Industrial applications of statistics have a long history, but the adoption of Bayesian methods in industry has accelerated in recent decades. Bayesian approaches offer natural solutions to many industrial problems: combining expert judgment with limited data in reliability assessment, updating quality estimates as production data accumulate, and making optimal decisions under uncertainty in supply chain and operations management. The ISBA Section on Industrial Statistics provides a community for researchers and practitioners working in these areas.
Applications in Industry
Bayesian methods have found applications across a wide range of industrial domains. In reliability engineering, Bayesian models combine test data with engineering knowledge to estimate failure rates and predict product lifetimes. In quality control, Bayesian approaches to process monitoring and acceptance sampling allow for the incorporation of prior information and sequential updating as new data arrive. Design of experiments benefits from Bayesian optimal design methods that maximize the information gained from limited experimental resources.
Reliability analysis is one of the oldest and most successful industrial applications of Bayesian statistics. When testing is expensive and failures are rare, Bayesian methods allow engineers to combine prior information—from similar products, physical models, or expert judgment—with limited test data to make informed decisions about product reliability.
Section Activities
The section organizes sessions at ISBA World Meetings and other conferences, bringing together academic researchers and industry practitioners. Workshops and short courses focus on practical Bayesian methods for industrial problems, including topics such as Bayesian process optimization, predictive maintenance, and Bayesian approaches to computer experiments and emulation.
The section also serves as a bridge between the Bayesian academic community and industry, helping to translate methodological advances into practical tools and promoting the adoption of Bayesian methods in industrial settings.
Emerging Areas
As industry increasingly embraces data-driven decision-making, new applications of Bayesian methods continue to emerge. Digital twins—virtual representations of physical systems—use Bayesian calibration and updating to maintain fidelity with real-world systems. Bayesian optimization is widely used for tuning complex manufacturing processes and machine learning hyperparameters. Predictive maintenance employs Bayesian state-space models to forecast equipment failures before they occur.
"In industry, we rarely have the luxury of unlimited data. Bayesian methods let us make the most of what we have, combining data with engineering knowledge to make better decisions."— Industrial statistician
Community and Collaboration
The section's membership includes statisticians working in manufacturing, pharmaceuticals, energy, defense, and technology companies, as well as academic researchers focused on industrial applications. This diverse community ensures that the section's activities are grounded in real industrial needs while benefiting from the latest methodological advances.