LEAN Six Sigma | Simulation Modeling

Simulations as Reinforcement for Lean Six Sigma (LSS)

Authored by: Robert Bailey, Advanced Process Optimization, Inc.

It is widely accepted that Lean focuses on waste reduction in processes and Six Sigma on reduction of variability. Lean tries to create flow and pursue perfection in processes. Six Sigma is extremely valuable in the pursuit of perfection. As an example, the only way to solve certain quality problems with complex interactions is with the statistical tools of Six Sigma. Another aspect of Lean is Customer Focus. If we express Customer Focus in terms of hitting a target, the concept of the Taguchi Loss Function makes sense. Again, Six Sigma comes into play by helping to reduce variation around the target. Lean and Six Sigma (LSS) are complimentary and they both promote the heavy use of visual management, metrics and involvement of the people.

Reinforcing Nature of Simulations:

Most people familiar with simulations recognize the value of being able to quickly run various scenarios on the computer. What is often missed though, is the power of simulation as a consensus-building tool and an insight generator. Simulation software with animated output provides a valuable visual stimulus. In a group setting with a computer and projector, a team can both exploit process constraints and explore potential breakthroughs.

Models are best built up piece by piece in joint sessions with the interested parties. You can think of these models as Management Flight Simulators. Ownership for the model and its results are built up over time. Participants get to change the values of key model variables and vet the output. These sessions are also opportunities for participants to have an “ah ha” moment. Chances are that if you just present people with a complex model that didn’t involve them in its creation, they will either reject it or at the very least be skeptical of the results.

Value Stream Mapping is a powerful Lean tool based on process architecture and data. The data is primarily deterministic. Additional insights can be derived via a simulation model if cycle time data is represented by a probability density function. The stochastic model of the Value Stream is a better representation of the real world. Multiple runs of the model will show the time varying nature and range on constraints, inventory etc. as well as variation in key metrics.

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