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Analysis of Loading Time Improvement on Finished Product Warehouse Using Lean Six Sigma and Discrete Event Simulation

Published:27 November 2022Publication History

ABSTRACT

Performance improvement must be carried out continuously at all process lines in the industry in order to increase its competitiveness, including in the process of operating the finished product warehouse. Improvements in the warehouse can be made by reducing waste and defects in every process (inbound, storage handling, picking and shipping). The Lean Six Sigma approach has been widely used in various industries and can increase productivity. This study aims to present an analysis of the application of lean six sigma in finished product warehouse operations with a discrete event simulation approach in order to obtain suggestions for improvements to reduce waste during loading process. Lean Six Sigma provides a structured approach through the implementation of DMAIC (Define, Measure, Analyze, Improve and Control) for analyzing a warehouse operation problem, diagnose its cause and generate improvement plan. Discrete event simulation is used in the "improve" stage to provide an evaluation of loading process improvement in product warehouse. Simulations are carried out in case studies of steel plate warehouse products where there is some waste during the loading process, including inefficient storage location and reshuffling during delivery. Improvement scenarios based on lean six sigma are simulated so that they can be implemented, in order to obtain a more systematic warehouse operation improvement design.

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  1. Analysis of Loading Time Improvement on Finished Product Warehouse Using Lean Six Sigma and Discrete Event Simulation

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    • Published in

      cover image ACM Other conferences
      APCORISE '21: Proceedings of the 4th Asia Pacific Conference on Research in Industrial and Systems Engineering
      May 2021
      672 pages
      ISBN:9781450390385
      DOI:10.1145/3468013

      Copyright © 2021 ACM

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      Publication History

      • Published: 27 November 2022

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