A lean pull system design analysed by value stream mapping and multiple criteria decision-making method under demand uncertainty

  • Authors:
  • Jiunn-Chenn Lu;Taho Yang;Cheng-Yi Wang

  • Affiliations:
  • Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan;Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan;Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan

  • Venue:
  • International Journal of Computer Integrated Manufacturing
  • Year:
  • 2011

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Abstract

Lean philosophy is a systematic approach for identifying and eliminating waste through continuous improvement in pursuit of perfection, using a pull-control strategy derived from customers' requirements. However, not all lean implementations have produced such desired results because of not having a clear implementation procedure and execution guide. This article proposes a lean pull system implementation procedure based on combining a supermarket supply with two constant work-in-process (CONWIP) structures that can concurrently consider manufacturing system variability and demand uncertainty in multi-products, multi-stage processes to achieve lean pull system. The study uses a multiple criteria decision-making (MCDM) method, using a hybrid Taguchi technique for order preference by similarity to ideal solution (TOPSIS) method that takes customer demand uncertainty as a noise factor. This allowed identification of the most robust production control strategy to identify an optimal scenario from alternative designs. Value stream mapping (VSM) was applied to visualise what conditions would work when improvements are introduced. Finally, a real-world, thin film transistor-liquid crystal display (TFT-LCD) manufacturing case-study under demand uncertainty is used to demonstrate and test findings. After comparing the current-state map and the future-state map of the case-study, the simulation results indicate that the average cycle time reduced from 15.4 days to 4.82 days without any loss of throughput.