industry 4.0
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Technovation ◽  
2022 ◽  
Vol 114 ◽  
pp. 102443
Author(s):  
Monica Cugno ◽  
Rebecca Castagnoli ◽  
Giacomo Büchi ◽  
Marco Pini
Keyword(s):  

2022 ◽  
Vol 8 ◽  
pp. 334-361
Author(s):  
Tanveer Ahmad ◽  
Hongyu Zhu ◽  
Dongdong Zhang ◽  
Rasikh Tariq ◽  
A. Bassam ◽  
...  

2022 ◽  
Vol 176 ◽  
pp. 121483
Author(s):  
Ajith Tom James ◽  
Girish Kumar ◽  
Pushpal Tayal ◽  
Ashwin Chauhan ◽  
Chirag Wadhawa ◽  
...  

Author(s):  
Aristo Surya Gunawan ◽  
Ati Cahayani

Industry 4.0 brings a challenge in terms of labor reduction. However, there is an opportunity in job creation for the creative industry/economy. The creative economy in Indonesia shows an increasingly contribution to the Indonesian economy in recent years, and it is also predicted to continue increasing in the future. Leadership for the entrepreneur is needed to make their business entity (Micro Small Medium Enterprises) to survive and succeed. This research aims to find out whether there is a difference in entrepreneurial leadership style with four demographic variables, i.e., gender, age, education level, and marital status.


Author(s):  
Nurshahrily Idura Ramli ◽  
Mohd Izani Mohamed Rawi ◽  
Fatin Nur Nabila Rebuan

Today, in the realm of Industry 4.0, vastly diverse Internet of Things (IoT) technology are integrated everywhere, not to mention included in academic programs in schools and universities. Domain ratio of the final year projects in Universiti Teknologi Mara exposes a staggering hype in IoT as compared to other domains despite not having IoT included in any of the courses. Meanwhile, to fulfill the needs of the student in exploring this technology, an integrated IoT learning platform is developed. It integrates an IoT smart home model and a web-based interface as a learning platform to inspire hands-on learning for the students. The raspberry pi, motion sensor, analog gas sensor, atmospheric sensor, ultrasonic proximity sensor, and rain detector sensor are integrated together in a Lego-built smart home model where its connectivity and readings are displayed in a simple web interface to enable and inspire learning. A manual to set up the entire model is also prepared as a guide for students to set up and further explore the functionalities and operabilities of “things”.


Industry 4.0 brings a challenge in terms of labor reduction. However, there is an opportunity in job creation for the creative industry/economy. The creative economy in Indonesia shows an increasingly contribution to the Indonesian economy in recent years, and it is also predicted to continue increasing in the future. Leadership for the entrepreneur is needed to make their business entity (Micro Small Medium Enterprises) to survive and succeed. This research aims to find out whether there is a difference in entrepreneurial leadership style with four demographic variables, i.e., gender, age, education level, and marital status.


2022 ◽  
Vol 54 (9) ◽  
pp. 1-38
Author(s):  
Frank Siqueira ◽  
Joseph G. Davis

Recent advances in the large-scale adoption of information and communication technologies in manufacturing processes, known as Industry 4.0 or Smart Manufacturing, provide us a window into how the manufacturing sector will evolve in the coming decades. As a result of these initiatives, manufacturing firms have started to integrate a series of emerging technologies into their processes that will change the way products are designed, manufactured, and consumed. This article provides a comprehensive review of how service-oriented computing is being employed to develop the required software infrastructure for Industry 4.0 and identifies the major challenges and research opportunities that ensue. Particular attention is paid to the microservices architecture, which is increasingly recognized as offering a promising approach for developing innovative industrial applications. This literature review is based on the current state of the art on service computing for Industry 4.0 as described in a large corpus of recently published research papers, which helped us to identify and explore a series of challenges and opportunities for the development of this emerging technology frontier, with the goal of facilitating its widespread adoption.


2022 ◽  
Vol 22 (1) ◽  
pp. 1-18
Author(s):  
Chen Chen ◽  
Lei Liu ◽  
Shaohua Wan ◽  
Xiaozhe Hui ◽  
Qingqi Pei

As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.


2022 ◽  
Vol 74 ◽  
pp. 102281
Author(s):  
Qiushi Cao ◽  
Cecilia Zanni-Merk ◽  
Ahmed Samet ◽  
Christoph Reich ◽  
François de Bertrand de Beuvron ◽  
...  

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