ABOUT THIS WEBINAR
Topic: Sensor & IoT Analytics
Analytics Managers, Chief Analytics Officers, Heads of Technology Innovation, Industrial Data Scientists, IT/IoT Solution Architects, Chief Technology Officers
Length: 45 minutes
The Industrial IoT (IIoT) is revolutionizing manufacturing and supply chain management through advances in human-machine interaction and decision support. Some benefits of IIoT enabled processes include the use of Machine Learning to optimize operational efficiency, improve productivity, reduce downtime, and maximize asset utilization.
Industrial systems monitor each critical part of a process, often by way of SCADA and telemetry data feeds. The more complex the operating environment, the more sensors are installed and embedded. As sensing devices become cheaper, smaller, and more capable, IIoT-enabled machines provide more data to machine learning algorithms to identify patterns that indicate a future failure, or opportunities for greater efficiency.
Sensors provide critical operational benefits such as:
- Warning users and operators of imminent threats
- Alerting about depletion of critical resources to improve supply chain efficiency
- Enabling real-time and predictive maintenance to improve up-time
But by observing complex patterns in sensor data over extended time periods, Machine Learning can help anticipate problems and opportunities earlier. For example, precisely targeted preventative maintenance reduces downtime and costs, thereby increasing asset utilization and profits. The challenge of managing the large and complex streaming data from IIoT systems can be overwhelming.
Data Scientists Mike Thurber and Will Goodrum will discuss some of the common challenges encountered when working with sensor data, and how we have helped clients in diverse industries find value in the connected data deluge to drive operational efficiency and reduce downtime.
Attendees will learn:
- Valuable application areas for AI/ML using sensor data
- Common problems encountered when applying advanced analytics and Machine Learning to sensor data
- How to implement the necessary infrastructure to collect, store, and provide sensor data for analysis
About The Hosts
Dr. Goodrum is a Data Scientist at Elder Research with a decade of experience applying numerical analysis and engineering to solve practical problems and generate value for customers. Previously, Dr. Goodrum worked in an engineering software firm, helping medium-to-large scale customers across industrial sectors develop superior products and reduce their time-to-market. Will holds a B.S. in Mechanical Engineering from the University of Virginia, and a PhD in Engineering from Cambridge University.
Mike Thurber is the Lead Data Scientist in Elder Research’s Commercial Analytics Group working across multiple teams and industries – including finance, retail, energy, and telecom – to deliver information products that drive business value. His expertise in collaboration, data exploration, predictive modeling and rigorous testing, and in remediating the selection bias common to analytic algorithms, creates confidence in the actions recommended by the analytic products of his team.