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Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review.

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Industry 4.0 utilizes advanced technologies for efficient manufacturing. This study examines how industrial standards are used for performance measurement and quality management in smart manufacturing environments, addressing current knowledge gaps.

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Area of Science:

  • Manufacturing Engineering
  • Industrial Management
  • Information Technology

Background:

  • The evolution of manufacturing from mass production to digitalization, driven by Information and Communication Technology (ICT).
  • The emergence of Industry 4.0, characterized by technologies like IoT, AI, cloud computing, and big data, transforming production processes.
  • The challenges in understanding and implementing Industry 4.0 technologies, particularly concerning performance measurement and quality management.

Purpose of the Study:

  • To investigate the application of industrial standards in Industry 4.0 for performance measurement and quality management.
  • To address the existing uncertainty and knowledge gaps regarding performance measurement and quality control in smart manufacturing.
  • To explore the concept of Quality 4.0 as the digitalization of quality management.

Main Methods:

  • Review of current methods and industrial standards for performance measurement in data-driven Industry 4.0.
  • Analysis of Key Performance Indicators (KPIs) utilized in smart manufacturing environments.
  • Inclusion of case studies to illustrate practical applications of Industry 4.0 technologies in manufacturing.

Main Results:

  • Identification of various industrial standards and KPIs employed by manufacturing industries for performance and quality assessment.
  • Demonstration of how smart manufacturing companies leverage Industry 4.0 technologies through case study analysis.
  • Discussion on the digitalization of quality management, termed Quality 4.0.

Conclusions:

  • Industrial standards are crucial for measuring performance and managing quality in the context of Industry 4.0.
  • Further research is needed to address challenges and explore opportunities in data-driven Industry 4.0 performance and quality management.
  • The adoption of Quality 4.0 principles is essential for optimizing smart manufacturing operations.