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Area of Science:

  • Industrial Engineering
  • Information Systems
  • Human-Computer Interaction

Background:

  • The integration of human-machine systems is crucial for optimizing information processing in industrial settings.
  • Understanding variability in value creation processes requires effective human-machine collaboration.
  • Industrial Internet of Things (IIoT) offers potential for enhanced integration and data analysis.

Purpose of the Study:

  • To evaluate the impact of human-machine integration on explaining variability in value creation processes.
  • To assess how IIoT technology and advanced data processing can improve human-machine integrability.
  • To identify appropriate management strategies for enhanced human-machine binomials.

Main Methods:

  • Action research methodology applied across two distinct case studies.
  • Analysis of different industrial sectors with varying complexity levels and value stream architectures.
  • Assessment of human influence and organizational requirements within the studied processes.

Main Results:

  • Increased transparency in value creation processes through enhanced human-machine integration.
  • Demonstrated benefits of analyzing processes with higher levels of integration.
  • Improved understanding of information processing and management strategies.

Conclusions:

  • Human-machine integration, facilitated by IIoT, is key to managing variability and optimizing value creation.
  • Advanced data processing and integration strategies lead to significant improvements in industrial operations.
  • The study provides a framework for enhancing collaboration and efficiency in complex industrial environments.