When is Lean too Lean Discrete Event Simulation
- Slides: 10
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 Motion Defective Product Over-processing Waiting
Discrete Event Simulation (DES) • Modeling events of a system where time between events is stochastic. • To expensive to use in deterministic systems
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
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
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
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
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
Take Away • In complex systems using strategic variability, model system before changing that system • QUESTIONS
- Quantifiers too much too many enough
- Parallel discrete event simulation
- Not too big not too small just right
- Research questions
- Too foreign for home too foreign for here
- Too much money is chasing too few goods
- Too anointed to be disappointed
- Too broad and too narrow examples
- Too broad and too narrow examples
- Not too narrow not too deep
- Iso 18404 training