Statistical process control workshop
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Statistical Process Control Workshop. An Introduction to the Principles behind SPC. Ilca Croufer. Workshop Outline. 1. Statistical Process Control a ) Definition b) Benefits & Tools 2. Process a) Definition b) Common & Special Cause Variation 3. Statistics Revision

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Statistical Process Control Workshop

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Statistical process control workshop

Statistical Process Control Workshop

An Introduction to the Principles behind SPC

Ilca Croufer

Workshop outline

Workshop Outline

1. Statistical Process Control

a) Definition

b) Benefits & Tools

2. Process

a) Definition

b) Common & Special Cause Variation

3. Statistics Revision

a) Mean, Variance and Standard Deviation

b) Normal Distribution

4. Control Charts

5. Control Chart Construction

6. Examples of Root Cause Analysis Techniques

a) 5 Whys

b) Fishbone Diagram

Statistical process control spc

Statistical Process Control (SPC)

Statistical process control (SPC) is a methodology focused on quality control and improvement, using data analysis

It consists of using valid statistical data analysis to determine and eliminate variation due to assignable causes.

It is based on the following principles:

- measuring the process

- identifying and eliminating unusual variation

- improving the process to its best target value

- monitoring the process performance over time

Benefits of statistical process control

Benefits of Statistical Process Control

Process improvement

In-depth management decision making

Better understanding of process performance

Establish process baselines

Better control on variables that impact a process

Gain More predictability

Examples of tools used in spc

Examples of Tools used in SPC


Scatter Diagrams

Run Charts

Pareto Charts

Control Charts

Process variation

Process & Variation

An activity which transforms inputs into outputs; (F(x) = Y)

X f(X) Y

Example: making a cup of tea, baking a cake, getting to work, etc.

Any process will have a certain degree of variation; some variation will be inherent to the process, some will not.

Variation in a Process = Common Cause Variation + Special Cause variation




Common cause vs special cause variation

Common Cause vs Special Cause Variation

Common Cause Variation:

- Irregular variation within an historical experience base

- Naturally present within the system

- Usually insignificant and predictable

Special Cause Variation

- Variation outside the historical experience base

- Assignable to a root cause

- Usually significant and unpredictable

Statistics revision i

Statistics Revision I

Distribution: arrangement of values of a variable showing their observed or theoretical frequency of occurrence.

E.g.: 21 25 33 33 45 47 51 55 55 61

A distribution is usually characterised by its mean, standard deviation and shape

In Statistics, different types of distribution exist, with the Normal Distribution being the most well-known and commonly used.

Statistics revision ii

Statistics Revision II

Mean = ∑ (sum of observed values)

Number of observations

Variance = ∑ (observed value – mean)2

Number of observations

Standard Deviation ( ) = √ (Variance)

Example ii

Example II

Consider the following distribution:

11, 17,25, 28, 34

Calculate the mean, variance and standard deviation.

Example ii solution

Example II - Solution

Mean = (11+17+25+28+34) = 23


Variance = (11-23)2+(17-23)2+(25-23)2+(28-23)2+(34-23)2 = 66


Standard Deviation = √66 = 8.12

Exercise i solution

Exercise I - Solution

Mean = (1.0+0.8+0.8+1.2) = 0.95


Variance = (1.0-0.95)2+(0.8-0.95)2+(0.8-0.95)2+(1.2-0.95)2 = 0.0275


Standard Deviation = √0.0275 = 0.17

Normal distribution curve

Normal Distribution Curve

Bell-shaped curve

Symmetrical around the mean

Defined by its mean and standard deviation

68.3% of data found within one standard deviations away from the mean

95.5% of data found within two standard deviations away from the mean

99.7% of data found within three standard deviations away from the mean

Control charts

Control Charts

Statistical tool used to

monitor the stability

of a process over time

Key features:

- UCL (Upper Control Limit) = mean + 3*sigma

- LCL (Lower Control Limit) = mean – 3*sigma

- central line (mean of data set)

A process is said to be in control when data points fall within limits of variation (i.e.: between Upper and Lower Control Limits)

Some control chart rules

Some Control Chart Rules

Rule1: Any point falls beyond 3 sigma from the centre line

Rule2: Two out of three consecutive points fall beyond 2 sigma

on the same side of the centre line

Rule3: Four out of five consecutive points fall beyond 1 sigma

on the same side of the centre line

Rule4: Nine or more consecutive points fall on the same side

of the centre line

Exercise iii part i

Exercise III – Part I

Consider the next set of control charts and identify whether the process is “in-control” or “out of control”:

(a) (b)

Exercise iii part ii

Exercise III – Part II

(c) (d)

Type i type ii errors

Type I & Type II Errors

Type I errors occur when a point falls outside the control limits even though no special cause is operating.

Type II errors occur when you miss a special cause because the chart isn't sensitive enough to detect it

All process control is vulnerable to these two types of errors.

Control charts data types

Control Charts & Data Types

Control charts can measure two types of data:

- Continuous Data(can be measured)

E.g.: temperature, volume, weight, height, time

- Discrete Data(can be counted)

E.g.: How many people in this room?

How many defects in an inspected unit?

There are different control charts to choose from depending on what data is available.

Control chart t ypes

Control Chart Types

Data collection

Data Collection

Before assessing the stability of a process over time using control charts, it is important to identify the type of data at hand, and how to consistently collect and validate it:

1. Decide what to collect: what metrics?

2. Determine the needed sample size

3. Identify source/location of data

4. Is the data in a useable form?

5. Identify how to collect the data consistently and validate it

6. Decide who will collect the data

7. Consider what you’ll have to do with the data (sorting, graphing,


8. Execute your data collection plan

Root cause analysis 5 whys

Root Cause Analysis – 5 Whys

5 Whys: a problem solving tool that helps understand the root cause

of a problem. The 5 Whys technique is usually very quick

and focused.

Key Points:

The 5 Whys strategy is an easy and often-effective tool for

uncovering the root cause of a problem.

Because it's simple, you can adapt it quickly and apply it to

almost any problem.

However, it is important to remember, if an intuitive answer is hard

to find, then other problem-solving techniques may need to be considered.

Root cause analysis fishbone diagram

Root Cause Analysis – Fishbone Diagram

Fishbone diagrams: diagram-based technique, which combines brainstorming with a type of mind map, and forces to consider all possible causes of a problem, rather than just the ones that are most obvious. Fishbone diagrams encourage broad thinking.

Key Points:

1. Identify the problem.

2. Work out the major factors involved.

3. Identify possible causes.

4. Analyse your diagram.



When used correctly, control charts are powerful instruments that can give you visual understanding on the stability of your process

Control charts cannot tell you what is wrong with your process, they can only let you know when something in your process has changed

Control charts can confirm the impact of process improvement activities

Thank you for attending

Thank you for attending

Ilca Croufer

[email protected]

2 Occam Court

Surrey Research Park

Guildford, Surrey


Tel: +44 1483 595 000

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