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When is Lean too Lean: Discrete Event Simulation and Lean Production

When is Lean too Lean: Discrete Event Simulation and Lean Production. Scott Metlen. ‘Romantic’ Lean & Complex Systems. Strategic variability leading to complex systems Complex system modeling (use discrete event simulation). Waste by Ohno , 1988. Overproduction Transportation Inventory

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When is Lean too Lean: Discrete Event Simulation and Lean Production

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  1. When is Lean too Lean:Discrete Event Simulation and Lean Production Scott Metlen

  2. ‘Romantic’ Lean & Complex Systems • Strategic variability leading to complex systems • Complex system modeling (use discrete event simulation)

  3. Waste by Ohno, 1988 • Overproduction • Transportation • Inventory • Motion • Defective Product • Over-processing • Waiting

  4. Discrete Event Simulation (DES) • Modeling events of a system where time between events is stochastic. • To expensive to use in deterministic systems

  5. System Variation, the Cause of Waste • Dysfunctional Variability (Suri, 2005) • Inconsistent process times when product is consistent • Set up times vary when product does not • Raw material varies when it should be consistent • Inconsistency of human performance • Strategic Variability (Suri, 2005) • Mass customization due to market demand • Willing to chase spikes and valleys in demand

  6. Danger of Lean • Eliminating strategic variation along with dysfunctional variability in the quest to eliminate waste • Prevent by simulating system change to determine the affect on profit if the change were executed

  7. Example • 875 parts used to assemble 10 products • Parts for each product collected as a ‘kit’ • 875 parts had different paths through the production system, scheduling was complex, average scrap rate 2% (complexity of parts and system) • FG warehouse kept to make sure each ‘kit’ was shipped on time and complete • FG seen as waste, was eliminated and replaced by expediting system to make sure a ‘kit’ shipped on time

  8. Results • Overtime went to $15 million • More production was installed where FG used to be • $200,000.00 opportunity cost saved on not having FG inventory • On time delivery dropped • Scrap rate increased

  9. New Solution Modeled • Modeled Strategic FG inventory • $100000 opportunity cost • Overtime reduced by $7 million • On time delivery reestablished • Scrap rate reduced • Recommendation was implemented, savings as indicated

  10. Take Away • In complex systems using strategic variability, model system before changing that system • QUESTIONS

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