Simulation Used as a Black Belt Tool in Lean Manufacturing

Simulation: Operations Excellence Tool in Lean Manufacturing

Have you solved the product mix issue of low volume products in your lean manufacturing program? Our tools and analytical approach:

Include variability analysis for all supply, manufacturing & logistics
Expand value stream mapping to dynamic simulation
Use both simulation and optimization modeling as six sigma tools.

Simulation is an essential lean six sigma tool to test designs before implementing changes. Heuristic algorithms are employed to develop lean manufacturing plans and production schedules, then tested by six sigma black belts using simulation.

Our simulation models can help to reduce variability - the source of many problems.

New plant designs down to individual resources
Capacity & throughput bottlenecks
Process improvements
Supply and demand balancing for both push and pull environments.

We have helped our clients realize real benefits, beyond what is possible with traditional tools such as value-stream mapping. Some examples:

Form postponement of packaging – 30% inventory reduction; with substantial service improvement to 97+%
Shift/work center change – manpower reduction 7 to 6 days/week
Add staff to assembly work center – reduced overall manpower 10%
Cycle Time reduction 18 to 13 weeks with plant to plant synchronization
Impact of late materials – starving downstream operations
Test schedules & kanbans
Variability & mix impacts on capacity investments.

Our models allow your lean analysts to test the impacts of lean improvement techniques, including:

Kanbans & CONWIP
Schedules
EPE (every-part-every …) rhythm cycles
Batch & campaign sizes vs. one piece flow
Setup reduction
Routing changes
Shared resources
Postponement

Variability impacts
Downtime impacts
Yield & scrap
Material lead times

Metrics include:

Takt times/rates
Overall Equipment Effectiveness (OEE )
End to end cycle times

Models are adaptable to either flow shops or job shops. Following is an example of a mixed environment with a machine within the flow used for multiple passes.

The model uses probabilistic processing for demand forecasts and actual ordering, as well as all processing within an operation, such as:

Processing times
Setup times
Full & Back-to-Back cleanouts
Quality lab & release times
Late materials
Quality / scrap yields & rates
Absentee rates in labor crews
Hold times
Equipment downtime.

The resulting output metrics provide confidence intervals on throughput and utilization for all equipment, people and shared resources. An example of a laboratory operation which has characteristics of a job shop, where there are multiple passes on different equipment and requires different people skills to perform, follows:

Additional examples of metrics from the Lean Simulation Model.

	Supply Simulation, Process Optimization, Lean Design