Speaker: Judy Jin, University of Michigan, Department of Industrial and Operations Engineering

Title: Data Fusion for Quality Improvements in Complex Systems

Abstract:
Our current data rich environment provides unprecedented opportunities and research challenges for improving complex system operations both in manufacturing and in service industries. The development of a novel data fusion methodology is currently very relevant, and will provide a long lasting future impact. Efforts in data fusion research have significantly advanced the development of methodology in areas like rapid change detection, root cause diagnostics, and system control. Data fusion, through integrating engineering domain knowledge with data analysis techniques from advanced statistics, signal processing, decision making, and system control, represents one of the frontiers in quality improvement research for complex systems.

This presentation will provide an overview of ongoing data fusion research for manufacturing system improvement and its extension to service industry applications. The basic concepts in data fusion research will be discussed, with emphasis placed on promoting the integration of disparate methodologies into a cohesive entity to enable effective decision-making for system operations. Examples of methodological developments and their applications will be discussed in order to demonstrate the characteristics of data fusion research and to emphasize the need for multidisciplinary system integration efforts. Detailed discussions will focus on an example of a model-free multiscale process monitoring method for autocorrelated processes.

Contact person: Dave Goldsman (or Jennifer Harris)