๐Ÿ‡บ๐Ÿ‡ธTransforming Paper Product Quality and Machine Performance with Machine Learning & Bayesian Networks

Steven Frazier, Georgia Pacific

To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.

Abstract

This presentation explores the application of machine learning and Bayesian networks to enhance tissue softness, a crucial factor in the paper product industry. It begins with the presenter's extensive background in engineering and analytics, setting the stage for a deep dive into utilizing Bayesian networks to improve tissue manufacturing processes.

The focus is on refining the balance between paper strength and softness, crucial for producing high-quality tissue. The presentation covers the technicalities of tissue production, from fiber refining to the mechanics of papermaking, illustrating the complex interplay of factors affecting product quality.

A significant portion is dedicated to the evolution of model development, from initial challenges to advanced iterations that accurately predict tissue softness. Techniques like Jackknife and K-Fold cross-validation are discussed for model evaluation, highlighting the learning curve and adjustments made to enhance model performance.

Operational insights form the core of the latter part, where data analysis reveals optimal manufacturing conditions. The presentation touches on the importance of data integrity, model adaptability, and the role of human operators in implementing AI-driven recommendations.

Concluding, the presentation reflects on the project's broader impacts, emphasizing continuous improvement, user readiness, and aligning project goals with customer expectations. This summary encapsulates the journey and lessons learned in applying advanced analytics to improve tissue softness, underscoring the potential of machine learning in industrial applications.

About the Presenter

With over 30 years of operations experience and 3 U.S. patents, Steven Frazier is an expert in creating high-performance solutions for complex business challenges. His multi-disciplinary approach has been pivotal in enhancing processes for leading Fortune 500 companies, including Coca-Cola, Procter & Gamble, and Georgia Pacific.

Steven specializes in Bayesian Networks and machine learning, providing critical insights that guide manufacturing efficiency and strategic value creation. His innovative modeling techniques have informed smarter procurement strategies, yielding substantial cost savings and process enhancements.

During his recent role at OnPoint (Koch Industries), Steven's models for tissue softness and strength revealed significant opportunities for value creation, contributing to both product and process improvements.

As he prepares to share his expertise at the Bayesialab conference on April 11, 2024, Steven's pragmatic and transformative approach is expected to resonate with a wide audience. With a solid educational foundation in Mechanical Engineering from Washington University and advanced certification in Lean Six Sigma from Villanova University, he's geared to propel organizations towards operational excellence and analytical innovation. A presentation you donโ€™t want to miss.

Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:

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