Steve Beeler

You have a goal…I have a way to get you there.

Control Charts

Source Inspection for Zero Defects

by

As the highest form of error proofing, source inspection for zero defects is such a powerful concept that it merits a close look.

Source inspection is 100% inspection, not statistical sampling, for causal factors that lead to defects. Defects are prevented through immediate action to remove causal factors. While this may sound complicated, it is not. In fact, examples of source inspection are everywhere around us.

The logic behind source inspection is pretty simple. Before completing an action of some kind, check for causal factors of defects. If causal factors are present, then the action is stopped before completion, and before the defect occurs.  With the causal factor(s) removed, the action can be restarted and completed without creating a defect.

Source Inspection

Everyday examples are all around us:

Automobiles. A little fender bender in a crowded parking lot is a defect that we can all do without. Possible causal factor: foot off of brake pedal. Source inspection: all vehicles with automatic transmissions have a safety interlock that requires the driver to have their foot on the brake pedal before shifting out of park.

On-line forms. In this case, the defect is missing information on an airline reservation, a catalog purchase, or whatever. Possible causal factors: error of omission or typographical error. Source inspection: if a required data entry field is empty, incomplete, or in the wrong format, the reservation or ordering process will not advance to the next screen until the field is filled correctly.

Thermocouples. A residential natural gas explosion would be a very bad defect. Possible causal factor: no pilot light. Source inspection: gas water heaters, furnaces, fireplaces, and so on have thermocouples. If the pilot light is on, the thermocouple is hot and gas is allowed to flow. If the pilot light goes out, the thermocouple cools down and shuts off the gas flow.

Does source inspection make statistical sampling and control charts obsolete?  Absolutely not.  Use statistical process control to reduce causal factor variability to minimize interruptions and increase productivity.

Control Charts

by

To provide a rational means to categorize (common cause vs special cause) and measure variation, Walter A. Shewhart invented control charts circa 1925 – 1931. W. Edwards Deming took these basic statistical concepts and tools with him to Japan after World War II and planted the seeds for the quality revolution. The rest is history.

Control charts are best maintained at the job station by the production team to provide those closest to the process guidance on when and when not to take action. Once a process is in statistical control (a very fine achievement in itself), a control chart can verify actions taken to reduce variability.

The primary signal of an out-of-control process is a point outside the control limits. Given constant probability (an requirement for process control), the chance of a point outside the control limits is very, very small. Other out-of-control signals include seven points in a row above or below the mean and runs of seven up or down. If these signals are present, then a special cause has visited the process. Look for it right away, before the trail grows cold!

Control Charts

Here is an example of an X-bar & R chart for variables data. An assembly plant body shop was requiring significant overline overtime to make production and the management team was attributing the volume shortfalls to “catastrophic” breakdowns at its constraint. A control chart for hour-to-hour production counts was in control (no special cause events) but with an unacceptably high level of common cause variation. The management team was “recalibrated” to focus their effort on the many small production loss causal factors occurring every hour: speed losses, absenteeism, operator pacing, untrained operators, material deliveries, weld faults, tip changes, etc.

Control Charts

Control charts also work with attribute data when variables data is not available. Here is an example of a U-chart of incomplete operations at an inspection and repair station. This chart has been out of control for quite awhile: the mean number of repairs have shifted up. In retrospect, the team was slow in finding the special cause in this case a defective bumper shield clip.

Control Charts

Out-of-control signals can be good. Let’s say an adjustment has been made to a process running above the specification target. A run of seven points below the averages chart mean is verification that the adjustment has moved the process mean. Or maybe a material spec change has been made. A run of seven points below the range chart mean is verification that the material change has reduced common cause variation. In both cases, new control limits must be calculated to reflect the process changes.

Terminology

Variables Data = quantitative value that can be measured (572 feet, 2403 lbs, 59.6 seconds, etc)
Attributes Data = qualitative value that can be counted (pass or fail, black or white, etc)