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A Classification Scheme for Smart Manufacturing Systems' Performance Metrics.

Y Tina Lee1, Senthilkumaran Kumaraguru2, Sanjay Jain3

  • 1Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD 20899-8260, USA.

Smart and Sustainable Manufacturing Systems
|August 9, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a classification framework for smart manufacturing performance metrics, including agility, asset utilization, and sustainability. It outlines themes and a conceptual model for evaluating these key performance indicators in data-intensive environments.

Keywords:
AgilityAsset utilizationMetrics classificationPerformance evaluationSmart manufacturingSustainability

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Area of Science:

  • Industrial Engineering
  • Manufacturing Systems
  • Operations Research

Background:

  • Smart manufacturing systems generate vast amounts of data.
  • Effective performance measurement is crucial for optimizing these systems.
  • Existing metrics lack a unified classification structure.

Purpose of the Study:

  • To propose a novel classification scheme for performance metrics in smart manufacturing.
  • To analyze key metrics such as agility, asset utilization, and sustainability.
  • To develop a generalized framework for performance evaluation.

Main Methods:

  • Identifying and discussing classification themes for performance metrics.
  • Developing a generalized classification scheme based on these themes.
  • Presenting a conceptual model for performance evaluation data.

Main Results:

  • A structured classification scheme for smart manufacturing performance metrics.
  • Identification of key themes relevant to agility, asset utilization, and sustainability.
  • A conceptual model to guide data collection for performance evaluation.

Conclusions:

  • A unified classification system enhances the understanding and measurement of smart manufacturing performance.
  • The proposed scheme provides a foundation for developing robust, real-time performance measurement systems.
  • Future work should address challenges in data-intensive, real-time enterprise environments.