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Design of Experiments (DOE) Training Course

Replaces trial and error with planned experiments, from choosing factors and designs to analysing effects and finding the best operating conditions.

SPC · Statistical Process Control & Quality AnalyticsAll Levels#2552ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Course Overview

This advanced practical course develops the capability to use Design of Experiments (DOE) as a structured method for identifying the factors that influence process and product performance and determining how those factors can be optimized. The programme focuses on replacing trial-and-error approaches with controlled, data-driven experimentation. Participants will learn how to define experimental objectives, select relevant factors and responses, establish appropriate experimental designs, and analyse results to determine significant effects and interactions. Emphasis is placed on translating statistical findings into practical process improvement decisions. The course covers experimental planning, factor screening, factorial designs, interaction analysis, response optimization, and confirmation testing. Practical exercises enable participants to design and interpret experiments using realistic operational and quality scenarios. DOE is also connected with process improvement, Statistical Process Control, process capability, root cause analysis, and continuous improvement to support systematic optimization of critical processes.

Learning Objectives

  • Analyse the principles, purpose, and applications of Design of Experiments.
  • Define clear experimental objectives and measurable process responses.
  • Identify critical factors and potential sources of process variation.
  • Select appropriate experimental designs based on process objectives and constraints.
  • Apply screening techniques to identify significant process factors.
  • Develop factorial and fractional factorial experiments.
  • Evaluate main effects and interactions between experimental factors.
  • Assess experimental results using appropriate statistical methods.
  • Identify optimal factor settings for improving process performance.
  • Develop confirmation experiments to validate improvement results.
  • Integrate DOE with SPC, process capability, root cause analysis, and continuous improvement.
  • Establish practical DOE methodologies for repeatable process optimization.

Who Should Attend

This course is designed for Quality Managers, Quality Engineers, Process Engineers, Manufacturing Engineers, Production Managers, Research and Development professionals, and Continuous Improvement specialists involved in process optimization and quality improvement. It is also suitable for professionals working in operations, laboratories, product development, engineering, reliability, process improvement, and operational excellence where controlled experimentation can support better technical and business decisions. The programme is particularly relevant to organizations seeking to reduce process variation, identify critical process drivers, optimize operating conditions, improve product performance, and accelerate evidence-based improvement initiatives.

Learning Outcomes

  • Explain the role of DOE in systematic process improvement.
  • Define experimental objectives, responses, factors, and measurable success criteria.
  • Identify controllable and potentially influential process factors.
  • Select an experimental design appropriate to a specific improvement problem.
  • Construct structured factorial and screening experiments.
  • Analyse main effects and interactions between factors.
  • Interpret experimental results and identify statistically meaningful relationships.
  • Distinguish significant process drivers from insignificant factors.
  • Determine factor settings that support improved process performance.
  • Design and evaluate confirmation experiments.
  • Use DOE findings to support process control and continuous improvement.
  • Develop an actionable DOE-based optimization plan for a selected process.

Course Outline

Day 1

DOE Principles, Experimental Strategy & Process Factors

  • Fundamentals and purpose of Design of Experiments.
  • DOE versus traditional trial-and-error experimentation.
  • Defining the business and technical problem.
  • Establishing experimental objectives and measurable responses.
  • Identifying controllable, uncontrollable, and noise factors.
  • Selecting critical process inputs and outputs.
  • Understanding factor levels and experimental ranges.
  • Experimental units, replication, randomization, and blocking.
  • Sources of experimental variation and potential bias.
  • Practical Application: Develop an experimental problem statement and identify the critical factors and responses for a selected process.
Day 2

Screening Designs & Factorial Experiments

  • Principles of factor screening.
  • Selecting factors for initial experimentation.
  • Full factorial experimental designs.
  • Main effects and their interpretation.
  • Interaction effects between factors.
  • Fractional factorial designs and their applications.
  • Understanding confounding and design limitations.
  • Determining appropriate factor levels.
  • Planning experimental runs efficiently.
  • Practical Application: Build a factorial experiment to identify the factors that have the greatest influence on a selected process response.
Day 3

DOE Data Analysis & Interpretation

  • Preparing and validating experimental data.
  • Analysing experimental effects.
  • Interpreting main-effect and interaction plots.
  • Evaluating statistical significance.
  • Understanding analysis of variance within DOE.
  • Assessing model adequacy and experimental error.
  • Identifying meaningful interactions.
  • Recognizing overfitting and misleading relationships.
  • Translating statistical results into process decisions.
  • Practical Application: Analyse an experimental dataset and identify the factors and interactions responsible for significant process changes.
Day 4

Process Optimization & Confirmation Testing

  • Moving from factor identification to process optimization.
  • Selecting optimal factor combinations.
  • Evaluating response behaviour under different conditions.
  • Balancing multiple process responses.
  • Developing practical operating windows.
  • Predicting process performance at selected settings.
  • Designing confirmation experiments.
  • Comparing predicted and actual results.
  • Validating improvement before full-scale implementation.
  • Practical Application: Develop an optimized process setting and conduct a confirmation analysis to verify the expected improvement.
Day 5

DOE Integration, Implementation & Continuous Improvement

  • Converting DOE findings into process improvement actions.
  • Integrating DOE with SPC and process capability analysis.
  • Linking experimental findings with root cause analysis.
  • Establishing controls for optimized process conditions.
  • Documenting experimental methods and results.
  • Communicating DOE findings to technical and management stakeholders.
  • Identifying opportunities for further experimentation.
  • Developing a structured DOE governance and review approach.
  • Sustaining gains after experimental optimization.
  • Final Workshop: Develop and present a complete DOE improvement plan for a selected process, covering objectives, factors, experimental design, analysis approach, optimization strategy, confirmation testing, and implementation controls.

Upcoming Dates

Selected date

Salalah · 15 – 19 Mar 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
DammamScheduled
2 – 6 Nov 2026€4,200—
DohaScheduled
9 – 13 Nov 2026€4,400—
DubaiScheduled
15 – 19 Nov 2026€4,400—
BangkokScheduled
16 – 20 Nov 2026€6,100—
FrankfurtScheduled
23 – 27 Nov 2026€5,800€5,220until 2 Nov
GenevaScheduled
30 Nov – 4 Dec 2026€6,300€5,670until 9 Nov
IstanbulScheduled
30 Nov – 4 Dec 2026€4,700€4,230until 9 Nov
BarcelonaScheduled
7 – 11 Dec 2026€5,400€4,860until 16 Nov
Kuala LumpurScheduled
7 – 11 Dec 2026€4,400€3,960until 16 Nov
LisbonScheduled
14 – 18 Dec 2026€5,400€4,860until 23 Nov
LondonScheduled
21 – 25 Dec 2026€5,600€5,040until 30 Nov
BeirutScheduled
27 – 31 Dec 2026€4,000€3,600until 7 Dec
MadinahScheduled
28 Dec 2026 – 1 Jan 2027€4,200€3,780until 7 Dec
MadridScheduled
4 – 8 Jan 2027€5,400€4,860until 14 Dec
MakkahScheduled
11 – 15 Jan 2027€4,200€3,780until 21 Dec
ManamaScheduled
11 – 15 Jan 2027€4,700€4,230until 21 Dec
CairoScheduled
17 – 21 Jan 2027€3,300€2,970until 28 Dec
MarbellaScheduled
18 – 22 Jan 2027€5,400€4,860until 28 Dec
MarrakeshScheduled
25 – 29 Jan 2027€4,300€3,870until 4 Jan
MilanScheduled
1 – 5 Feb 2027€6,000€5,400until 11 Jan
MunichScheduled
8 – 12 Feb 2027€5,400€4,860until 18 Jan
Cape TownScheduled
8 – 12 Feb 2027€5,100€4,590until 18 Jan
MuscatScheduled
15 – 19 Feb 2027€5,300€4,770until 25 Jan
OnlineScheduled
21 – 25 Feb 2027€1,700€1,530until 1 Feb
ErbilScheduled
28 Feb – 4 Mar 2027€3,100€2,790until 8 Feb
RiyadhScheduled
1 – 5 Mar 2027€4,200€3,780until 8 Feb
ParisScheduled
1 – 5 Mar 2027€5,700€5,130until 8 Feb
RomeScheduled
8 – 12 Mar 2027€5,800€5,220until 15 Feb
SalalahScheduled
15 – 19 Mar 2027€5,300€4,770until 22 Feb
JeddahScheduled
21 – 25 Mar 2027€4,200€3,780until 1 Mar

Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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