Design of Experiments - DoE - MTS Consulting Partner

Training Information

DoE – experimental design DoE (Design of Experiments) is a structured, statistical planning and execution of processes to obtain relevant product and process parameters that influence their respective influence on output variables of interest. DoE provides a data model on the cause-and-effect structure of products or processes.

From this, characteristics and interactions as well as the optimal settings can be determined. Get an overview of DoE’s practices and techniques in a mix of training and workshop exercises. The learned knowledge can be profitably converted into operational practice.

Duration	3 days from 9am to 5pm
Date	–
Venue	–
Cost excl. VAT	–
Qualification	After completing the seminar, each participant will receive a certificate of attendance.
Minimum participants	–
Prerequisites	Each participant must have a notebook with the software Microsoft® Excel from 2003, MINITAB® from R14 and Adobe® Reader® from 7.0. The software MINITAB® can be obtained from us.
Print Description

Objective:

With the method of statistical experimental design, you learn to gain maximum results about the effects of influencing parameters and with as little effort as possible. It will show you solutions how to improve the quality of your products and processes efficiently.

Target group:

Employees and executives from the fields of engineering, quality management, production, CIP, technicians as well as R & D from manufacturing companies and service companies.

Seminar content:

Introduction to DoE – Design of Experiments – Overview
Basics of statistics and regression and analysis of variance
Elementary capability metrics
Calculation of required sample sizes and experiments
Preparation of experimental plans

Systematic derivation of the relevant input information for DoE’s
Review of the understanding of Y and X variables
Dealing with noise variables
Handling of continuous and discrete variables
Setting up a roadmap for planning a DoE
Selection of factors
Determination of the factor levels, determination of the test room
Selection of the design on the basis of existing (data-based) knowledge
Determination of the number of replications, sampling theory to DoE
Preparation of documentation of results and accompanying information
Development of suitable templates
Consideration of the experimental environment
Coordination with stakeholders, e.g. in the production
Influence of measuring systems on a DoE

Experiment designs and evaluation

One factor method (OFAT = One Factor at Time)
Full factorial design plans (2 ^ k Full Factorial)
Setting up a DoE test plan
Effects of major factors and interactions
Graphical representation of the results (main effect diagram, interaction diagram, cube diagram)
Calculation of the effects
Determination of statistically significant effects
DoE – exercise on an example

Partial factorial designs (fractional factorial designs)

Screening designs to determine the key factors
Statistical analysis of test results, regression modelling
Residual analysis with interpretation
Further use of a model (output forecasts, “best settings” in production
Desirability – Functions
Recognizing and interpreting critical states in (product and process) designs
Exploring the performance limit of a design
Handling, weighting and meaning of several Y-variables
Simulation options, Response Optimization
DoE and variation reduction
Response surface designs
Approach and theory of the central composite design (CCD) including variants
Box Behnken Design
D-optimal designs (approach, benefits and risks)
Orthogonality and rotation
Theory for the statistical evaluation of RSDs
Handling a complete roadmap for analysis and modelling
Non-linear desirability functions
Evolutionary Optimization (EVOP)

Outlook

Mixing experiments
Application situations and approach
Variants
Statistical modelling

Taguchi Designs

Application situations and approach
Planning, selection and integration of noise factors
Statistical analysis
Analysis and interpretation of the robustness of designs

Build a comprehensive DoE roadmap with all branches
Systematic expansion of acquired DoE knowledge in the company
Setting up and targeted use of process algorithms (transfer functions)
Optimization of process chains, production lines etc.
DoE in DMAIC and DFSS projects
Targeted use of DoE to set tolerances
DoE to Data Mining interface

“DoE” in interaction with “neural networks” or decision trees

Tips and exchange of experience

Dr. Dietmar Stemann

Managing Partner, Master Black Belt

Dr. Dietmar Stemann is the Managing Director of MTS ConsultingPartner and he has many years of experience in product and process improvement with Lean Six Sigma and Design for Six Sigma (DFSS). Its strength lies in the consistent introduction and implementation management for Six Sigma and Lean. He has in-depth experience in the international upper middle class and international corporations.

Thomas Solecki

Senior Consultant Six Sigma Master Black Belt

Thomas Solecki has been a certified Six Sigma Black Belt since 2003 and has completed an apprenticeship within Lean Project Implementation. Many years of professional experience in the automotive industry as a project manager and an expert in methods – with a focus on Six Sigma and Lean – shaped his sense of process optimization. His industry expertise comprises of automotive industry as well as the electronic industry.

Gebhard Mayer

Managing Partner, Master Black Belt

Dipl. Eng. Gebhard Mayer has very intensive and longterm experiences in the Management of R&D, production, Quality and also Engineering. His spectrum of competencies contains many items from different industrial branches.
His main strenghts are a systematic way of working, paired with data ananalytics as well as root cause thinking and a visualization which is very important and helpful in complex projects.