IoT: Enhancing Operations Through Intelligent Maintenance
Thursday, February 26, 2019 – 9 AM PT, 12 PM ET
Together, IoT technologies and connected industrial assets are capable of producing data insights that provide amazing business value. IoT-enabled intelligent equipment maintenance is one such opportunity that carries enormous benefit potential. Traditional maintenance methods are reactive – servicing equipment once it fails – or based on usage or time benchmarks. This can lead to expensive unplanned downtime, non-routine repairs, and under- or over-servicing equipment – which can inflate expenses and reduce asset longevity.
This live webinar will highlight how enabling more sophisticated maintenance strategies can help organizations realize broader operational benefits beyond just reducing maintenance costs. Our IoT and data science experts will discuss the fundamentals of creating an intelligent maintenance ecosystem and share insights on what organizations can do to enhance their own maintenance strategies – including:
- How to utilize IoT, data science, and SME input to advance from preventative to proactive maintenance
- The role of data science (including exploratory data analysis, anomaly detection, and machine learning) in different levels of maintenance maturity
- Organizational challenges, common mistakes, and best practices – including case study examples based on real-world experience
Webinar brought to you by :
Dave McCarthy, Vice President, IoT Solutions – Bsquare
Dave McCarthy is a leading authority on industrial IoT. As vice president at Bsquare Corporation, he advises Fortune 1000 customers on how to integrate device and sensor data with their enterprise systems to improve business outcomes. Dave regularly speaks at technology conferences around the globe and recently delivered the keynote presentation at Internet of Things North America. Dave earned an MBA with honors from Northeastern University
Rebecca Grollman , PhD, Data Scientist – Bsquare
At Bsquare, Rebecca works with IoT customers to build predictive models using machine learning to develop actionable insights through data science. She also researches and builds tools to accelerate the data science process.