Multimodal data fusion for systems improvement: A review

In recent years, information available from multiple data modalities has become increasingly common for industrial engineering and operations research applications. There have been a number of research works combining these data in unsupervised, supervised, and semi-supervised fashions that have addressed various issues of combining heterogeneous data, as well as several existing open challenges that remain to be addressed. In this review paper, we provide an overview of some methods for the fusion of multimodal data. We provide detailed real-world examples in manufacturing and medicine, introduce early, late, and intermediate fusion, as well as discuss several approaches under decomposition-based and neural network fusion paradigms. We summarize the capabilities and limitations of these methods and conclude the review article by discussing the existing challenges and potential research opportunities.