CONTROL CHART

My Example

OVERVIEW

This page has been designed to help in understanding and learning the use, design and analysis of Control Charts, which is the most important tool of Statistical Quality Control.

 

HISTORY

Dr. Shewhart developed the control charts as an statistical approach to the study of manufacturing process variation for the purpose of improving the economic effectiveness of the process. These methods are based on continuous monitoring of process variation.

Background Information

However, a point that plots outside of the control limits is interpreted as evidence that the process is out of control, and investigation and corrective action is required to find and eliminate the assignable causes responsible for this behavior. The control points are connected with straight line segments for easy visualization.

Even if all the points plot inside the control limits, if they behave in a systematic or nonrandom manner, then this is an indication that the process is out of control.

USES OF CONTROL CHARTS

1. It is a proven technique for improving productivity.
2. It is effective in defect prevention.
3. It prevents unnecessary process adjustments.
4. It provides diagnostic information.
5. It provides information about process capability.

INTERPRETATION

Analysis of Patterns on Control Charts

A control chart may indicate an out-of-control condition either when one or more points fall beyond the control limits, or when the plotted points exhibit some nonrandom pattern of behavior.

The process is out of control if any one or more of the criteria is met.

1. One or more points outside of the control limits. This pattern may indicate:
   • A special cause of variance from a material, equipment, method, or measurement system change.
   • Mismeasurement of a part or parts.
   • Miscalculated or misplotted data points.
   • Miscalculated or misplotted control limits.
2. A run of eight points on one side of the center line. This pattern indicates a shift in the process output from changes in the equipment, methods, or materials or a shift in the measurement system.
3. Two of three consecutive points outside the 2-sigma warning limits but still inside the control limits. This may be the result of a large shift in the process in the equipment, methods, materials, or operator or a shift in the measurement system.
4. Four of five consecutive points beyond the 1-sigma limits.
5. An unusual or nonrandom pattern in the data.
   1. A trend of seven points in a row upward or downward. This may show
      • Gradual deterioration or wear in equipment.
      • Improvement or deterioration in technique.
   2. Cycling of data can indicate
      • Temperature or other recurring changes in the environment.
      • Differences between operators or operator techniques.
      • Regular rotation of machines.
      • Differences in measuring or testing devices that are being used in order.
6. Several points near a warning or control limit.

MY CONTROL CHART EXAMPLE

Problem Scenario: To determine the number of non-conformities for 25 handphones produced.

Data

Mean: 2.28

Interpretation: All the conditions are along the three stigma and most of them are above the LCL.