Welcome!
FREE PREVIEWWhat is DOE? And how can I use it to make better pizza!
FREE PREVIEWQuiz 1
FREE PREVIEWAssignment 1
DOE Vs. OFAT.
FREE PREVIEWAssignment II
Steps to apply DOE
FREE PREVIEWIntroduction to the P-Value
FREE PREVIEWWhat is P-Value ?
FREE PREVIEWAssignment 3
Quiz 2
FREE PREVIEWSummery for Section one
FREE PREVIEWWelcome to Section 2
FREE PREVIEWDefining the factors and the output.
FREE PREVIEWExercise
FREE PREVIEWAssignment 1
Introduction to the DOE Designs and factor levels
FREE PREVIEWQuiz 1
Quiz 2
Assignment 2
The concept of the full factorial design
FREE PREVIEWAssignment 3
Example (Replication and Center point method)
FREE PREVIEWQuiz 3
Assignment 4 (Bonus)
Summery for section 2
FREE PREVIEWTh Fractional factorial design
FREE PREVIEWThe concept of the Fractional factorial design
FREE PREVIEWQuiz 1
Assignment 1
Quiz 2
Fractional factorial design (The Resolution)
FREE PREVIEWQuiz 3
Quiz 4
The trade-off table
FREE PREVIEWAssignment 2
Practical example for the resolution
FREE PREVIEWQuiz 5
Exercise 2
FREE PREVIEWAssignment 3
Optimization methods (Response Surface method) (The Section is in Progress)
FREE PREVIEWIntroduction to the response surface methodology (RSM)
FREE PREVIEWCentral Composite Design (CCD)
FREE PREVIEWQuiz 1
Assignment 1
Practical example using CCD
FREE PREVIEWRandomizing & Blocking
FREE PREVIEWAssignment 2
My experience with the DoE course is outstanding. The whole course is designed carefully with the best possible graphic description. It would take months if I would try to learn the DoE through other platforms. I am very much looking forward to en...
Read MoreMy experience with the DoE course is outstanding. The whole course is designed carefully with the best possible graphic description. It would take months if I would try to learn the DoE through other platforms. I am very much looking forward to enrolling in their other courses as well.
Read LessExtensive resources provided!
Extensive resources provided!
Read Lessand prove your skills
All the steps to apply a doe method.
How to choose the suitable factors for doe, and what the factor levels are.
The different doe designs (full factorial design, fractional factorial design, center point method and Central Composite Designs (CCD))
How to calculate the effect of the factors by using simple mathematical equations
Effect diagram, regression equation, alias structure.
What response surface method is, and how we use it to optimize a process.
How to carry out an experiment and what randomization and blocking are.
Some of the statistical basics, for example what P-Value is.
Using Program Minitab (Appling what we learned on a project from real life).
Welcome to Excedify's self-paced online Design of Experiments (DOE) Training, a transformative learning experience that equips participants with the essential skills to drive data-driven decision making and process optimization. This comprehensive course offers the flexibility to learn at your own pace while benefiting from expert instructor support throughout your journey.
In today's fast-paced world, organizations rely on efficient experimentation and data analysis to enhance product quality and operational efficiency. The DOE methodology empowers professionals to make informed decisions through structured experimentation and statistical analysis.
Key Learning Objectives: Our engaging online DOE Training is designed to help you achieve the following key objectives:
Fundamental Principles: Gain a deep understanding of DOE fundamentals, including the principles of experimental design, factors, levels, and responses.
Planning Effective Experiments: Learn how to strategically plan experiments to optimize resources and meet specific research or process improvement goals.
Data Collection and Analysis: Acquire hands-on experience in data collection methods and statistical analysis using popular software tools, all within the online platform.
Interpretation and Inference: Develop proficiency in interpreting experimental results, identifying influential factors, and drawing meaningful conclusions.
Advanced DOE Techniques: Explore advanced DOE methodologies like factorial designs, response surface methodologies, and mixture experiments for complex optimization challenges.
Real-World Applications: Apply DOE concepts to real-world case studies and practical scenarios, with the flexibility to access course material and exercises whenever convenient for you.
Who Should Attend: Our self-paced online DOE Training welcomes a diverse audience, including engineers, researchers, R&D professionals, data analysts, quality assurance experts, and anyone engaged in process improvement initiatives. The course accommodates your busy schedule, allowing you to learn and progress at your own speed.
Course Features:
Embark on a Journey of Excellence: At Excedify, we believe in empowering professionals like you with knowledge and skills that lead to organizational success. Enroll in our self-paced online Design of Experiments (DOE) Training and gain the confidence to drive data-driven decision making, optimize processes, and achieve your professional goals.
To enroll or inquire further, please contact us at [email protected] . Our team is eager to support your learning journey and ensure a fulfilling experience with our DOE Training.
Empower yourself and your organization with data-driven excellence. Enroll in the Design of Experiments (DOE) Training today!
we offer a 30-day money-back guarantee period in which you can view and engage with all the course material. If you did not love the course for any reason, we will return 100% of the paid price. If you issue the refund after you completed the course, we will refund 50% of the paid price and the course certificate will be revoked.
Yes, you can pay using one of the following methods: just email us at [email protected] stating which course you would like to buy. PAYMENT METHODS: Apple Pay; Google Pay; Alipay; WeChat Pay; Bancontact; EPS; giropay; iDEAL; Przelewy24; Sofort; Klarna; Bank debits; SEPA Direct Debit;
Yes, you can buy a bulk license with one custom order. Just email us at [email protected] with the required number of users and the course of interest. We will take care of the bulk enrollment and account setup. We offer company communities and progress reporting for our B2B customers.
Design of Experiments (DOE) is a structured, statistical approach to planning and analyzing controlled experiments so you can identify which input variables (factors) significantly impact an output (response). Wikipedia +2 JMP +2 Through DOE, instead of changing one factor at a time, you change multiple factors simultaneously and use models to understand main effects, interactions, and optimize performance.
It accelerates improvement by revealing which factors matter most and how they interact (vs. trial-and-error). It helps optimize processes (maximize, minimize, or hit target values) more efficiently. It supports data-driven decisions rather than guesswork, reducing cost and waste. It’s a key tool in Six Sigma, R&D, manufacturing, and quality assurance roles.
Changing only one factor at a time assumes that factors change independently. But real systems often have interactions between factors. DOE allows you to estimate both main effects and interactions in fewer experiments than exhaustive brute force. Using DOE, you get more insight with fewer runs than brute force or OFAT.
Typically, a comprehensive DOE course includes: Screening designs (e.g. fractional factorial, Plackett-Burman) for identifying influential factors. Factorial designs (full and fractional) to estimate main and interaction effects. Response Surface Methodology (RSM) designs (Central Composite, Box-Behnken) for optimizing around a local region. Specialized designs: mixture designs, split-plot, custom/optimal designs. Robust design / noise factor designs (Taguchi, robust parameter design) We’ll also address design evaluation, replication, randomization, blocking, and model diagnostics.
You should be comfortable with introductory statistics: hypothesis testing, confidence intervals, regression, ANOVA. Advanced calculus is usually not necessary, but familiarity with linear models (matrix thinking) helps. If your grasp is weak, we often provide a statistics refresher module before diving into DOE.
In the course, you’ll learn DOE using one or more of: Minitab, JMP, R / Python (stats/design packages), Excel with add-ins, or commercial tools like Design-Expert. You don’t need deep prior experience—basic familiarity with the software interface is enough. We provide templates, scripts, or starter files to jump in quickly.
Most practical DOE training takes 4–8 weeks (if part-time) or 1–2 weeks intensive (full days). Expect to invest 3–6 hours per week if self-paced. Recordings, lifetime access, or cohort sessions may extend or compress this duration.
Yes, successful completion earns a certificate (PDF/Printed or LinkedIn shareable). We may also align it with industry standards (e.g. ASQ Body of Knowledge) or provide continuing education units (CEUs / PDUs) for quality/engineering professionals.
Yes — DOE is considered an advanced statistical tool in the Improve (or Optimize) phase of DMAIC. Our DOE course often complements or integrates into Six Sigma training, enhancing your credentials in continuous improvement roles.
Factors = input variables you can control (temperature, pressure, speed). Levels = values you assign to each factor (e.g. 100 °C, 120 °C). Responses = the output or outcome you measure (yield, defect rate). Replicates = repeating the same factor settings to estimate experimental error. Blocking = grouping runs to reduce systematic noise (e.g. day, batch). Confounding / aliasing = when two effects (main effect or interaction) cannot be distinguished from one another due to how the design is structured.
RSM is used after you’ve identified key factors: it fits a second-order (quadratic) model to explore curvature and locate an optimal region. Designs used for RSM include central composite and Box-Behnken. You’ll use it when your factor effects are not purely linear and you want to fine-tune settings.
Yes — in cases where factors are not independent (e.g. ingredient formulations that sum to 100 %), mixture designs apply. We also teach constrained or custom design techniques, where you restrict factor combinations for practical or physical reasons.
Yes — for more sophisticated processes (hard-to-change factors, hierarchies), the course will explain: Split-plot and split-block designs Nested and hierarchical designs Custom/optimal designs tailored to your constraints
DOE often reduces time to optimal settings vs trial-and-error, saving cost. It quantifies improvements and supports decisions with statistical evidence. Use simple ROI examples: show how controlling a few factors can yield a measurable gain (e.g. +5 % yield). We provide templates / slide decks to help build internal justification.
Yes — we include real industry datasets, guided exercises, and (depending on format) mentor feedback or peer review of your proposed experimental plans. Some formats include a capstone project applying DOE to a real or simulated problem.
