Despite the growing interest in IIoT and AI, many companies are not realizing solutions that can provide the promised value for a variety of reasons. Some less than successful solutions do not incorporate domain knowledge, or have poor data quality, or have an incomplete business case, or a lack of robustness in the machine learning models and approaches.
This presentation will address those challenges and lessons learned as well as offer a practical approach to embarking on the digital transformation journey using tools like IIoT, AI and ML - all from a practical perspective through a case study from an automotive tier 1 supplier. The presentation will discuss how manufacturers are leveraging AI-based technologies to look at Asset Health and Diagnostics, as well as predict the remaining useful life of equipment in real time, achieving significant productivity improvements. The case studies will shed the light on how manufacturers can transform from a “fail and fix” to a “predict and prevent” maintenance approach; keeping in mind cost, time-to-deploy, technology architecture, and scalability.
- Comprehend the many challenges and lessons learned of deploying Artificial Intelligence AI technologies, and the countermeasures to overcome those challenges.
- Learn a systematic methodology for deploying A.I. including business and technical problem definition, and the end-to-end analytics approach from data collection to the delivery of the health and process information.
- Capture insights on real world manufacturing examples with ROI including stamping, casting, CNC machining, industrial robotics, among other solution templates for AI-based predictive maintenance and predictive quality.