04-18, 14:55–15:30 (US/Eastern), Auditorium 3
In the mining industry's pursuit of zero harm, distinguishing real safety improvements from random variation is crucial yet challenging. This talk demonstrates how classical changepoint analysis and Bayesian methods provide safety teams at Asarco LLC with rigorous tools to objectively evaluate progress towards our zero-harm goal. Using near miss reporting and lost time metrics, we will show how these statistical approaches help identify meaningful trends while avoiding misleading conclusions from natural variation. While the focus is on mining, these methods are applicable to other safety-critical and data-limited scenarios. No prior experience with changepoint analysis is required.
The presentation will cover how changepoint analysis is implemented, how the insights generated are applied to improve the safety metrics, and the challenges we have faced in communicating the insights. It will be structured as follows:
• Understanding variability in the process (5 min): How random variation impacts safety metrics and challenges in measuring zero-harm.
• Changepoint analysis implementation (10 min): Introduction to changepoint analysis using the changepoint package from R and Bayesian changepoint using the RBeast package from Python.
• Communicating the insights (10 min): Challenges in communicating the insights and presenting them in a way that is actionable for the safety team and executives.
• Q&A (5-10 min): Open discussion and audience questions.
Attendees will learn:
• Why comparing absolute numbers might be misleading.
• How to implement changepoint analysis to detect significant changes in safety metrics.
• Strategies to communicate actionable findings in non-data science teams and executive level.
This session is ideal for data practitioners with a background in basic probability and statistics (e.g., understanding distributions and confidence intervals). No programming expertise is required, but references to Python libraries and code snippets will provide actionable insights for those looking to implement these techniques in their work.
No previous knowledge expected
Mauricio is the leader of the Advanced Analytics Group at Asarco LLC, a subsidiary of Grupo Mexico, where he leverages AI/ML to drive improvements in costs, productivity, and safety. With over 7 years of experience across Latin America and the US, Mauricio has a proven track record in consulting and applying advanced analytics to solve complex business challenges. Prior to joining Asarco, he led commercial strategy analytics projects at EY-Parthenon. Mauricio has a Ms. in Data Science and an MBA from the University of Virginia and holds a Bs. in Industrial Engineering from the University of Lima.