A reduction in unplanned downtime and wasted scrap can yield millions of dollars in savings for manufacturers. This compelling value proposition has resulted in a significant investment and interest in predictive maintenance and predictive quality solutions based on artificial intelligence (AI). Manufacturing firms are embracing digital transformations and smart solutions by leveraging AI and data metrics to achieve greater efficiency and productivity on the shop floor.

Despite this interest, there are a variety of reasons companies are not fully adopting solutions that can provide this ROI and value. Some solutions on the market today are readily available, but they lack an accompanying team of experts with domain knowledge to provide consultation. Other companies experience roadblocks due to poor data quality or find that their solution is not robust enough in its machine learning models and algorithms.

Sponsored by Wago, Oracle, and Epicor, Predictronics CTO Dr. David Siegel will co-host a webinar, entitled Case Studies in AI-Based Predictive Maintenance and Quality, on April 28, 2020 at 2 p.m. EST with our Predictronics sales partner and CEO of IoTco, Dr. Mohamed Abuali.

Topics to be presented and discussed in the webinar include:

• Challenges of adopting predictive solutions
• Proven processes for realizing the impact of predictive solutions

• Conducting a workshop to define the business and technical problems
• Utilizing a state-of-the-art, end-to-end analysis platform, from data collection to health and process data visualization
• Leveraging expertise to determine how the solution can be maintained and improved over time

• Solutions in manufacturing with the greatest ROI

Dr. Siegel will also present compelling real-world case studies in industrial applications, such as stamping, casting, CNC machining, and industrial robotics, to demonstrate the value of rapidly deploying AI in manufacturing.

Join Dr. Siegel and learn how you can develop a successful AI-based solution to reduce unplanned downtime through predictive maintenance and improve product quality in production.