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Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case

Yuchang Won1, Seunghyeon Kim1, Kyung-Joon Park1

  • 1Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
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This study shows how to use Internet of Everything (IoE) data for fault monitoring to improve automotive production lines. It details deriving key productivity data from existing systems for continuous improvement.

Area of Science:

  • Industrial Engineering
  • Manufacturing Systems
  • Data Analytics

Background:

  • Continuous productivity improvement is crucial for manufacturing competitiveness.
  • Traditional fault monitoring systems lack data essential for productivity analysis.
  • Existing production lines often lack dedicated systems for continuous improvement data.

Purpose of the Study:

  • To demonstrate deriving continuous improvement data from conventional fault monitoring systems.
  • To present a case study in an automotive parts production line.
  • To provide a method for numerical decision-making on production line modifications.

Main Methods:

  • Modeling the production system using existing fault monitoring data.
  • Deriving essential datasets (uptime, downtime, cycle-time) for continuous improvement.
Keywords:
continuous productivity improvementfault monitoring datainternet of everythingproduction systems engineeringsmart factory

Related Experiment Videos

  • Applying the method to an automotive parts production line case study.

    • Main Results:

    • Successfully derived the dataset required for continuous improvement from conventional fault monitoring.
    • Developed a model applicable to automotive parts production lines.
    • Quantified expected productivity improvements for operational decision-making.

    Conclusions:

    • It is feasible to derive continuous improvement data from existing fault monitoring systems.
    • The proposed method aids in making informed decisions regarding production line configuration.
    • IoE data analytics can significantly enhance manufacturing productivity.