Expert Panel Recap: Operational Excellence with IIoT and Advanced Analytics
Jason Myers /
Jan 24, 2024
About the session
This panel covered the use of Internet of Things (IoT) and advanced analytics in manufacturing. The panelists discussed the importance of machine data, the challenges of collecting and analyzing this data, using advanced analytics and machine learning in industrial processes, and future trends in this area. They also examined the role of time series data in manufacturing and how it can drive operational excellence.
- Jane Arnold, Independent Member of the Board, Aperio
- Jay Clifford, Developer Advocate, InfluxData
- Sebastian Trolli, Research Manager – Global Head of Industrial Automation Program, Frost & Sullivan
- Hans Michael Krause, Director Ecosystem ctrlX World, Rexroth
After some brief introductions, the panelists discussed the importance of machine data and its evolution, followed by the challenges of collecting, storing, and analyzing machine data and how to overcome them. The conversation then moved to using advanced analytics and machine learning in industrial processes. They also considered future trends in this area, including the operationalization of AI, edge analytics, and a focus on sustainability.
Takeaway 1: Using IoT and advanced analytics can significantly improve manufacturing efficiency and worker skills.
Panelists discussed how the Internet of Things (IoT) and advanced analytics can optimize manufacturing processes, improve worker skills, and enhance safety measures. They emphasized the importance of using machine data to drive operational excellence, as well as the need for effective strategies to integrate and analyze time series data in real-time.
Jay Clifford explained that advanced analytics can help detect patterns and predict trends: “Once you cross those hurdles of data integration … the next step is how do we perform something that hits your KPIs, which is normally along the lines of anomaly detection to start, which then moves into something like predictive maintenance later.” Sebastian Trolli added that wearable devices and immersive technologies like augmented reality (AR) and virtual reality (VR) are crucial in improving worker safety and efficiency.
Hans Michael Krause emphasized the need for a scalable and effective IoT infrastructure. “We need to face the truth; what does it bring to us, actually, when is the return right … many of you maybe made IoT pilots also and never scaled them, so think right from the beginning when you design a pilot about the ROI, how you can improve the OEE, and how can you scale it actually into your organization,” he said.
Takeaway 2: Open ecosystems and data governance are vital for IoT success.
The panelists highlighted the importance of having an open ecosystem, strong data governance policies, and robust cybersecurity measures in establishing a successful IoT infrastructure. They also discussed the need for a flexible network infrastructure that can support technological advancements like 5G.
Krause emphasized the importance of an open ecosystem approach, stating, “We support open source … because then you are not depending on one company [to deliver] you a full stack from the sensor to the cloud.”
Clifford echoed these sentiments. “A time series database is … an initial application where [sic] you feed your raw machine data to, and it basically allows a number of users to scale alongside it … it gives you the best of both worlds.”
Trolli mentioned the importance of cybersecurity, data governance, and protocol standardization when implementing IoT solutions. “Don’t forget about cybersecurity … ensure you have a robust data governance program and a robust cybersecurity policy in place,” he said.
Takeaway 3: Emerging trends like generative AI, edge analytics, and sustainability are shaping the future of industrial analytics.
The panelists identified several emerging trends in the industrial analytics sector, including generative AI, edge analytics, and sustainability. These trends, they suggested, are shaping the future of manufacturing and contributing to operational excellence.
Trolli highlighted the operationalization of AI as a major trend, stating, “Moving beyond experimental AI projects to fully operationalize AI in manufacturing environments … is about being agile, efficient, and predictive.” He also touched on the growing importance of edge analytics in reducing latency, easing bandwidth constraints, and enhancing data security.
On the topic of sustainability, Trolli noted that applying analytics to optimize resource usage and reduce environmental impact is becoming increasingly important. “Everyone in the industrial automation industrial software landscape is talking about sustainability. It’s a really massive trend today,” he said.
- Machine data is an invaluable asset that drives operational excellence.
- The proliferation of IoT devices has led to an unprecedented volume, variety, and velocity of data.
- Predictive maintenance is a top use case in manufacturing analytics.
- The real power of time series data lies in its ability to provide a detailed and continuous view of operations.
- Time series data is the backbone of modern manufacturing analytics.
- The future of industrial analytics is moving towards a place where smart factories are not just a concept but a concrete reality.
- The operationalization of AI and its impact on industrial analytics is a major emerging trend.
- Edge analytics and AI are increasingly important in managing workloads in an IoT-rich manufacturing environment.
- “The importance of machine data has grown exponentially, reshaping the whole industrial analytic space.” (Sebastian Trolli)
- “The role of machine data in industrial analytics will only become more and more significant.” (Jay Clifford)
- “The operationalization of AI in manufacturing environments is leading to smarter, more efficient, and more adaptable production processes.” (Sebastian Trolli)
- “Edge analytics reduces latency, eases bandwidth constraints, and enhances data security in comparison to cloud-based analytics.” (Sebastian Trolli)
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