🏗️ Module 4: DoE Design Studio

Part 1: The DoE Paradigm Shift

🎯 Learning Objectives

Understanding OFAT Limitations: Identify why one-factor-at-a-time approaches miss critical interactions and optimal conditions

DoE Efficiency Mastery: Calculate and compare resource requirements between OFAT and factorial designs

Interaction Recognition: Visualize how factor interactions create synergistic effects in pharmaceutical formulations

Cost-Benefit Analysis: Quantify ROI improvements through systematic experimental design

Starting Your DoE Journey...

🔬

OFAT Simulation Lab

10 min

One-Factor-At-a-Time (OFAT)

OFAT Path: Sequential exploration missing the true optimum ⭐

Pharmaceutical Example: Tablet Formulation

Step 1: Fix compression force at 15 kN, vary binder concentration (2-6%)

Step 2: Fix binder at "optimal" 4%, vary compression force (10-20 kN)

Step 3: Fix both, vary lubricant level (0.5-1.5%)

Result: Miss interaction between binder and compression force!

Design of Experiments (DoE)

DoE Approach: Systematic exploration finding the true optimum ⭐

Pharmaceutical Example: Factorial Design

Step 1: Define factors: Binder (2-6%), Force (10-20 kN), Lubricant (0.5-1.5%)

Step 2: Create 2³ factorial design (8 experiments)

Step 3: Estimate main effects AND interactions simultaneously

Result: Discover binder-force interaction improves dissolution!

📊 Resource Efficiency Calculator

Let's think step-by-step about experimental efficiency:

Number of Factors (k):

Levels per Factor (L):

Fraction Level (p):

OFAT Approach: -

Full Factorial: -

Fractional Factorial: -

Efficiency Gain: -

🎯

DoE Advantage Showcase

15 min

🔍 Interaction Detection Engine

Understanding Interactions: Let's think step-by-step...

Select an interaction type to see the effect

Real Pharmaceutical Interaction Example

Scenario: Disintegrant effectiveness vs. tablet hardness

Low Hardness: Both disintegrants A and B work equally well

High Hardness: Disintegrant A works much better than B

Interaction: Choice of disintegrant depends on hardness level!

OFAT Miss: Would conclude "no difference" between disintegrants

📈 Statistical Power Comparison

Step-by-step power analysis:

Effect Size (Cohen's d):

Significance Level (α):

Sample Size per Group:

OFAT Power (single comparison): -

DoE Power (multiple factors): -

Information Advantage: -

💰

Cost-Benefit Analysis

5 min

💡 Return on Investment Calculator

Let's calculate the financial impact step-by-step:

Cost per Experiment ($):

Value per Batch ($):

Defect Reduction (%):

Batches per Year:

Annual Cost Savings

Calculate to see results

Enter your values and click calculate to see the financial impact

📊 Information Per Run Metric

OFAT Information

✅ Main effects only
❌ No interaction effects
❌ No curvature information
❌ Sequential, not simultaneous
❌ Limited statistical power

Information Score: 3/10

DoE Information

✅ Main effects
✅ Two-factor interactions
✅ Curvature (with center points)
✅ Simultaneous estimation
✅ High statistical power
✅ Predictive models
✅ Design space mapping

Information Score: 10/10

🧠 Knowledge Check: Test Your Understanding

You have 5 factors to optimize in a tablet formulation. Using OFAT with 3 levels each, how many experiments do you need minimum?

A) 15 experiments (5 factors × 3 levels)

B) 8 experiments (2³ factorial design)

C) 11 experiments using formula: (L-1) × k + 1 = (3-1) × 5 + 1

D) 243 experiments (3⁵ full factorial)

📋

Key Takeaways & Next Steps

🔍 What You Learned

OFAT misses critical factor interactions

DoE provides maximum information per experiment

Statistical power increases dramatically with DoE

ROI improvements can be substantial

📈 Coming Up Next

Screening designs for factor identification

Full factorial design construction

Response surface methodology

Real pharmaceutical optimization

💡 Pro Tip for Pharmaceutical Scientists:

Always consider interactions in formulation development! The "best" level of one excipient often depends on the levels of others. DoE helps you discover these hidden relationships that OFAT approaches systematically miss.