Statistical Process Control (SPC), Basic Procedure of Control Chart.

Statistical Process Control (SPC)

Statistical evaluation of the output of a process during production

- Goal is to make the process stable over time & then keep it stable unless the planned changes are made
- Statistical description of stability requires that ‘pattern of variation’ remains stable over time, not that there be no variation in the variable measured

• Process is in control has only random cause variation (inherent variability of system)

• When the normal functioning of the process is disturbed by some unpredictable events, special cause variation is added to common cause variation

Control Chart

A graphical display of data over time (displayed in time sequence they occur/measure)

- Used to differentiate common cause variation from special cause variation
- Combine numerical & graphical description of data, using sampling distribution

• Normal distribution is basis for control chart

• Goal of using this chart is to achieve & maintain process stability

Basic Components of a Control Chart

A control chart always has

- A central line usually mathematical average of all the samples plotted
- Upper control & lower control limits defining the constraints of common variations or range of acceptable variation

• Performance data plotted over time

• Lines are determined from historical data

Control Chart Basic Procedure

• Choose the appropriate control chart for the data

• Determine the appropriate time period for collecting & plotting data

• Collect data, construct the chart & analyze the data

• Look for “out-of-control signals” on the control chart.

- When one is identified, mark it on the chart & investigate the cause. Document how investigated, what learned, the cause & how it was corrected

• Continue to plot data as they are generated. As each new data point is plotted, check for new out-of-control signals

A process is in control

• No sample points outside limits

• Most points near process average

• About equal number of points above and below centerline

• Points appear randomly distributed

A process is assumed to be out of control

• Rule 1: A single point plots outside the control limits

• Rule 2: Two out of three consecutive points fall outside the two sigma warning limits on the same side of the center line

• Rule 3: Four out of five consecutive points fall beyond the 1 sigma limit on the same side of the center line

• Rule 4: Nine or more consecutive points fall to one side of the center line

• Rule 5: Six or more consecutive points steadily increasing or decreasing

Types of the control charts

• Variables control charts

- Variable data are measured on a continuous scale (time, weight, distance etc.)
- Applied to data with continuous distribution

• Attributes control charts

- Attribute data are counted & can’t have fractions or decimals (success or failure, accept or reject, correct or not correct etc.)
- Applied to data following discrete distribution

Use of control chart

• Control ongoing processes by finding & correcting problems as they occur

• Predict the expected range of outcomes from a process

• Determine whether a process is stable (in statistical control)

• Analyze patterns of process variation from special causes (non-routine events) or common causes (built into the process)

• Determining whether the quality improvement project should aim to prevent specific problems or to make fundamental changes to the process