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title: "Expanding the Power of Optimal Design: How Minitab Delivers More with Minitab DOE by Effex"
description: Explore how Minitab’s expanded optimal design capabilities, strengthened by Effex technology, deliver greater flexibility, efficiency, and power for complex, real-world experiments.
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# Expanding the Power of Optimal Design: How Minitab Delivers More with Minitab DOE by Effex
[Joshua Zable](https://blog.minitab.com/en/blog/author/joshua-zable) | 2/17/2026
Topics: [Design of Experiments - DOE](https://blog.minitab.com/en/blog/tag/design-of-experiments-doe), [Minitab DOE by Effex](https://blog.minitab.com/en/blog/tag/minitab-doe-by-effex)
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Design of Experiments (DOE) has long been at the heart of innovation, quality improvement, and product optimization. While classical designs such as full factorial and fractional factorial experiments remain foundational, modern engineering and product development challenges increasingly demand more flexibility. That’s where optimal designs come in — and why Minitab’s continued investment in this area, including the addition of Minitab DOE by Effex’s optimal design capabilities, meaningfully strengthens the portfolio.
## **What Are Optimal Designs?**
Optimal designs are computer-generated experimental designs that are built to satisfy specific modeling or practical constraints. Instead of relying on predefined design structures (like 2^k factorials), optimal designs use algorithms to select the most informative experimental runs based on:
- The model you want to estimate
- The number and type of factors (continuous, categorical, mixed-level)
- Resource constraints (limited runs, restricted factor combinations)
- Custom optimality criteria (D-optimality, A-optimality, I-optimality, etc.)
At their core, optimal designs answer a critical question:
Given real-world constraints, what is the most statistically efficient set of experiments we can run?
This flexibility makes them indispensable in modern industrial, pharmaceutical, advanced manufacturing, and R&D environments where textbook designs often don’t fit the problem.
### New virtual training: Design smarter DOE studies that handle real constraints and optimize multiple responses [](https://blog.minitab.com/hs/cta/wi/redirect?encryptedPayload=AVxigLLVJbHjGx5y7D%2BguwSpC3pPT0XsDauRm6sXtcNW8fXkC2Z5M%2BVGPj7ktiFBPpwwTMkZfHM0aknqbKsf0Ena4DdWQ9M%2Bx8dkjrH9DmyZ0vcQCO9wvYJnVo93GVVs77W3H0JDTWMLNWqxBumqj0C5ehiHpfRZTeJQtwAoRSM9RDTtHPQQZnyHZEYKXOty8bMmvpGiA8im2Zqzqc%2FTfdwm3%2Bxzg3Jz9Vat8UXn8abuxbVupmulNYgEqvg98Wud3Z0xFvbCcW4Rjvi%2F1JGP6oUW%2FBs%3D&webInteractiveContentId=212093732905&portalId=3447555&hsLang=en) [](https://blog.minitab.com/hs/cta/wi/redirect?encryptedPayload=AVxigLINxy4CkEIk9KrXE4I8ZbFik5Ovc%2FEf7dINsljwCot%2B1CX7K1wTYPiI3fyhjzB%2FxZwh3Gk8VejZX0HV%2BKRxzw7Xo3ZsiDgqMNRhnahp68LcRiqRwIv11D%2Bb8qQwPXBNIIyxxZ%2BuP1tNcuGNj5fE6rFbV4T8vmTb4Sj%2BVUm%2Bvawu%2F6Atj0EPdGFvqRtCdOCIdGHKxJMNIsGd1Bu9Qi8j6srMdVMqP2UkhH%2FVZX0nlYTwu1cs6YYVhXEOCSeCd7qaKTbYb0Hmc5a7%2B3PbjpVNnl5gDtA%3D&webInteractiveContentId=212093723017&portalId=3447555&hsLang=en)
## **Why Optimal Designs Matter in Today’s Environment**
Organizations face increasing pressure to:
- Reduce experimental cost and time
- Work with constrained materials or expensive prototypes
- Study complex systems with many variables
- Model nonlinear or higher-order relationships
- Incorporate both hard-to-change and easy-to-change factors
Traditional factorial or response surface designs work well under ideal conditions. But real-world experimentation rarely happens under ideal conditions.
Optimal designs allow practitioners to:
1. Handle Irregular Design Spaces
When certain factor combinations are infeasible, unsafe, or impossible, optimal designs can exclude those regions while maintaining statistical power.
2. Manage Mixed Factor Types
Experiments often include a combination of continuous factors, categorical factors, and constrained mixture components. Optimal design algorithms accommodate this complexity seamlessly.
3. Minimize Runs While Maximizing Information
When runs are expensive — such as in aerospace testing, pharmaceutical development, or high-value manufacturing — optimal designs ensure each run contributes maximum information toward the desired model.
4. Support Custom Modeling Goals
Different problems require different optimality criteria:
- D-optimality focuses on precise parameter estimation.
- I-optimality emphasizes prediction accuracy across the design space.
- A-optimality minimizes average parameter variance.
Optimal designs allow teams to choose the criterion aligned with their business objective.
## **Minitab’s Strength in Optimal Designs Just Got Stronger**
Minitab has long supported optimal designs as part of its comprehensive DOE platform, giving users:
- D-optimal designs
- Custom design generation
- Support for complex models
- Seamless integration with model fitting and analysis tools
- Clear visualization and interpretation of results
The addition of Minitab DOE by Effex’s optimal design technology further enhances Minitab’s portfolio in meaningful ways.
1. Advanced Algorithmic Depth
Minitab DOE by Effex brings additional optimization techniques and computational approaches that expand the range and robustness of design generation. This strengthens:
- Stability in large, complex experimental spaces
- Efficiency when dealing with many factors
- Advanced constraint handling
- Hard constraints between factors
- Custom feasible regions
- Nonlinear or irregular boundaries
- Resource-based limitations
As experiments become more multidimensional, algorithmic sophistication becomes increasingly important.
2. Greater Flexibility in Complex Constraints
Modern experiments often involve:
- Hard constraints between factors
- Custom feasible regions
- Nonlinear or irregular boundaries
- Resource-based limitations
Minitab DOE by Effex’s capabilities extend the ability to generate statistically efficient designs even under highly restrictive conditions — reducing compromises between feasibility and statistical power.
3. Enhanced Support for Advanced Modeling Needs
In advanced R&D settings, teams may require:
- Higher-order polynomial models
- Custom model specifications
- Prediction-focused optimality
- Tailored criteria based on engineering objectives
By expanding the available design generation strategies, the improved Minitab portfolio better supports these sophisticated use cases.
### Want to learn more about Minitab’s expanded offering? [](https://blog.minitab.com/hs/cta/wi/redirect?encryptedPayload=AVxigLI%2FnNFFRB3kSVi1WGcx1Er6vwB7yL1A7%2FdhxYj9SjKJKlGQnkNNLWij8FfWk49zPAtK8rpxVDTud7P0vkOAstQDX%2B5lIlfmwtnWrZF3H67C%2F15CPhJYRi6BQNhvoj2Jmqx7ta9dIEf7PXx33Eo12vUYpp%2Bg5i5P3G0FOZa3gelycBekQ0B%2BO2Pr4bma5%2BrQPAI%3D&webInteractiveContentId=207470623889&portalId=3447555&hsLang=en)
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