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 Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

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