1st Edition

Predictive Safety Analytics Reducing Risk through Modeling and Machine Learning

By Robert Stevens Copyright 2024
98 Pages 31 B/W Illustrations
by CRC Press

98 Pages 31 B/W Illustrations
by CRC Press

98 Pages 31 B/W Illustrations
by CRC Press

Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by... Read more
1. Safety in Numbers: A Data-Driven Approach. 2. Analytics Defined. 3. The Safety Data Repository. 4. Use Cases. 5. Where to Go from Here.

Biography

Rob is part of the leadership team at First Analytics, a boutique analytical consulting firm. First Analytics designs and implements predictive analytics and machine learning solutions. The firm services multiple industries with many applications. With an enabling engagement model, the firm teams up with its clients to build their in-house capabilities and systems. In his role as Vice President at First Analytics, Rob helps companies develop and execute programs to cultivate their analytics competency. He brings experience to bear stemming from more than thirty years as an analytics professional, starting as an econometrician. His career has consisted of consulting, product development, client service, technical, and sales roles within software, consulting, and market research firms. Rob has participated in or led safety analytics implementations in the railroad, utility, oil and gas, and manufacturing industries. He has spoken on predictive safety analytics in venues such as National Safety Council congresses, an OSHA safety conference, human and organizational learning conferences, manufacturing forums, private analytics consortiums, and conferences and webinars sponsored by software companies.

"A key strength of Predictive Safety Analytics is its rigorous framing of safety as a data-intensive, model-driven decision problem, with strong emphasis on data architecture, feature engineering, and interpretability, aligning well with applied statistical practice and organizational decision contexts."

-Johanes Robert Kera
Fly Bali Indoaviasi,
Department of Civil and Environment,
Master in Transport System Engineering,
Gadjah Mada University, Yogyakarta, Indonesia
[email protected]


-Dewanti
Department of Civil and Environment,
Gadjah Mada University, Yogyakarta, Indonesia
Center for Transportation and Logistic Studies,
Gadjah Mada University, Yogyakarta, Indonesia