There have been numerous studies showing efficacy of strategies in process optimization. The common comparisons made are usually between the ‘one factor at a time’ or OFAT experiments and a ‘design of experiments’  approach. When faced with an unfamiliar, high-dimensional process space (e.g. >10 factors), researchers often resort to the OFAT methods as they are easy to interpret.   Generally, it would be cost-prohibitive and logistically challenging to run multiple experiments geared towards the same objective just to evaluate which strategy outperforms others. To circumvent these issues, we used a Polymerase Chain Reaction (PCR) simulator with 12 unfamiliar continuous and categorical factors to explore these questions.

Our team comes from decades of experience in process optimization in the electronic materials industry (former employees of Apple and others). We intentionally sought and selected a simulator from a research area completely unknown to us that has the ability to simulate a large number of factors and their complex interactions on many responses. To automate experimentation, we used a python web automation script. By using a simulator and our script, we can run through many experiments while mimicking real-life constraints and experimental budgets as seen in our own professional careers. While adhering to run budget rules, we compare the efficiency and accuracy of four strategies; two OFAT type strategies as commonly used in the industry, and two strategies from the DOE and advanced DOE genre. JMP is used for all experimental analyses and modeling and an objective attempt is made to compare the strategies.