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WHAT YOU WILL LEARN In continuous improvement (CI) projects, cause-and-effect (CE) diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. In this webinar, a framework that combines cause-and-effect diagrams with Bayesian Belief Networks (BBNs) to estimate causal relationships in instances where formal data collection/analysis activities are too costly or impractical is presented. This framework enables continuous improvement practitioners to leverage qualitative data and expertise to conduct in-depth statistical analysis in the event that data collection activities cannot be fully executed. Furthermore, this allows continuous improvement practitioners to identify critical root causes of a given problem under investigation before generating solutions. ABOUT PROFESSOR MARK RODGERS Professor Rodgers teaches Supply Chain Management at the Rutgers Business School, where he previously developed and taught courses on Demand Planning and Fulfillment, Operations Management, and Operations Research. Prior to this position, he held several senior analytics and strategy positions at Bristol-Myers Squibb, The Port Authority of NY & NJ, ZS Associates, and Verizon. He holds a Six Sigma Black Belt certification, and is an active member of the American Society for Quality (ASQ), the Production and Operations Management Society (POMS), and the Institute for Operations Research and Management Sciences (INFORMS). He holds a Doctorate in Industrial & Systems Engineering from Rutgers University, Masters of Science degrees in Statistics and Industrial & Systems Engineering from Rutgers University, a Masters of Engineering degree in Pharmaceutical Manufacturing Practices from Stevens Institute of Technology, and a Bachelor of Science in Ceramics and Materials Science Engineering from Rutgers University.