DOE Software for Quality by Design (QbD) and Process Optimization

Ensure Product Robustness with Fewer Trials

When you need to ensure product robustness, define an optimal process or formula, or determine the best way to make changes or substitutions, you need a data analytics method and software tool that gives you confidence and simplifies your work. MODDE® Design of Experiments (DOE) Software is that tool.  

Design of Experiments (DOE) is a data analytics method that helps you plan, conduct, analyze and interpret controlled tests to determine which factors exert influence over your product quality, stability or other key process attributes.  Rather than experimenting with one parameter at a time, DOE speeds up the process and helps you identify important interactions by manipulating multiple factors at the same time. 

DOE supports a quality by design (QbD) approach to product development that is favored by regulatory agencies. By building experiment certainty and reproducibility into your process, you can be confident in the robustness of your formulation and quality of your final product.

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Design of Experiments Applications Across Industries

DOE in Pharma/Biopharma

DOE supports a Quality by Design (QbD) approach that helps demonstrate product robustness and determine acceptable ranges for formula composition...

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DOE in Food & Beverage

DOE supports a Quality by Design (QbD) approach that helps demonstrate product robustness and determine acceptable ranges for formula composition...

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DOE in Process Industries

DOE helps develop processes to reduce waste, improve environmental footprint and identify the best substitutions for ingredients affected by...

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MODDE® Design of Experiments Software Speeds Time to Market

Reducing the number of experiments needed to determine the right process or formula means a faster time to market.  MODDE DOE software helps you verify the statistical accuracy of your results, cull the data, and ensure your models are viable.  

With MODDE you can: 

  • Quickly determine the outer ranges of your experimental requirements 

  • Create a more accurate design space (with wizards and design templates) 

  • Identify the robust optimum for quality by design 

  • Set up subset designs and complimentary designs 

  • Ensure your models are viable 

  • Investigate all possible combinations (full factorial design)  

  • Clarify which portion of combinations (fractional factorial) will suffice  

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MODDE-Q Embedded Design of Experiments

Find out how you can embed Design of Experiments into your solution and reduce the risk of human error by automating the data transfer. 

MODDE-Q saves you time in development, offering all the capability of MODDE Pro. The easy-to-use Software Development Kit (SDK) speeds up development of your own AI or ML solution and provides a user-friendly interface.

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DOE EduPack 

EduPack is an educational package for academia with all you need to learn data analytics. With hands-on practical skills to solve problems and explore data, you can become the master of your data.

In Design of Experiments (DOE) you can build knowledge by setting up well planned experiments and get detailed information of how the process works. This will save you time and improve the quality of your results.

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DOE Supports Quality by Design (QbD)

DOE is an essential tool to ensure products and processes satisfy Quality by Design requirements imposed by regulatory agencies.  Using a QbD approach to develop your testing process can help you reduce waste, meet compliance criteria and get to market faster.  

DOE helps you create a reliable QbD process for assessing formula robustness, determining critical quality attributes and predicting shelf life by using a few months of historical data.

Why Use a Quality By Design Approach? 

Using a Quality by Design (QbD) approach to develop the testing process and to choose the critical quality attributes for a pharmaceutical product can help to:  

  • Ensure products meet defined critical quality attributes 

  • Meet regulatory compliance criteria 

  • Predict formula robustness 

  • Reduce waste in production 

  • Get to market faster 

Using DOE to Optimize Processes

When it comes to creating an optimal manufacturing process that limits variation and conserves energy or resources, or a developing a new formula that is most likely to meet customer expectations, design of experiments (DOE) is an indispensable tool. 


DOE helps you to: 

  • Minimize the number of experiments you have to do to find the ideal formula or recipe 

  • Create a robust process (one that holds up to changes in environment, humidity, ingredient variation, etc.) 

  • Adapt a recipe for changes in ingredients or packaging needs (availability, eco-compliance, regulations, consumer trends, etc.) 

What’s the Alternative? DOE vs. Alternative Approaches 

The typical alternative to using DOE to determine which processes or formulas are contenders is the classic “COST” approach.  The COST approach, which stands for “Change One Separate factor at a Time” is a logical way to approach an experimentation, but it requires a lot of time and effort, and may lead to the wrong conclusions.  

COST doesn’t let you consider the interaction between investigated factors and may give you different results depending on your starting point. DOE helps shorten that process and uses advanced data analytics to help inform your decisions about which factors need to be tested.  

Another weakness of the COST approach is that it can’t define the number of runs that will be needed to find the maximum. You can only test two factors at a time and can’t define how many runs you’ll need to be sure you’ve covered all the options. 

Why using DOE is better than using a COST approach: 

  1. DOE suggests the correct number of runs needed (often fewer than used by the COST approach) 
  2. DOE provides an unbiased model for the direction to follow that is independent of start settings 
  3. Many factors can be used (not just two) and their interactions taken into account 

Using DOE to Predict Formula Robustness

Being able to demonstrate product robustness and deliver the intended quality of the product within allowable ranges for the claimed shelf-life period is critical for pharmaceutical manufacturers. Both international and country specific regulatory agencies, such as the FDA, pay close attention to shelf-life claims. 

Predicting formulation robustness requires a careful design of experiments that holds up under statistical analysis. Using DOE for formulation robustness studies can help you select a commercial formulation that is sufficiently robust within the acceptable ranges around the label claim to meet the shelf life stability requirements. 

Download a case study about predicting formula robustness featuring an example from Hoffmann-Roche. 

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Steps to Predict Formula Robustness

Step 1: Choose the Right Measurement Factors  

Ensure that the factors selected to study can be used to predict an acceptable formulation parameter range where all the values for the assessed quality attributes will be inside the specified limits. 

Step 2: Design a Statistically Valid Study 

Consider how the factors being investigated fit into a full factorial design. For pharma companies, for example, robustness studies must be able to prove that specific critical quality attributes stay within the acceptable ranges for the entire shelf-life period. In addition: 

  • The study must result in a regression model that is statistically significant 

  • The study must provide output parameters (quality attributes) that are within predefined limits 

Step 3. Analyze the Data Using Multiple Linear Regression 

One important way to produce a valid testing model is to use a tool that makes Design of Experiments easier. For example, MODDE Design of Experiments Software, can help you set up multivariate formulation robustness studies that demonstrate the acceptable ranges of quality for a target composition, define the allowable edges of the composition range, and predict the stability requirements needed to reach the end of shelf life. 

Learn More About Using Design of Experiments

Past Webinars & Videos 

Find out more about using MODDE for Design of Experiments in one of our recorded webinars. 

Design of Experiments (DOE) for the Beginner

D-Optimal Design - What Is It and When to Use It

Lean and Clean Using One Click Analysis


Finding the Optimal Design Space

Scientific investigations involve changing a number of controlled variables to direct the response in question towards a desired level. Design of Experiments (DOE) is a rational and cost-effective approach to practical experimentation that allows the effect of variables to be assessed using only the minimum of resources. DOE is the backbone for efficient QbD implementation strategies. The final specifications for a region where all specifications are fulfilled to a defined risk level is called Design Space. Determining the optimal Design Space is a key advantage of using MODDE DOE Software. 

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EduPack Design of Experiments (DOE)

The education package will take students on a journey to discover, how DOE and QbD can be applied to solve problems and bring new insights to their field of application. With EduPack students get the best education in DOE for generating good results.

DOE EduPack is designed to give students hands-on skills to solve problems and learn:

  • How to create efficient experimental designs to match the objectives
  • How to analyze data based on sound statistical principals to evaluate results of the experiments
  • How to interpret results by using graphical and statistical tools
  • How to convert modeling results into concrete action with MODDE® optimizer & verifying experiments
  • How to define a design space and find robust setpoints  

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