Publication: Bridging Data Science and Process Control

Principal Consultant, Herman Tan, has been featured in the latest research document titled "Data Science Approach to Process Control," co-authored by a dedicated team from the National University of Singapore (NUS). The research delves into the intersection of data science and manufacturing, offering fresh insights and practical solutions to enhance process control in complex production environments.

Abstract Highlights: The research tackles the limitations of traditional Statistical Process Control (SPC) techniques in modern manufacturing. As production processes grow more intricate, existing SPC methods struggle with nonstationary and autocorrelated data. The authors propose a novel aggregation technique to mitigate noise and address endogeneity challenges, using a blend of machine learning tools and empirical validation.

Key outcomes include:

• 34.5% Cost Reduction: Achieved by a manufacturing company using our data science-based solution.

• Predict, Explain, and Act (PEA) Framework: A new approach to seamlessly integrate data science into process control, enabling proactive issue resolution.

For those interested in cutting-edge solutions at the crossroads of data science and manufacturing, this paper offers valuable perspectives and actionable strategies.

Read the full paper here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4857275