🌳Decision Tree for Choosing the Right DOE Method

1. What is your primary goal?

• Screening many factors quickly → Go to Step 2
• Modeling curvature or optimizing a continuous response → Use RSM (CCD or BBD)
• Improving robustness against noise factors → Use Taguchi Method (OA + S/N ratios)
• Exploring mixture proportions → Use Mixture Designs (Simplex‑Lattice, Simplex‑Centroid)
• Working in a constrained or irregular design space → Use Optimal Designs (D‑optimal, A‑optimal, Custom)
• High‑dimensional simulation or ML‑based modeling → Use Space‑Filling Designs (LHS, Sobol, Quasi‑Random)

2. For screening: How many factors do you have?

• ≤ 4 factors → Full Factorial or Fractional Factorial
• 5–12 factors → Resolution IV/V Fractional Factorial or → Taguchi Orthogonal Arrays (if robustness is also a goal)
• > 12 factors → Definitive Screening Designs (DSD) or → Plackett–Burman Designs

3. Do you expect strong interactions or curvature?

• Yes, interactions + curvature matter → RSM (CCD or BBD)
• Only interactions matter, not curvature → Full or Fractional Factorial
• No, mostly main effects → Taguchi OA or Plackett–Burman

4. Are experiments expensive or sequential?

• Yes, each run is costly → Sequential DOE
  – Start with screening (FF/FFR/Taguchi)
  – Move to RSM near optimum
  – Refine with Adaptive DOE or Bayesian Optimization
• No, runs are cheap → Classical DOE (FF, FFR, CCD, BBD)

5. Are there hard constraints on factor combinations?

• Yes (e.g., forbidden regions, safety limits) → Optimal Designs (D‑optimal, I‑optimal)
• No → Use classical designs (FF, FFR, CCD, BBD)

6. Is the system noisy or sensitive to environmental variation?

• Yes, robustness is critical → Taguchi Method (S/N ratios + OA) or → Dual Response Surface Method (mean + variance models)
• No → Use RSM or Factorial DOE

📌 Summary Table (Quick Reference)

Goal	Best DOE Method
Screening	Fractional Factorial, Taguchi OA, Plackett–Burman
Optimization	RSM (CCD, BBD)
Robustness	Taguchi (S/N), Dual RSM
Mixture formulation	Simplex‑Lattice, Simplex‑Centroid
Constrained design space	D‑Optimal / Custom Designs
High‑dimensional ML modeling	LHS, Sobol, Space‑Filling
Expensive experiments	Sequential DOE, Bayesian Optimization

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