Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Area of Science:

• Manufacturing Engineering
• Information Systems
• Computer Science

Background:

• Traditional manufacturing struggles with Industry 4.0 demands due to a lack of standards, skills, and technology.
• Enterprise Integration and Interoperability (EII) are crucial for efficient communication in big data environments.
• Data management challenges impact both technical solutions and organizational functions.

Purpose of the Study:

• To identify synergies between EII, Big Data Processing, and Industry 4.0.
• To propose an architectural framework for Industry 4.0 solutions in big data-driven manufacturing.
• To demonstrate the framework's applicability using predictive maintenance.

Main Methods:

• Literature review and conceptual framework development.
• Integration of EII, Big Data Processing, and Industry 4.0 principles.
• Case study instantiation for predictive maintenance.

Main Results:

• Identification of key synergies enabling the Fourth Industrial Revolution.
• Proposal of a novel architectural framework for big data-driven manufacturing operations.
• Demonstration of the framework's practical application in predictive maintenance.

Conclusions:

• The proposed framework facilitates the design and modeling of Industry 4.0 solutions.
• EII and Big Data Processing are essential for overcoming manufacturing challenges.
• The framework supports the adoption of advanced manufacturing functions like predictive maintenance.