Polyhedrika

Analyzing our Value Stream we identify a number of constraints that limit its effective capacity. The most visible constraints are the capacity of the different steps. But there are other, not so obvious, constraints such as the capacity variation of some steps. Process variation has been described as a virus infecting the value stream: it causes chaos and it is often undetected.     Dice Throwing Exercise A bottleneck limits the effective capacity of the whole line. Bottlenecks caused by variation are more subtle and difficult to detect and act upon. Process simulation can help us experience the effects of different types of constraints, try solutions and detect possible side effects. Process simulation helps us understand the value stream dynamic behavior: how variation affects key process metrics such as WIP, Lead time, Throughput, Cycle time, On Time Delivery, etc. Understanding these dynamic effects will enable us to get to the root cause of problems implementing definite solution

    A still picture of a Value Stream Map may not fully explain the behavior of the Value Stream. In some cases we need to understand its dynamic behavior. To do this we will convert our Excel Value Stream Map into a Monte Carlo Simulator. In Excel Value Stream Map we saw how a complex Value Stream could be mapped using Excel. We saw the importance of mapping feedback loops such as test-repair loops because they often become the bottleneck for the complete value stream. In this situation not all items flow along all branches so we need to calculate the % flowing along each branch. This enabled us to calculate the cycle for each workstation based on the staffing. Finally we were able to calculate the ideal staffing to balance the line: insure no workstation cycle is above the average inter-arrival time (takt time). This we did using Solver. Value Stream Map: Still Picture Vs Dynamic Behavior Mapping the current value stream with a VSM is a good starting point to understand how it behav