Managing Uncertainty to Improve Decision Making - Statistical Thinking for Quality Control and Improvement

Managing Uncertainty to Improve
Decision Making
Statistical Thinking for Quality Control and Improvement

Dr. Diego Kuonen, CStat CSci

Statoo Consulting
Statistical Consulting + Data Analysis + Data Mining Services
Morgenstrasse 129, 3018 Berne, Switzerland

www.statoo.info
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Copyright © 2009, Statoo Consulting, Switzerland. All rights reserved.

“In this world there is nothing certain
but death and taxes.”

Benjamin Franklin

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• All managing activities are subject to uncertainty and
decisions have to be made in this environment.

 Uncertainty is everywhere and you can not escape from it.

 It becomes essential to face up to uncertainty, not hide it away
under false concepts, but to understand it and to act in the
face of uncertainty.

 As managing uncertainty leads to better decision making,
the need for a formal tool to deal with uncertainty is greater
now than ever before.

 If you want to manage uncertainty, then you need to practise
statistical thinking – there is no other choice.

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What is statistical thinking?

• Statistical thinking is the philosophy of learning and action
based on the following fundamental principles:

– all work occurs in a system of interconnected processes –
a process being a chain of activities that turns inputs into
outputs;

– variation, which gives rise to uncertainty, exists in
all processes; and

– understanding and reducing variation are keys to
success.

• All three principles work together to create the power of
statistical thinking.

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• The definition above highlights three key components:
– process thinking,
– understanding and managing uncertainty, and
– using data whenever possible to guide actions and to improve
decision-making.

• Statistical thinking is a philosophy – a mind-set.

• Statistical thinking is a an overall approach to improvement
and therefore more broadly applicable than statistical methods.
It is a way of thinking, behaving, working, taking action and
interacting with others.

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Steps in implementing statistical thinking

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“Statistical thinking will one day be as
necessary for efficient citizenship as
the ability to read and write.”

Herbert G. Wells

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The meaning of quality and quality improvement

• The importance of statistical thinking derives from the
modern definition of quality:

“Quality is inversely proportional to variation”.

 If variation in the important characteristics of a product or
a service decreases, the quality of the product or the service
increases.

• Quality improvement is the reduction of variation in
processes.

• Process variation affects not only quality but also
operating cost, cycle time, profitability and customer
satisfaction.

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“If I had to reduce my message for
management to just a few words,
I would say it all had to do with
reducing variation.”

W. Edwards Deming

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Data-driven decision making
• Using statistical thinking and statistical methods to improve
business performance:

• The process focus of statistical thinking provides the context and
the relevancy for broader and more effective use of
statistical methods, which are required to deal with process
variation and hence play a central role in improvement efforts.
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Statistical methods for quality control and improvement

Example production process inputs (e.g. temperatures,
pressures and other process variables are controllable;
environmental factors or properties of raw materials provided by
an external supplier are uncontrollable) and outputs.
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• Since variation can only be described in statistical terms,
statistical methods play a central role in quality
improvement efforts.

 Modern quality assurance systems focus on three major areas.

◊ „Statistical Process Control” (SPC):

– monitoring processes, reducing
variation through elimination of
assignable causes („special causes”);
– systematic use of control charts is an
excellent way to reduce variation;
– on-line technique.

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◊ „Design Of Experiments” (DOE): off-line technique for

– discovering the key factors that influence the quality
characteristics of interest in a process;
– process optimisation by increasing process performance.

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◊ „Statistical Data Mining” (SDM): usage of historical data for

– discovering unexpected relationships between process factors
and quality characteristics;
– monitoring complex processes and investigating process
excursions;
– discovering the key factors that influence quality characteristics
in complex processes and subsequent process optimisation.

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Are you drowning in uncertainty and
starving for knowledge?

Have you ever been Statooed?

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• Statoo Consulting is a software-vendor independent Swiss
consulting firm specialised in statistical consulting and
training, data analysis and data mining services.

• Statoo Consulting offers consulting and training in
statistical thinking, statistics and data mining in
English, French and German.

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Have you been Statooed?

Dr. Diego Kuonen, CStat CSci
Statoo Consulting
Morgenstrasse 128
3018 Berne
Switzerland

email kuonen@statoo.com

web www.statoo.info

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