🎯 Learning Objectives

🔬

Compare Experimental Approaches

Understand the limitations of One-Factor-At-a-Time (OFAT) versus the efficiency of Design of Experiments (DoE) methodologies

📊

Design Systematic Experiments

Apply factorial designs, screening methods, and response surface methodology to formulation optimization

🎯

Optimize Pharmaceutical Formulations

Use statistical methods to find optimal formulation conditions and understand factor interactions

⚡

Implement Quality by Design

Integrate DoE principles with QbD frameworks for robust pharmaceutical development

🚀 Experience DoE in Action

Before diving into the theory, let's see the power of Design of Experiments

❌ Traditional OFAT Approach

40% Information Efficiency
Experiments: 16
Interactions: None Detected
Optimum: Local Only
VS

✅ DoE Approach

95% Information Efficiency
Experiments: 8
Interactions: All Captured
Optimum: Global Found

💡 Key Insight: Resource Efficiency

DoE uses 50% fewer experiments while providing 137% more information about the system behavior

🔍 Key Insight: Interaction Detection

Only DoE can systematically detect how factors interact with each other - crucial for formulation optimization

📚 Module Structure

Master Design of Experiments through 8 interactive parts plus a comprehensive capstone project

1
OFAT vs DoE Comparison Lab
30 min
Experience the dramatic difference between traditional one-factor-at-a-time experiments and systematic design of experiments
🔄 Interactive Simulation 📊 Side-by-Side Comparison ⚡ Efficiency Calculator
Pharmaceutical Example: Optimizing tablet dissolution using compression force, lubricant level, and disintegrant concentration
2
Screening Design Workshop
45 min
Learn to efficiently screen many factors to identify the most important ones using Plackett-Burman and fractional factorial designs
🎯 Plackett-Burman Designer 📈 Fractional Factorial Builder 🔍 Effect Significance Tester
Pharmaceutical Example: Screening 11 excipients to identify the 3 most critical for tablet hardness
3
Full Factorial Design Center
45 min
Master 2^k and 3^k factorial designs to understand main effects, interactions, and quadratic effects in pharmaceutical systems
🏗️ Visual Design Builder 📊 Interaction Plot Generator 🔢 ANOVA Calculator
Pharmaceutical Example: Full 2³ design for optimizing coating weight, temperature, and speed effects on film uniformity
4
Response Surface Methodology
45 min
Use Central Composite and Box-Behnken designs to model quadratic relationships and find optimal operating conditions
🌄 3D Surface Visualizer 📈 Contour Plot Generator 🎯 Optimization Engine
Pharmaceutical Example: RSM optimization of sustained-release matrix tablets using polymer type and concentration
5
Mixture Design Laboratory
30 min
Handle constrained optimization problems where factors must sum to 100% using simplex designs and mixture models
📐 Simplex Designer 🔗 Constraint Handler 📊 Ternary Plot Creator
Pharmaceutical Example: Optimizing blend ratios of three polymers for controlled drug release profiles
6
Pharmaceutical Optimization Hub
60 min
Apply DoE principles to real pharmaceutical challenges including multi-response optimization and robust parameter design
🎯 Multi-Response Optimizer 💪 Robustness Analyzer 📋 Case Study Library
Pharmaceutical Example: Simultaneously optimizing dissolution rate, tablet hardness, and friability using desirability functions
7
Quality by Design Integration
45 min
Integrate DoE with QbD principles to define design space, control strategy, and ensure regulatory compliance
🏗️ Design Space Builder ⚡ Control Strategy Designer 📋 Regulatory Framework
Pharmaceutical Example: Establishing proven acceptable ranges (PAR) for critical process parameters using DoE data
8
Statistical Analysis Workshop
30 min
Master ANOVA techniques, model diagnostics, and validation methods essential for proper DoE analysis
📊 ANOVA Engine 🔍 Residual Analyzer ✓ Model Validator
Pharmaceutical Example: Complete statistical analysis of a coating optimization study with interaction effects
🎯
Capstone Project
90 min
Complete a comprehensive formulation optimization project that integrates all DoE concepts from screening through final optimization
🔬 Complete Case Study 📊 Full Analysis Pipeline 📋 Professional Report
Challenge: Develop and optimize a new immediate-release tablet formulation using systematic DoE approach

🎯 Your Learning Journey

Follow this structured path to master Design of Experiments

🚪

Foundation Building

Start with understanding why traditional OFAT methods are inefficient and how DoE provides a systematic alternative

Think step-by-step: We begin here because you need to see the dramatic efficiency gains before learning the technical details
→
🔍

Screening Phase

Learn to identify important factors from many possibilities using efficient screening designs

Think step-by-step: In real pharmaceutical work, you often start with 10+ potential factors - screening helps focus your efforts
→
🏗️

Detailed Investigation

Use factorial designs to understand main effects and interactions between the important factors

Think step-by-step: Once you know which factors matter, factorial designs reveal how they work together
→
🎯

Optimization

Apply response surface methods to find the optimal conditions for your formulation

Think step-by-step: RSM builds quadratic models to locate the true optimum, not just the best experimental point
→
⚡

Real-World Application

Integrate DoE with Quality by Design principles for regulatory compliance and robust development

Think step-by-step: QbD frameworks require scientific understanding that only DoE can provide systematically

🚀 Ready to Begin?

Start your journey into systematic experimental design

🎯 Recommended Path

Most Popular

Follow the complete sequential path for comprehensive understanding

⚡ Quick Preview

Get a taste of DoE power with our interactive demo

📚 Review Mode

Jump to specific topics if you have prior DoE experience

📋 Prerequisites Check

✅ Module 3: Hypothesis Testing (Completed)
📖 Basic understanding of ANOVA principles
💊 Familiarity with pharmaceutical formulation concepts
📊 Comfort with basic statistical calculations