Machine learning and design of experiments with an application to product innovation in the chemical industry

Abstract

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.

Keywords:

• Experimental design
• artificial neural networks
• random forests
• R&D
• product development

Acknowledgments

The paper has been developed by Rosa Arboretti, Riccardo Ceccato, Luca Pegoraro and Luigi Salmaso. Chris Housmekerides, Luca Spadoni, Elisabetta Pierangelo, Sara Quaggia, Catherine Tveit and Sebastiano Vianello contributed in providing the initial idea for the case study and the experimental data.

Disclosure statement

No potential conflict of interest was reported by the author(s).