Measurement Framework of Smart Factory Technology Capability in Small-Medium Manufacturing Industry

2020 ◽  
Vol 25 (3) ◽  
pp. 121-134
Author(s):  
Chui-Young Yoon
Keyword(s):  
Manufacturing Industry ◽  
Smart Factory ◽  
Measurement Framework ◽  
Technology Capability

Industrie 4.0 in der Textilindustrie*/Industry 4.0 - Integrated Industry in the textile industry: Smart factory approaches with a special focus on textile products and processes

2015 ◽  
Vol 105 (04) ◽  
pp. 195-199
Author(s):  
R. Riedel ◽  
N. Göhlert ◽  
E. Müller
Keyword(s):  
Textile Industry ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Industrie 4.0 ◽  
Smart Factory ◽  
Special Focus ◽  
Considerable Potential ◽  
Textile Products

Industrie 4.0 bietet für die produzierende Industrie in Deutschland erhebliche Potentiale zur Steigerung der Wettbewerbsfähigkeit. Die Anwendung und volle Ausnutzung der Möglichkeiten entsprechender Technologien sind jedoch an bestimmte Voraussetzungen gebunden. Der Fachbeitrag reflektiert vor diesem Hintergrund die Umsetzungspotentiale von Industrie 4.0 in der Textilindustrie.   Industry 4.0, also called Integrated Industry, provides considerable potential for the manufacturing industry in Germany to increase its competitiveness. However, the application and the full exploitation of the potential of those technologies depend on certain conditions. Against this background, the article reflects on the implementation potential of Industrie 4.0 in the textile industry.

Business process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory

2018 ◽  
Vol 60 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Jana-Rebecca Rehse ◽  
Sharam Dadashnia ◽  
Peter Fettke
Keyword(s):  
Business Process ◽  
Industry 4.0 ◽  
Business Process Management ◽  
Manufacturing Industry ◽  
Process Management ◽  
Process Mining ◽  
Smart Factory ◽  
Manufacturing Processes ◽  
Planning And Control ◽  
And Control

Abstract The advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.

The prioritization of technologies and public R&D roles between the manufacturing and service industries in the fourth industrial revolution

foresight ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 680-694 ◽  
Author(s):  
Jinwon Kang ◽  
Jong-Seok Kim ◽  
Seonmi Seol
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Market Failure ◽  
Industrial Revolution ◽  
Service Industry ◽  
Service Industries ◽  
Delphi Survey ◽  
Smart Factory ◽  
Content Type ◽  
Fourth Industrial Revolution

Purpose The purpose of this study is to reveal the similarities and differences between the manufacturing and service industries in their prioritization of technologies and public research and development (R&D) roles, along with the complementation of properties of technology and public R&D role in the context of Fourth Industrial Revolution. Design/methodology/approach Two rounds of Delphi surveys were designed to meet the purpose of this study, which used rigorous triangulation techniques. The Delphi method was combined with the brainstorming method in the first-round Delphi survey, while the second-round Delphi survey focused on experts’ judgments. Finally, language network analysis was performed on the properties of technology and public R&D roles to complement the data analyses regarding prioritization. Findings This study identifies different prioritizations of five similar key technologies in each industry, so that it can note different technological impacts to the two industries in the Fourth Industrial Revolution. Smart factory technology is the first priority in the manufacturing industry, whereas artificial intelligence is the first priority in the service industry. The properties of the three common technologies: artificial intelligence, big data and Internet of things in both industries are summarized in hyper-intelligence on hyper-connectivity. Moreover, it is found that different technological priorities in the service and manufacturing industries require different approaches to public R&D roles, while public R&D roles cover market failure, system failure and government failure. The highest priority public R&D role for the service industry is the emphasis of non-R&D roles. Public R&D role to solve dy-functions, focus basic technologies and support challenging areas of R&D is prioritized at the highest for the manufacturing industry. Originality/value This study of the different prioritizations of technologies in the manufacturing and service industries offers practical lessons for executive officers, managers and policy-makers. They, by noting the different technological impacts in the manufacturing and service industries, can prepare for current actions and establish the priority of technology for R&D influencing the future paths of their industries in the context of the Fourth Industrial Revolution. While managers in the service industry should pay greater attention to the technological content of hyper-intelligence and hyper-connectivity, managers in the manufacturing industry should consider smart factory and robot technology.

scholarly journals Design and Optimization of Smart Factory Control System Based on Digital Twin System Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yan Bai ◽  
Jeong-Bong You ◽  
Il-Kyoo Lee
Keyword(s):  
Production Line ◽  
Manufacturing Industry ◽  
Technical Support ◽  
Smart Factory ◽  
Optimal Control Strategy ◽  
Actual System ◽  
Processing Scheme ◽  
Digital Twin ◽  
Twin System ◽  
Low Efficiency

Aiming at the problems of irrational allocation of resources, low efficiency caused by unbalanced production line layout, and slow production line upgrade of the smart factory, this paper builds a real physical smart factory platform through the optimal control strategy and uses the GRAFCET algorithm to optimize the logistics scheduling during the actual system operation. The genetic algorithm is used to optimize the layout effect of the production line; the digital twin technology is used to provide predictive analysis technical support for the upgrading and reengineering of the production line. Through the analysis and comparison of the production capacity and equipment utilization of the physical smart factory and the virtual smart factory processing scheme, practice shows that the design of the digital twin system can effectively improve the effect and accuracy of the lean production method in the production process reorganization. Quantitative analysis of manufacturing industry provides powerful theoretical and technical support.

scholarly journals Empirical study of measuring supply chain performance

2015 ◽  
Vol 22 (2) ◽  
pp. 290-308 ◽  
Author(s):  
Ilkka Sillanpää
Keyword(s):  
Supply Chain ◽  
Performance Measurement ◽  
Manufacturing Industry ◽  
Case Study Research ◽  
Future Research ◽  
Order Book ◽  
Content Type ◽  
Measurement Framework ◽  
Efficiency And Effectiveness

Purpose – Supply chain (SC) performance measurement – the process of qualifying the efficiency and effectiveness of the SC. The purpose of this paper is to create a SC measurement framework for manufacturing industry, define which data should be measured and verify the measurement framework in the case company’s SC. Design/methodology/approach – There is a review of the current understanding of supply chain management and literature related to SC performance measurement and the study creates a framework for SC measurement. This research is qualitative case study research. Findings – This study presents the main theoretical framework of SC performance measurement. The key elements for the measurement framework were defined as time, profitability, order book analysis and managerial analysis. The measurement framework is tested by measuring case SC performance. Research limitations/implications – In the study, a performance measurement framework was created for the needs of manufacturing industry. Suggestions for future research are multiple case study in different manufacturing industry areas and positivistic-based SC performance research. Practical implications – The measurement framework in this study offers guidelines for measuring the SC in manufacturing industry but the measurement framework could be used in different areas of industry as well. Originality/value – The SC performance measurement framework is tested and a valid framework for SC performance measurement in manufacturing industry.

scholarly journals Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning

2021 ◽  
Vol 7 ◽  
pp. e350
Author(s):  
Seungjin Lee ◽  
Azween Abdullah ◽  
Nz Jhanjhi ◽  
Sh Kok
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Smart Factory ◽  
Economic Losses ◽  
Fast Time ◽  
Management Service ◽  
Learning Program ◽  
Botnet Detection

The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.

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