Cogent Engineering (Dec 2024)

Enhancing operation efficiency of leather manufacturing industry through hybrid of value stream mapping and discrete event simulation

  • Wogiye Wube Woldemicael,
  • Eshetie Berhan,
  • Daniel Kitaw,
  • Gezahegn Tesfaye

DOI
https://doi.org/10.1080/23311916.2024.2375423
Journal volume & issue
Vol. 11, no. 1

Abstract

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Traditional value stream mapping (VSM) has played substantial role in reducing lean wastes in the manufacturing sector overall as well as specifically within automotive companies. However, it provides only a static view of linear production systems with limited process details. This research is conducted in the case of leather industry. The case study is challenged with work -in- progress inventory, unnecessary processes, long waiting time, and long lead time. Therefore, the purpose of this research is to address literature gaps and challenges faced by a case leather company and enhance operational efficiency by combining VSM with discrete event simulation. Product matrix analysis is used to categorize leather goods into product families and select a representative product. Data collection involves direct observation (Gemba-walk) and report reviews. Numerical results from the enhanced current state VSM (ECSVSM) quantify value-added time, non-value-added time, value-added ratio, Takt Time, and total production lead time as 2198.24 s, 14.59 d, 0.54%, 230.1 s, and 14.67 d, respectively. This ECSVSM data is then applied to develop a simulation model of the As-Is system. Based on findings from the ECSVSM and As-Is simulation, shop floor wastes are identified. Alternative scenarios are proposed to mitigate these wastes, with one scenario selected along with additional proposals involving 5S, supermarkets, and layout adjustments. A future state VSM incorporates these solutions. Results demonstrate major improvements, including enhanced operational efficiency up 8.16%, and reductions in non-value-added time by 42.9%, lead time by 42.72%, alongside increased value-added ratio by 55.56% and lowered key waste categories.

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