Overview

The digital transformation promises transformative gains in maintenance, quality, and throughput, but only when the underlying data is structured, governed, and consistently defined. Most manufacturers inherit decades of implicit tag naming, inconsistent units, and undocumented context that cause AI models to drift, misinterpret values, or fail at scale. As organisations move from “collecting data” to “operationalising AI,” the absence of a standardised manufacturing data model becomes the first great barrier to enterprise-wide adoption.

Standardised data modelling fails without governance. Yet in most manufacturing organisations, no one is clearly responsible for enforcing consistent data models across the enterprise. Some groups ignore the issue. Others handle it informally. A few do it reasonably well, but only within their own silo. Without clear ownership and governance, data consistency breaks down. That is the second major barrier to enterprise adoption.

The third barrier is the gap between OT, IT, and security. Operations protects uptime. Security protects the perimeter. IT needs plant data to support analytics and business outcomes. These are all legitimate priorities, but no one owns the space between them. Nothing moves because no one has explicit authority to resolve the trade-offs. This is not a people problem. It is an architecture and accountability problem. Digital transformation rarely dies in the boardroom or the proof of concept. It dies here, at the seam between teams, where everyone has responsibility for part of the problem and veto power over the rest.

Target audience:

• solution architects and systems integrators leading digital transformation programs
• CTOs, operations leaders, and digital manufacturing executives
• automation engineers responsible for tag structures, PLC UDTs, and UNS implementation
• data/AI teams struggling with inconsistent plant‑floor semantics.

This session presents a practical blueprint for building standardised manufacturing data models and a Unified Name Space (UNS) that can power the digital transformation and enable AI to reliably consume them at scale.

Attendees will learn 10 actionable strategies to turn chaotic, inconsistent plant data into AI‑ready information that scales across assets and facilities.

Speaker

John Rinaldi, CEO and Chief Strategist, Real Time Automation (RTA)

John is the CEO and Chief Strategist of Real Time Automation (RTA), a global leader in industrial connectivity solutions. A preeminent authority on industrial networking architectures, John has spent decades bridging the gap between complex factory floor protocols and enterprise-level data requirements.

He is the author of six definitive texts on industrial communications, including Industrial Ethernet, Ethernet/IP, OPC UA: The Everyman’s Guide, and has published over 75 videos and 500 technical treatises on the evolution of the connected factory. Under his leadership, RTA has become synonymous with protocol simplicity and high-performance integration, providing engineered solutions for mission-critical applications worldwide.

The material presented has not been peer-reviewed. Any opinions are the presenter’s own and do not necessarily represent those of IChemE or the Process Management and Control Special Interest Group. The information is given in good faith but without any liability on the part of IChemE.

Time

14:00–15:00 BST.

Software

The presentation will be delivered via Microsoft Teams. We recommend downloading the app from the Microsoft website, rather than using the web portal.