Polyhedrika

  Statistical Process Control (SPC) is used to control a critical process output acting on the factors affecting it only when required. The purpose is to differentiate  between a statistically significant change in the process and common cause variation because the actions required will be different. Real Time Statistical Process Control enables the operators running the process to know when action is required, as described in    Real Time SPC There are two extremes to avoid:            Over-reaction (adjusting after every output) makes the process worse: dispersion is increased. Under-reaction is just as bad: a slow process degradation passes undetected until it is too late. SPC Charts for Variables and Attributes We will now analyse some of the charts used for variables and attributes. SPC Chart Interpretation SPC uses some rules, developed by the Western Electric company, to detect symptoms of  statistically significant  variation. The center line is the average of all values and th

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