Design of Experiments and machine learning for product innovation: A systematic literature review

The recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques. This article aims to provide a systematic overview of the literature on the joint application of DOE and ML in product innovation (PI) settings. To this end, a systematic literature review (SLR) of two major scientific databases is conducted, retrieving 388 papers, of which 86 are selected for careful analysis. The results of this review delineate the state of the art and identify the main trends in terms of experimental designs and ML algorithms selected for joint application on PI. The gaps, open problems, and research opportunities are identified, and directions for future research are provided.

Automated summary

The recent increase in digitalization of industrial systems has led to a boost in data availability, facilitating the adoption of machine learning methodologies. In contexts where data are scarce or expensive to collect, Design of Experiments can provide an informative dataset for analysis using ML techniques.