What is Advanced Industrial Analytics (AIA)?
The AIA applications usually integrate with internal or external platforms, data connectors, and edge-to-cloud agents that facilitate data connectivity, modeling, and contextualization techniques required for effective analysis. Built on this data foundation, the applications then use several statistics, such as first principle, physics-based, and machine learning (ML) algorithms, to provide insights of varying levels of sophistication across the descriptive–prognostic spectrum.
Finally, these applications should ideally deliver value across several industrial use cases – including (but not limited to) asset performance, quality, manufacturing, productivity, process optimization, EHS, sustainability, etc., and target multiple user personas, such as engineers (industrial, process, reliability engineers), business users (cross-functional operations, quality, supply-chain, EHS personnel), and data scientists (data engineers, wranglers stewards, scientists, software engineers).
The IX Reference Architecture Evolves for the Journey to Zero+
The LNS Research Reference Architecture is a future-looking framework that categorizes the technological capabilities needed to deliver for all personas involved in manufacturing operations. While previous frameworks have been focused on core functions such as operations, quality, asset performance management, IT, and OT, our research shows that companies that successfully transform include a broader set of functional areas. Industrial operations have moved from a pure focus on cost reduction to a more extensive scope around profitability, sustainability, and the Future of Industrial Work (FOIW) lifecycle to achieve the vision of Operation 2030 and the Journey to Zero+.
Rub-A-Dub-Dub...It's All About the Data Hub
If these terms leave you more confused than when you started reading, join the club. I am an OT guy, and so much of this was new to me. And it’s another reason to have a good IT/OT architect on your team. The bottom line is that these terms support the various perspectives that must be addressed in connecting and delivering data, from architecture and patterns to services and translation layers. Remember, we are not just talking about time-series or hierarchical asset data. Data such as time, events, alarms, units of work, units of production time, materials and material flows, and people can all be contextualized. And this is the tough nut to crack as the new OT Ecosystem operates in multiple modes, not just transactional as we find in the back office.