scholarly journals Business Models for Sustainable Innovation in Industry 4.0: Smart Manufacturing Processes, Digitalization of Production Systems, and Data-driven Decision Making

2019 ◽  
Vol 7 (3) ◽  
pp. 21 ◽  
Keyword(s):  
Decision Making ◽  
Industry 4.0 ◽  
Business Models ◽  
Production Systems ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Data Driven Decision Making ◽  
Sustainable Innovation

scholarly journals Big data driven decision-making for batch-based production systems

Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 814-818 ◽  
Author(s):  
Yongheng Zhang ◽  
Rui Zhang ◽  
Yizhong Wang ◽  
Hongfei Guo ◽  
Ray Y Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Production Systems ◽  
Data Driven ◽  
Data Driven Decision Making

scholarly journals A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 828
Author(s):  
Alexandros Bousdekis ◽  
Katerina Lepenioti ◽  
Dimitris Apostolou ◽  
Gregoris Mentzas
Keyword(s):  
Decision Making ◽  
Virtual Worlds ◽  
Industry 4.0 ◽  
Autonomous Robots ◽  
Simulation Software ◽  
Data Driven ◽  
Future Research ◽  
Data Driven Decision Making ◽  
Main Research ◽  
Iot Devices

Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

scholarly journals Reference Architectures for Industry 4.0: Literature Review

2020 ◽  
Author(s):  
Alexandre Helmann ◽  
Fernando Deschamps ◽  
Eduardo de Freitas Rocha Loures
Keyword(s):  
Literature Review ◽  
System Architecture ◽  
Industry 4.0 ◽  
New Technologies ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Automation System ◽  
Manufacturing Processes ◽  
Direct Integration

Currently, production systems are receiving the application of more advanced, integrated and connected technologies to optimize the performance of their manufacturing processes. The new technological solutions demand architectures that support intelligent solutions for a new digitalized industry. However, production systems already in operation have difficulty in implementing these technologies. The existing barriers limit the availability of the direct integration of different systems contemplated in an automation system architecture. This article systematically reviews the existing literature to portray the characteristics of each architecture and that can guide the adoption of new technologies. Through this review, emerging reference architectures were identified, such as RAMI4.0, IIRA, IBM Industry 4.0 and NIST Smart Manufacturing. In conclusion, the article presents a framework for considering which model best fits with the new technological solutions.

scholarly journals Enabling Smart Manufacturing Technologies for Decision-Making Support

2016 ◽  
Author(s):  
Moneer Helu ◽  
Don Libes ◽  
Joshua Lubell ◽  
Kevin Lyons ◽  
K. C. Morris
Keyword(s):  
Decision Making ◽  
Manufacturing Systems ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Advanced Manufacturing ◽  
Data Driven Decision Making ◽  
Use Of Data ◽  
Manufacturing Technologies ◽  
Common Understanding ◽  
Decision Making Support

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

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