Controlling Process Variations in the Workplace

“Understanding Anomalies, (Noise)”

Joseph F Simpson

5/11/2015

The established process should run consistently, and in most cases will with improvements made methodically by the method to improve.

Where the product is placed by your results

Abstract

The established process should run consistently, and in most cases will with

improvements made methodically by the method to improve.

How

By alluding to these priorities; Application of ownership at the management level by making a plan, interpreting,

presenting it, reporting when necessary, adjusting as needed to fit the condition, and

then reflecting.

Developing employee skill, with increased responsibility continuously.

Utilization of the systems developed, and maintenance of them for improvement.

Continuous communication by managers to employees, and company of the plan;

o Goals

o Expectation

o Responsibility

Reporting by managers of their success, failure, and path forward...

Continuously monitoring condition with “Go & See”.

Why

For the benefit of;

Creating a sustainable system

Measuring return on investment

Improving, by creating a stable system

Gaining confidence.

Creating goods

Making both the tangible and intangible asset

Built in Quality

Respect for People

ANOMALY

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Process & Product variations are difficult to identify, understand their effect,

contain/fix, and finally report from your results, even after thorough root cause analysis

and improvement

Anomaly’s (Noise) in manufacturing; disrupts production in many ways, and in

the end leads to variations that fall well beyond any measures (SPC) established to

control leading to process/product failures. Considering this, anomalies are not

considered process variations because they are not wanton acts or as in variability you

can build in obsolesce.

Results by anomalies that affect;

Productivity

Quality

Part Performance

Cost

Anomaly (from Wikipedia)

1. A deviation from the common type, rule, arrangement, or form; irregularity;

abnormality.

2. Someone or something anomalous.

3. An unexpected, unusual, or strange condition, situation, or quality.

4. Astron. a quantity measured in degrees, defining the position of an orbiting body

with respect to the point at which it is nearest to or farthest from its primary.

(Control)

5. In some fields, anomaly means unwanted information or data that is not relevant

to the hypothesis or theory being investigated or tested.

Having said this and understanding the existence of these factors (anomalies) then

they should fall under two categories “visible, or invisible”

Outside interference can be “visible” because it’s quantitative, can be

measured, and solved as such.

Outside interference can be “invisible” with difficulty because

o The problem is actually not there.

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o We cannot comprehend so it’s not there.

o Its beyond our capability

o It’s hidden by what we can actually see. It occupies the same space as

what we are looking at.

On the other hand outside factors are not actually invisible but sometimes

conveniently not addressed. If manufacturing problems are not addressed it

could simply be out of sight (invisible), out of mind. It’s the excuse as to why

things are the way they are, we accept this as the foregone conclusion since

there is no handy answer. No one should ever feel manufacturing must adjust

conditions on a regular basis because that really tells us interferences &

influences exist.

If you consider your process as multi-dimensional then the anomaly effect is real.

Overall an anomaly is not difficult to understand and is easily defined as:

1. Not part of the general condition, it’s a cause of disruption.

2. Lacks agreeable quality or is noticeably unpleasant. It interferes with one's

intentions or actions.

3. Unwanted signal or a disturbance (as static or a variation).

4. Interfering with the operation of a mechanical device or system.

5. Something that takes you outside stasis, or perfection

6. Something that causes random changes.

a. Creates irrelevant meaningless data

b. Produces results occurring along, or driving outside desired path

The types of anomaly’s can be but are not limited to:

Environmental

Disruptive

Static variations (that with which occurs naturally)

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We should understand completely what creates the anomaly and

contain it. (Understand its effect).

STATISTICAL CONTROL

The effect of anomalies on production cannot be traced by statistical analysis

simply because many of the SPC formulas are written for a process with a stable mean,

meaning they measure the output of an end product. Let us consider SPC to

understand its premise. SPC is based on the idea that these attributes (results) have

two sources of variation: natural (also known as common) and assignable (also known

as special) causes. If the observed variability of the attributes of a process is within the

range of variability from natural causes, then the process is said to be under statistical

control. The practitioner of SPC tracks the variability of the process to be controlled so

when that variability exceeds the range to be expected from natural causes, one then

identifies and corrects assignable causes. In other words the stable process is built as a

consideration of anomalies. In effect statistical control requires time where these

anomalies occur not randomly but with some regular interval.

The rules attempt to distinguish unnatural patterns from natural patterns based on

several criteria:

The absence of points near the centerline

The absence of points near the control limits

The presence of points outside the control limits

Other unnatural patterns (systematic (auto correlative), repetition, trend

patterns)

OCCURRENCE

It doesn’t matter whether manufacturing problems occur beyond any degree within

the design intent. Best Practice is measured by the willingness to respond by; problem

solving, communication to production in controlling said anomalies, fearless of

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consequences in your action, and most importantly the ability in eliminating by providing

expert process control.

What hinders or obstructs this is a variety of situations, or attitudes such as;

1. Simply don’t care, G.A.S. (Fill in the blanks)

2. That we produce products and work each day in the principle by which we

simply get through it. “Whew factor”

3. We are afraid to address it (hopefully will subside or go away eventually)

4. We know everything, or are oblivious to the problem (blame someone or

something, there is no problem here please disperse)

5. We simply don’t have the answer, or think it’s unsolvable.

6. There is no benefit for us to solve said problem. Production demands that we

ignore as long as possible, quality is foregone, productivity is affected, and

excuses pile up.

RANDOM UNWANTED INPUT

An anomaly can be considered random unwanted input (data) without meaning;

that is, data that is combined with process controls or independently, as this changes

the production environment. It is simply produced as an unwanted by-product of other

activities distractions, or actions.

Anomalies can be classified two ways.

Dependent (as required to make)

o Time

o Temperature

o Pressure

o Weight

o Standardization

Independent (foreign to the process)

o Foreign Object

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o Contaminant

o Distraction

Example:

Ambient temperature changes would hamper control

Dust might impact part quality

Someone or something disrupts

Anomalies are by far the single greatest threat to manufacturing

reliability.

It’s a daily occurrence; productivity is affected almost every day by outside

influences. Countless wasted hours are spent troubleshooting, putting out fires, or

babysitting a process in order to meet the production needs of your company. Valuable

time is flushed down the toilet when you should be; developing capital projects,

continuing education, getting ready for launch, attending meetings etc. What this comes

down to be the money wasted, flushed down the drain in matters that are completely

controllable by you. Money is spent installing Automated Process Control but this in

reality is not to be considered a failsafe, at best gives you a false sense of security by

the campaign at the point of sale.

RATIONAL PLAN

1. Develop sound process conditions to achieve product quality confirmation

o Process Control Charts

Include process labels

o Establish realistic criteria for hand-over

Example: Handover CriteriaParts Breakdow

nPunch

ListScrap Repair Trial

ShotsCycle Time

Accuracy Fitting Result

#1 <1% 95% < 2% See #1

100 68 100% 100%

See

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#2

Handover variables:

A. Except faults normally caused by machine or skill.

B. Scrap details

a. All shots are for each mold, process, part.

b. Allowed some scrap parts for material changes, mold changes,

and start-up.

c. Parts allowed for start-up.

2. Develop control limits that are sustainable under normal conditions

o Develop baseline for reference

o Allow for variance

o Establish daily check plan by manufacturing

o Go and see mentality to confirm daily

3. Develop real world principles with quality group.

o Making a case when to be objective or subjective

o Have on hand realistic boundary parts

Remember to be through in your development of handover criteria and make

sure not only to have values for scrap, repair, downtime ETC but include clear

statements around real world conditions and capabilities. It is important that conditions

are in place, & ranges exist to create the real world effect, but not far reaching effects.

Remember this does not happen initially as in the start of production because good real

world data is normally established over time, because manufacturing will hone in by

improving product finish & capability, using similar methods applied in the development

processes.

Standards once complete should not be changed easily in the product lifecycle,

because companies must thoroughly understand the effect on products before changes

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Ultimate Goal

Ideal Situation

GAP

Current Situation

Anomalies occupy this

space.

Time

Resul

ts

Results over Time

Anomaly is the following:• Drag (external factors)• Gap (results of products)

are initiated. So with this in mind & with the thorough understanding that random

changes made to appease the anomaly will be reduced dramatically.

Now with all that said, don’t forget to “only” complete 100% of what is needed to achieve

100% capability at handover because in the real world you will never achieve

manufacturing’s expectations and desires. What this means is engineering &

manufacturing people should meet beforehand and agree to what is needed at

handover.

Statistical Process Control ( SPC) & Real World Effect

VARIABILITY IN THE PROCESS:

In agreement we meet the

natural condition;

however we must consider the

mechanism to investigate

problems

that exist outside of that.

MECHANISM OF

DETERMINATION:

There must be

some mechanism in place that preludes rational of problem existence. We must first

observe the actual condition and conclude the following:

It is acceptable and should be debated as such. A mistake of the standard or harsh

judgment.

Critical and must be fixed completely and right.

Subjective and could be allowed to fail as needed to teach.

Simple let it go as their demise.

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What this means is you’re not going to fix everything, and not

everything needs fixing.

Conclusion

We live in a world that demands instantaneous results, and quick fixes. Many

problem-solving methods exist.

Eight Steps PDCA TPS Black Belt.

These techniques solve problems regularly by engineering and manufacturing expertise.

This article is not about the solution but the existence of an anomaly by poor engineering/management methods, techniques, and judgement.

The question remains on judgment:

1. Is there an actual problem?

2. Do you really understand the quality requirements?

3. Do you have a temporary plan in place?

4. Do you maintain sample parts that show condition variability, and real world?

5. Is the problem solved?

6. Do you maintain standards that show normal condition, and ranges?

7. When all is said and done are we back to stasis?

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