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Automating the process of method-time-measurement

Author

Listed:
  • Gualtiero Fantoni
  • Salam Qaddoori Al-Zubaidi
  • Elena Coli
  • Daniele Mazzei

Abstract

Purpose - This work reports on a developing method time measurement system for measuring manufacturing and assembly processes automatically. This automatic system enables the production engineers and management to detect, process, and display concise and accurate information about the operations in real time. Design/methodology/approach - This system is based on Internet of things technology and RFID-antenna. This methodology consists of seven main steps and one final optimization step. Mainly, the operator is equipped by RFID reader, and the work station tools and devices are provided by RFID tags. Responding the RFID tags to the reader will refer to the certain operations, the difference time between start and end of the operations will be collected immediately and calculated by the microprocessor of the system. Findings - This automatic system is promising, considering the accurate time measurements and recommendations that obtained from the case study which includes measuring manual assembly operations to be followed in order to overcome the limitations which are not only technical but also managerial, legal and organizational. Research limitations/implications - The acquired data about timing and duration of individual operations are anonymized to guarantee the compliance with respect to the privacy laws (GDPR and Italian work's laws). Originality/value - This work presents a unique system to measure the time instead of traditional methods in the factories environment and satisfies the requirements to study the recommendations in order to overcome the challenges.

Suggested Citation

  • Gualtiero Fantoni & Salam Qaddoori Al-Zubaidi & Elena Coli & Daniele Mazzei, 2020. "Automating the process of method-time-measurement," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 70(4), pages 958-982, December.
  • Handle: RePEc:eme:ijppmp:ijppm-08-2019-0404
    DOI: 10.1108/IJPPM-08-2019-0404
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    Cited by:

    1. Andrzej Szajna & Mariusz Kostrzewski & Krzysztof Ciebiera & Roman Stryjski & Waldemar Woźniak, 2021. "Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification," Energies, MDPI, vol. 14(12), pages 1-35, June.

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