Statistically designed experiments, otherwise known as DOE, is an invaluable tool for anyone looking to improve research, development and manufacturing processes and products in almost any industry. They can allow you to gain new and vital understanding that is just not possible using passively collected data, reducing costs and time to market and increasing quality.
But commonly used DOE methods can have limitations when applying them to many practical situations, and making use of prior data can be complicated because this data is messy and incomplete. This leads to inefficiencies, the failure to recognize the expected benefits, and the risk of low adoption of DOE itself.
You will learn how to avoid these pitfalls using the modern approaches to design and analysis.