Data-driven decision making with Blockchain-IoT integrated architecture: a project resource management agility perspective of industry 4.0

2021 ◽  
Author(s):  
Santosh B. Rane ◽  
Yahya Abdul Majid Narvel
Keyword(s):  
Decision Making ◽  
Resource Management ◽  
Industry 4.0 ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Integrated Architecture

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.

Data-Driven Decision Making to Select Condition-Based Maintenance Technology

2019 ◽  
pp. 324-343
Author(s):  
María Carmen Carnero-Moya
Keyword(s):  
Decision Making ◽  
Vibration Analysis ◽  
Industry 4.0 ◽  
Condition Based Maintenance ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Technical Parameters ◽  
Multicriteria Model ◽  
Expected Benefits ◽  
Using Data

Condition-based maintenance (CBM) may be considered an essential part of the Industry 4.0 environment because it can improve production processes through the use of the latest digital technologies. The literature includes a large number of contributions on new techniques for diagnosis, signal treatment, analysis of technical parameters, and prognosis. However, to obtain the expected benefits of a vibration analysis program, it is necessary to choose the instruments and introduction process best suited to the organization, and so guarantee the best results using data-driven decision making in accordance with the needs of Industry 4.0. Despite the importance of these decisions, no relevant models are found in the literature. This contribution describes a fuzzy multicriteria model for choosing the most suitable technology in vibration analysis. The goal is to create a model that is easy for organizations to use, and which reflects the judgements of a number of experts in maintenance and vibration analysis. The model has been applied to a Spanish state-run healthcare organization.

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