Smart factory adoption in small and medium-sized enterprises: Empirical evidence of manufacturing industry in Korea

2020 ◽  
Vol 157 ◽  
pp. 120117
Author(s):  
Jeong Yeon Won ◽  
Min Jae Park
Keyword(s):  
Empirical Evidence ◽  
Manufacturing Industry ◽  
Smart Factory

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.

scholarly journals Empirical evidence of a long-run relationship between agriculture and manufacturing industry output in Nigeria

2019 ◽  
Author(s):  
Eze Osuagwu
Keyword(s):  
Error Correction ◽  
Empirical Evidence ◽  
Manufacturing Industry ◽  
Policy Implications ◽  
Necessary Condition ◽  
Macroeconomic Factors ◽  
Vector Error Correction ◽  
Vector Error ◽  
Long Run ◽  
Industry Output

<p>This study investigates a relationship between agriculture and manufacturing industry output in Nigeria from 1982-2015, using the Granger causality, co-integration and error correction techniques. Empirical evidence reveals a bidirectional relationship between the sectors. Although, a positive and significant relationship exists in the short and long-run estimates, a long-run divergence from the vector error correction model suggest that changes in agricultural productivity are not restored to equilibrium, given that macroeconomic factors distort the linkage. Policy implications indicate that macroeconomic stability is a necessary condition for agricultural and manufacturing sectors to foster economic growth.</p>

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.

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