Effects of Understanding Smart Factories on Smart Factory Construction Performance: Focusing on the mediating effect of government support elements

2021 ◽  
Vol 26 (4) ◽  
pp. 1-10
Author(s):  
Dae-Hung Jun ◽  
Il-Seob Koo
Keyword(s):  
Mediating Effect ◽  
Government Support ◽  
Smart Factory ◽  
Smart Factories

scholarly journals How Social Entrepreneurs Affect Performance of Social Enterprises in Korea: The Mediating Effect of Innovativeness

2018 ◽  
Vol 10 (8) ◽  
pp. 2643 ◽  
Author(s):  
Changhwan Shin
Keyword(s):  
Economic Performance ◽  
Social Enterprise ◽  
Social Performance ◽  
Social Enterprises ◽  
Mediating Effect ◽  
Government Support ◽  
Policy Implications ◽  
Social Entrepreneurs ◽  
The Social ◽  
Positive Direct

Schumpeter argued that entrepreneurship brings about creative destruction in capitalist economies. South Korea enacted the Social Enterprise Promotion Act in 2007 to promote corporate social enterprise. However, despite government support, social enterprises in Korea are not successful, especially in social and economic performance, which is defined as the social and economic value that social enterprises should pursue. A questionnaire survey was conducted among 100 social entrepreneurs, and the structural equation model was used as the research method. The results of the analysis are as follows. Openness and innovativeness have a positive direct impact on economic as well as social performance. In addition, openness and innovativeness play a mediating role not only in social performance, but also in economic performance. This paper suggests theoretical and policy implications based on the above analysis.

scholarly journals «Smart Factories» as a Promising Direction for Digitalization of Enterprises

2021 ◽  
Vol 10 (525) ◽  
pp. 363-367
Author(s):  
I. V. Yatskevych ◽  
◽  
N. D. Maslii ◽  
Keyword(s):  
Business Processes ◽  
Business Environment ◽  
Production Costs ◽  
Smart Factory ◽  
Automated Control ◽  
Promising Direction ◽  
Level Of Automation ◽  
Production Flexibility ◽  
High Level ◽  
Smart Factories

The work examines the role, problems and prospects of a «smart factory» in the conditions of digitalization of enterprises, taking into account imbalance of the business environment. It is defined that in modern conditions, most enterprises focus on automating business processes and increasing efficiency, and only a minority of them transforms the business model, which is grounded on an insufficient level of maturity for drastic changes. It is substantiated that the so-called «smart factory» is a promising direction for the development of digitalization of enterprises, taking into account the imbalance of the business environment. It is noted that the «smart factory» is an environment wherein machines and equipment can improve processes through automation and self-isolation. At the same time, they are aimed at mass production of articles, while maintaining a maximum production flexibility. These requirements are ensured due to the high level of automation and robotization of the enterprise. Automated control systems for technological and business processes are also widely used due to their consistency at each stage of interaction. The study of the content of the concept of «smart factory» allowed to determine and systematize its functions (planning, logistics, supply chain, product development), advantages (accessibility, optimization, proactivity, flexibility), segmentation, problem groups (personnel, technologies, business process). It is specified that the development and implementation of «smart factories», despite the advantages of the latter, can be difficult and risky. Enterprises that have succeeded with the introduction of «smart factories» can increase production and value by reducing production costs, improving quality and flexibility, as well as reducing the time for the market entrance.

scholarly journals Honeypot Coupled Machine Learning Model for Botnet Detection and Classification in IoT Smart Factory – An Investigation

2021 ◽  
Vol 335 ◽  
pp. 04003
Author(s):  
Seungjin Lee ◽  
Azween Abdullah ◽  
N.Z. Jhanjhi ◽  
S.H. Kok
Keyword(s):  
Machine Learning ◽  
Process Management ◽  
Denial Of Service ◽  
The United States ◽  
Smart Factory ◽  
High Detection Rate ◽  
Botnet Detection ◽  
Detection Model ◽  
Log File ◽  
Smart Factories

In the United States, the manufacturing ecosystem is rebuilt and developed through innovation with the promotion of AMP 2.0. For this reason, the industry has spurred the development of 5G, Artificial Intelligence (AI), and Machine Learning (ML) technologies which is being applied on the smart factories to integrate production process management, product service and distribution, collaboration, and customized production requirements. These smart factories need to effectively solve security problems with a high detection rate for a smooth operation. However, number of security related cases occurring in the smart factories has been increasing due to botnet Distributed Denial of Service (DDoS) attacks that threaten the network security operated on the Internet of Things (IoT) platform. Against botnet attacks, security network of the smart factory must improve its defensive capability. Among many security solutions, botnet detection using honeypot has been shown to be effective in early studies. In order to solve the problem of closely monitoring and acquiring botnet attack behaviour, honeypot is a method to detect botnet attackers by intentionally creating resources within the network. As a result, the traced content is recorded in a log file. In addition, these log files are classified quickly with high accuracy with a support of machine learning operation. Hence, productivity is increase, while stability of the smart factory is reinforced. In this study, a botnet detection model was proposed by combining honeypot with machine learning, specifically designed for smart factories. The investigation was carried out in a hardware configuration virtually mimicking a smart factory environment.

scholarly journals Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019 ◽  
Vol 8 (5) ◽  
pp. 143 ◽  
Author(s):  
Rabab Benotsmane ◽  
György Kovács ◽  
László Dudás
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Optimal Operation ◽  
Added Value ◽  
Smart Factory ◽  
Motion Path ◽  
Robot Arm ◽  
Quantitative Research Methods ◽  
Smart Factories

Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

scholarly journals Barriers of embedding big data solutions in smart factories: insights from SAP consultants

2019 ◽  
Vol 119 (5) ◽  
pp. 1147-1164 ◽  
Author(s):  
Shuyang Li ◽  
Guo Chao Peng ◽  
Fei Xing
Keyword(s):  
Big Data ◽  
Project Managers ◽  
Smart Factory ◽  
Business Managers ◽  
Structured Interviews ◽  
Content Type ◽  
Systems Research ◽  
Information Systems Research ◽  
Analytical Tools ◽  
Smart Factories

Purpose Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers. Design/methodology/approach The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach. Findings A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers. Originality/value This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.

Software-based smart factory networking

2016 ◽  
Vol 64 (9) ◽  
Author(s):  
Robert Bauer ◽  
Roland Bless ◽  
Christian Haas ◽  
Markus Jung ◽  
Martina Zitterbart
Keyword(s):  
Communication Network ◽  
Software Defined Networking ◽  
Smart Factory ◽  
Network Functions ◽  
Smart Factories

AbstractThis paper describes a framework for software-based networking in smart factories (SF) that enables them to easily adapt the communication network to changing requirements. Similar to cloud-based systems, such SFs could be seen as production clusters that could be rented and configured as needed. The SF network utilizes software-defined networking (SDN) combined with network functions virtualization (NFV) in order to achieve the required flexibility. This paper presents and discusses

scholarly journals An improved cyber-physical systems architecture for Industry 4.0 smart factories

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878419 ◽  
Author(s):  
Jehn-Ruey Jiang
Keyword(s):  
Vertical Integration ◽  
Physical System ◽  
Industry 4.0 ◽  
Smart Factory ◽  
Cyber Physical System ◽  
Industrial Internet Of Things ◽  
Systems Architecture ◽  
Physical Systems ◽  
Industrial Internet ◽  
Smart Factories

The cyber-physical system is the core concept of Industry 4.0 for building smart factories. We can rely on the ISA-95 architecture or the 5C architecture to build the cyber-physical system for smart factories. However, both architectures emphasize more on vertical integration and less on horizontal integration. This article proposes the 8C architecture by adding 3C facets into the 5C architecture. The 3C facets are coalition, customer, and content. The proposed 8C architecture is a helpful guideline to build the cyber-physical system for smart factories. We show an example of designing and developing, on the basis of the proposed 8C architecture, a smart factory cyber-physical system, including an Industrial Internet of Things gateway and a smart factory data center running in the cloud environment.

Semantische Kommunikationsschnittstellen zur Zustandsüberwachung im Karosseriebau/Standardised I4.0 communication interfaces – Requirements and measures to establish I4.0 principles in modern production systems

2016 ◽  
Vol 106 (10) ◽  
pp. 699-704
Author(s):  
H. Fleischmann ◽  
J. Kohl ◽  
A. Blank ◽  
M. Schacht ◽  
J. Fuchs ◽  
...  
Keyword(s):  
Communication Networks ◽  
Industry 4.0 ◽  
Production Systems ◽  
Industrie 4.0 ◽  
Efficient Implementation ◽  
Smart Factory ◽  
Vehicle Body ◽  
Body Construction ◽  
Semantic Communication ◽  
Smart Factories

Industrie 4.0-Technologie verspricht Unterstützung bei der Erfüllung komplexer Produktionsaufgaben. Bisher verhindern jedoch historisch gewachsene, industrielle Kommunikationsnetze durch die oft wenig semantische, strikte Kommunikation entlang der bestehenden Ebenen der Automatisierungspyramide eine effiziente Umsetzung der Prinzipien von „Smart Factories“. Diese Veröffentlichung thematisiert die Entwicklung semantischer Kommunikationsschnittstellen am Beispiel des Karosseriebaus der Audi AG.   Industry 4.0 technology promises to support the fulfillment of complex production tasks. Even today, historically grown industrial communication networks prevent an efficient implementation of smart factory principles, especially due to a lack of semantics and the strict communication along the existing layers of the automation pyramid. This publication focuses on the development of semantic communication interfaces using the example of the digitalization of the vehicle body construction at the Audi AG.

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