Journal of Production Engineering

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Vol. 22 No. 1 (2019)
Original Research Article

Improving manufacturing process by optimizing time parameters

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  • Bojana Ristovska,
  • Elena Papazoska,
  • Valentina Gecevska
Bojana Ristovska
University „Ss. Cyril and Methodius“ in Skopje, Faculty of Mechanical Engineering, Karpos 2 bb, P.O.Box 464, 1000 Skopje, Republic of Macedonia
Elena Papazoska
University „Ss. Cyril and Methodius“ in Skopje, Faculty of Mechanical Engineering, Karpos 2 bb, P.O.Box 464, 1000 Skopje, Republic of Macedonia
Valentina Gecevska
University „Ss. Cyril and Methodius“ in Skopje, Faculty of Mechanical Engineering, Karpos 2 bb, P.O.Box 464, 1000 Skopje, Republic of Macedonia
DOI: https://doi.org/10.24867/JPE-2019-01-042

Published 2019-06-30

abstract views: 12 // FULL TEXT ARTICLE (PDF): 9


Keywords

  • Lean methodology,
  • production process,
  • takt time

How to Cite

Ristovska, Bojana, Elena Papazoska, and Valentina Gecevska. 2019. “Improving Manufacturing Process by Optimizing Time Parameters”. Journal of Production Engineering 22 (1):42-46. https://doi.org/10.24867/JPE-2019-01-042.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Lean methodology as one of the most modern ways of working, uses a variety of methods and tools that are aimed at continuous improvement and functioning of a single system. Due to its growing use in the manufacturing and service sector, Lean methodology is the subject of this paper. First, a series of Lean methods and tools will be presented to help analyze the current state of the selected production process, from raw materials to finished product. Then, with the help of the obtained data from the monitoring, it will be determined whether it is possible to improve the production process, by calculating the required number of operators in relation to the number of machines operated by one operator and the required number of operators in relation to the takt time, which in turn is related to the volume. Finally, a comparison of these data will be made to determine the future state of the process

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FULL TEXT ARTICLE (PDF)