Far Shore Ltd, ‘Realising your Organisations Potential’
Course Outline for Design of Experiments Training
Introduction
The term experiment is defined as the systematic procedure carried out under controlled conditions
in order to discover an unknown effect, to test or establish a hypothesis, or to illustrate a known
effect. When analysing a process, experiments are often used to evaluate which process inputs have
a significant impact on the process output, and what the target level of those inputs should be to
achieve a desired result (output). Experiments can be designed in many different ways to collect this
information. Design of Experiments (DOE) is also referred to as Designed Experiments or
Experimental Design - all of the terms have the same meaning.
Design of Experiments can be used at the point of greatest leverage to reduce design costs by
speeding up the design process, reducing late engineering design changes, and reducing product
material and labour complexity. Designed Experiments are also powerful tools to achieve
manufacturing cost savings by minimizing process variation and reducing rework, scrap, and the
need for inspection.
Design of experiments or DOE is a set of advanced analytical tools based on mathematical
techniques. Using special computer software, it is possible to gain a deep understanding of the
processes, including the impact of interactions among factors, and to do so in the most efficient
manner with minimum numbers of experimental runs.
These tools or DOE will be presented on this training course.
Timescale
2 day full time course + post course follow up
Who should attend?
Anyone involved in:
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product and process development,
process and product optimisation and improvement,
validation and technical services,
Requirements
It is useful if participants have a basic understanding of statistics, including hypothesis testing and
regression.
Outline course content
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Why DOE is so important and provides such an opportunity to business.
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DOE basics
Factorial designs
Reducing Experimental Trials
Planning and Preparing for a Designed Experiment
Response Surface Methodologies
Subjects Covered
Factorial Designs
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Steps in data analysis
Residuals
Assumptions for DOE analysis
Identifying large effects
Main effects
Interpreting interaction plots
Identification of significant effect
Viewing effects on the response
Main effects plots
Interaction plots
Cube plots
Prediction equation
Reducing experimental trials
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The half fraction and confounder
Fractional factorials
Confounding
Resolution
Screening designs
Plackett Burman designs
Planning and Preparing for a Designed Experiment
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Response Variables
Choosing factors
Setting levels for each factor
Replication
Randomisation
Blocking
Response Surface Methodology
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Quadratic model
Designs
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Contour plots
Software
Minitab will be demonstrated as part of the training. Delegates are invited to bring a laptop loaded
with either Minitab 16 or Minitab 17 and they will work through several Minitab exercises
throughout the course. A free 30 day trial version of Minitab 17 is available on
www.minitab.co.uk.
Course Tutor
Nick Ustianowski is a Master Black Belt in six sigma and lean. He has more than 17 years of
experience of implementing Operational Excellence within the pharmaceutical and other regulated
industries. Nick is currently working within the pharmaceutical, food, high technology and health
service sectors as a trainer, business coach and transformational agent.
Course Manual
Delegates will receive a comprehensive course manual written by the course tutor, which explains
the underlying statistics, describes the principles of experimental design, explains in detail how
experiments are designed and analysed, includes examples of several practical case studies, and
incorporates completed versions of all the course exercises and graphs, including the output from
Minitab computer software. The course manual will provide a very useful reference for participants
undertaking the design and analysis of experiments when they return to their workplace.
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