Predictive Safety Analytics

Reducing Risk through Modeling and Machine Learning
1. Auflage 2023
ISBN: 978-1-032-42438-5
Verlag: CRC Press

Buch, Englisch, 98 Seiten, Format (B × H): 145 mm x 222 mm, Gewicht: 268 g

Reihe: Reliability, Maintenance, and Safety Engineering

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 employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can “meet you where you are” by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations.

This book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest to them.

Zielgruppe

Professional Reference

Autoren/Hrsg.

Stevens, Robert

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Technische Wissenschaften Technik Allgemein Industrial Engineering
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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.

Über Autor(innen)

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.