Using Control Charts as a Process Control Tool in the Lubrication Industry

In many manufacturing processes, the consistency of the process outweighs peak process 
performance.  If a process is not a bottleneck, the drive to achieve peak process performance 
may result in greater costs than the benefits it delivers.  

This is because pursuing peak performance has the potential of pushing the equipment over the 
edge resulting in unplanned stoppage for various reasons.

A good understanding of the variables involved in the process and the behavior of these will 
help instill confidence in the operator resulting in less stress and higher productivity.  This 
coupled with reduced unplanned shut down due to machine failure will deliver greater process 
efficiency.

In a multi-process business unit there may be many identical machines.  Very often, each 
machine has its own “personality”.  These “personalities” when established will give machine 
operators greater understanding, hence confidence in the machine.

Control charts can be used to establish such “personality” by helping to identify variations and 
their sources.

Far from being a passive tool, control charts are also used to actively control and improve 
process performance.

This is done by:
? Detecting and monitoring process variables over time
? Distinguishing special from common causes of variation, as a guide to internal or external 
factors.

Knowing the above will enable the operator to take corrective action to control the process and 
improve its performance consistency and predictability.  This will result in higher quality, lower 
process costs and higher effective capacity.

It has the added advantage of serving as a common language for discussing process problems.

There are different Control Charts suitable for the type of data and lubricant sample size.  In 
lubrication systems one generally use variable data having one sample size.  The types of 
Control Chart would be Individual and Moving Range Charts.
 
The process of constructing the Control Chart would involve the following: 
? Identify the variable to be charted
? Determine the appropriate frequency of sampling
? Establish sampling method making sure the samples taken are representative
? Record the data 
? Calculate the control limits and construct the Control Charts.

A simple Variable Data Individual Control Chart would provide an indication of the range, and 
standard deviations, measured over time.

The Chart will help determine if the process mean (centerline) is where it should be with regard 
to process specification or aim.  If not, either the process is not set up properly or the 
expectation of the operator is not realistic.

Once the expectation and the process performance have been aligned, then the Control Chart 
can be used to analyze if the variable is fluctuating within the normal tolerances (inherent 
common causes) or outside the tolerances due to abnormal (special cause) affecting the 
process.  The normal fluctuation is attributed to system design, choice of machine, preventive 
maintenance and can only be affected by changing that system.  However, special causes are 
attributed to human error, unplanned events, and freak occurrences, that is not part of the way 
the process normally operates or is present because of an unlikely combination of process 
steps.

Special causes must be eliminated before the Control Chart can be used as a monitoring tool.  
Once this is done, the process will be “in control” and samples can be taken at regular intervals 
to make sure that the process doesn’t fundamentally change. 

The process is in “statistical control” if the process is not being affected by special causes.  All 
the points must be randomly dispersed about the average line for an in-control system

Control does not mean the product or service will meet your needs.  It only means that the 
process is consistent (given all factors influencing the process).  Likewise, an out of control 
situation does not mean the product is out of specification, in some instances it could produce 
better quality product if the special cause is the installation of better machinery, for example.












As a guide, a process can be said to be “out of control” if either one of the following is true:

When the control chart is divided into zones, as shown above, any of the following points are 
true:-
? One or more points fall outside of the control limits.
? Two points, out of three consecutive points, are on the same side of the average in Zone A 
or beyond.
? Four points, out of five consecutive points, are on the same side of the average in Zone B 
or beyond.
? Nine consecutive points are on one side of the average
? There are six consecutive points, increasing or decreasing
? There are fourteen consecutive points that alternate up and down.
? There are fourteen consecutive points within Zone C (above and below the average)

Some of the common questions for investigating an out-of-control situation are as follow:
? Are there differences in the measurement accuracy of instruments/ method used?
? Are there differences in the methods used by different personnel?
? Does the environment, e.g., temperature, humidity, affect the process?
? Has there been a significant change in the environment?
? Is the process affected by predictable conditions?  Example: tool wear.
? Were any untrained personnel involved in the process at the time?
? Has there been a change in the source for input to the process?  Example: raw materials, 
information.
? Is the process affected by employee fatigue?
? Has there been a change in policies or procedures? Example: maintenance procedures.
? Is the process adjusted frequently?
? Did the samples come from different parts of the process? Shifts? Individuals?
? Are employees afraid to report “bad news”?

A team should address each “Yes” answer as a potential source of a special cause.
Process Control Chart		OILTECH AUSTRALIA
		TECHNICAL BULLETIN
1
		27-Aug-98