Stabilizing and Improving Processes with Control Charts: A Comprehensive Guide, Slides of Production and Operations Management

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Chapter 6

Stabilizing and Improving a

Process with Control Charts

Chapter 6

Stabilizing and Improving a Process with

Control Charts

• Sections
  • Introduction
  • Process Variation
  • The Structure of Control Charts
  • Stabilizing a Process with Control Charts
  • Advantages of a Stable Process
  • Improving a Process with Control Charts
  • Two Possible Mistakes in Using Control Charts
  • Stabilizing a process with Control Charts
  • Quality Consciousness and Types of Control Charts
  • Three Uses of Control Charts
  • Take Away Knowledge
  • Summary
  • Key Words
  • References
  • Exercises
  • Appendix 6: Using Minitab for p-charts

Introduction

• A process that has been defined and

documented can be stabilized and then

improved. In great measure this can be

accomplished through the use of statistical

control charts, as well as other techniques.

• Without valid measurements, process

improvements are difficult if not impossible,

and perhaps the best means of measuring

process performance is a statistical control

chart.

• Control charts and other tools and methods we

describe must be used in an environment that

provides a positive atmosphere for process

improvement; and top management must sincerely

desire real process improvement.

• W. E. Deming points out that "any attempt to use

statistical techniques under conditions that rob the

hourly worker of his pride in workmanship will lead to

disaster.“

• With this caveat, we may begin to consider the issues

of stabilizing and improving a documented and

defined process.

Process Variation

• Control Charts and Variation
  • Control charts are used to identify and differentiate

between common and special causes of variation. When

a process no longer exhibits special variation, but only

common variation, it is said to be stable.

• When only common causes of variation are present in a

process, management must take action to reduce the

difference between customer needs and process

performance by endeavoring to move the centerline of

the process closer to a desired level (nominal) and/or by

reducing the magnitude of common variation.

Process Variation

The Need for the Continual Reduction of

Variation

• Deming wrote, "It is good management to

reduce the variation in any quality

characteristic, whether this characteristic

be in a state of control or not, and even

when few or no defectives are being

produced."

• Control charts are constructed by drawing samples

and taking measurements of a process characteristic.

Each set of measurements is called a subgroup.

• Control limits are based on the variation that occurs

within the sampled subgroups.

• In this way, variation between the subgroups is

intentionally excluded from the computation of the

control limits; the common process variation

becomes the variation on which we calculate the

control limits.

• The control limit computations assume that there are

no special causes of variation affecting the process. If

a special cause of variation is present, the control

chart, based solely on common variation, will

highlight when and where the special cause

occurred.

• When the data consist of a series of

fractions that are defective or

possess some other characteristic of

interest, the appropriate control chart

is a p-chart. This is a depiction of

the process output in terms of an

attribute of interest - in our example,

the fraction defective.

• Control limits are calculated as

plus and minus three times the

standard error. The standard error

for the average proportion is:

• Where n is the subgroup size.

n

p( 1 p)

p

−

σ =

• Using this value, the upper and lower

control limits for a p-chart are given by:

n

p( 1 p) LCL(p) p - 3

n

p( 1 p)

UCL(p) p 3

−

−

= +

• The action taken on the process stemming from

investigations of days 8 and 22 should change the

process so that the special causes of variation will

be eliminated. Consequently, the data from days 8

and 22 may now be deleted. After eliminating the

data for the days in which the special causes of

variation are found, the control chart statistics are

recomputed.

Hence,useLCL 0.

LCL(p) 0. 017 3

UCL(p) 0. 017 3

p

Advantages of a Stable Process

• A stable process is a process that exhibits only

common variation or variation resulting from inherent

system limitations. The advantages of achieving a

stable process are:

• Management knows the process capability and can predict performance, costs, and quality levels.
• Productivity will be at a maximum, and costs will be minimized.
• Management will be able to measure the effects of changes in the system with greater speed and reliability.
• If management wants to alter specification limits, it will have the data to back up its decision.
• A stable process is a basic requirement for process

improvement efforts.

Two Possible Mistakes in Using

Control Charts

• There are two types of mistakes that the

user of a control chart may make: over-

adjustment and under-adjustment.

Proper use of control charts will minimize

the total economic consequences of

making either of these types of errors.

Over-adjustment

• The over-adjustment error occurs when the user

reacts to swings in the process data that are

merely the result of common variation, such as

adjusting a process downward if its past output is

above average or adjusting a process upward if its

past output is below average. When a process is

over-adjusted, it resembles a car being over-

steered, veering back and forth across the

highway. In general, processes should be adjusted

-- not on the basis of time-to-time observations --

but on the basis of information provided by a

statistical control chart.