statistical process control (spc)

Statistical Process ControlUnderstanding and Applying Statistical Process Understanding and Applying Statistical Process

Control to Improve PerformanceControl to Improve Performance

By: Jeffry YorisBy: Jeffry Yoris

Objectives

• To understand concept of SPCTo understand concept of SPC

• To be able to use SPC in To be able to use SPC in controlling & improving your controlling & improving your process process

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What We’ll Discuss Today…

1. Basic Statistics• Central Tendency & Dispersion• Normal Distribution Table

2. Statistical Process Control– Definition– Concept of Variation– Why 3 Sigma Limit ?– Stable and Capable Process

3. Constructing Control Chart – Select Critical Process to Control– Selecting Control Charts– Determining Control Limits– Constructing Control Charts

4. Monitoring & Improvement With SPC– Interpreting Control Chart– Reduce Variation– Shifting Means

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Central Tendency & Dispersion

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Normal Distribution

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95%

99.74%

Central Limit Theory

Mean, Average Std.

Deviation

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• Constructing Control Chart – Select Critical Process to Control– Selecting Control Charts– Determining Control Limits– Constructing Control Charts


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Statistical Process Control

• SPC is The Use Of Control Charts To Determine If A Process Is “In Control”.

• It answers:– Is a process stable? – if it is unstable, when is it occuring?

So, What Is “Stable”?

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Concept of Variation

• Variation is expected. It’s natural.

– Darwin on natural selection: “Individual within species always varies. The fittest survive”.

– Deming on business: “Change is mandatory for survival. Survival is not mandatory”.

ProcessInput Output

Random factors affecting

performance

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Two Types of Variation

1. Expected Variation– “Noise”

– Common Cause– Normal– Random but stable– Consistent pattern over

time– Predictable

2. Unexpected Variation– “Signals”

– Special Cause– Not normal– Not random– Inconsistent pattern

over time– Unpredictable

Stable Process: No unexpected variation


Why 3 Sigma Limits ?

• Probability of a process to produce variation outside its +/- 3 sigma limit is 0.26%, therefore this variation is unexpected.

• We may conclude that this variation is not coming from the “standard” process, but due to:– Different method– Different inputs– Untrained operator– Non-standard equipment– Other “special” causes.

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95%

99.74%


Stable and Capable Process

• A stable process is at the state in which all special causes of process variation have been removed and prevented from recurring so that only the common causes of process variation remain.– All points within control limit (U/LCL)

• A capable subprocess is a process that can satisfy its specified product quality, service quality, and process-performance objectives.– All points within specification limit (U/LSL).

• A process can be stable but not capable, and vice versa.

• Objective: Stable and Capable Process


Select Critical Process To Control

• “To a hammer, everything is nail”.

• You only have 24 hours a day. You can’t control every thing.

• Select critical process(es) to monitor and improve.

• “Critical” maybe defined as:– Important to customer satisfaction– Important to achieve business objectives– Affect overall process performance– Others?


Attribute & Variable Data

1. Attribute• A product characteristic that

can be in discrete response• Good-bad, yes-no, defect-no

defect.

2. Variable• A product characteristic that

is continuous and can be measured

• Weight, length, duration

• For attribute data, construct ONE control charts

• For variable data, construct TWO control charts:

• X Chart=To Measure variation between subgroups (mean, median, individual)

• R Chart to measure variation within subgroups (range or standard deviation


Components in a Control Chart


Constructing Control Chart1

1. Select the process to be chartered2. Determine sampling plan

• Size of samples• Frequency• Assure randomness

3. Collect data (variable or attribute)4. Calculate central line 5. Calculate control limits (CLs)6. Construct control charts


Constructing Control Chart2

Notes;

• Any points outside CLs, once identified with a special cause(s) should be removed and re-calculate the charts. Points within control limits but showing trends, shift, or instability are also special causes.

• After doing no.1, continue to plot new data on new chart, but DO NOT change CLs.

• Change CLs when a permanent, desired change has occurred in the process, and only using data AFTER the change occured


Selecting Control Charts


Attribute Data Table


Variable Data Table


Table of Constants


Interpreting Control Chart

• Does your process meet customer specification (U/LSL)?• Do unexpected variations exist?• Does your control chart have:

– No point outside control limit ?– Most points appear near centerline ?– About equal number appear below and above average ?– No pattern or trend. Points appear randomly distributed ?

• Does your process average change?• Does your for customer specification (U/LSL) change?

• Action!


Revising Control Limits UnMasks Other Sources of Variation


Steps for Improvement

SPC Cycle of Improvement:• Standardize Process• Monitor With Control Chart• Identify Special Causes• Remove Special Causes• Increase Stability: Reduce Variation Due To Random

Causes• Increase Capability: Shifting The Process Centerline• Back To 1.

Nothing will change just because you make control chart of it.

statistical process control (spc)

Business