, we established to smart factory that provides data based on the Internet of things. Among the data collected, casting parameter data were analyzed through a data mining technique to establish a relationship between casting parameters and the quality of production. It is expected that a method of systematic implementation will be provided to die-casting companies that want to build smart factories in the future and that a plan for managing casting parameter by-product will be established. In addition, algorithms that can solve the problem of multi-collinearity among the casting parameters and aid in the development of new products are needed to detect optimum casting parameters.

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