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Data Design, Analytics & AI

Streamline Your Digital Transformation Process With a Modern IT/OT Architecture

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Paul Brodbeck, Chief Technologist

Paul has over 30 years of experience in design, programming, and commissioning of DCS and PLC automation control systems for specialty chemical and pharmaceutical companies.

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Modern IT/OT Reference Architecture

Your Digital Transformation process can be streamlined by deploying a modern IT/OT Reference Architecture based on Industry 4.0 data broker technology.

Keys to Success:

  • Adoption of a competitive business strategy, based on the deployment of Machine Learning and Artificial Intelligence
  • Centralization of enterprise-wide real-time data (standardized and contextualized) into a Unified Namespace to optimize analytics
  • MQTT Data Brokers in a Hub/Spoke architecture
  • Creation of a scalable data backbone based on a lightweight, open platform, and high-volume transport protocol

Successful digital transformation should be accomplished incrementally—with small, agile projects to develop confidence and value before scaling to the entire enterprise. An Agile project approach is essential to learn and adapt to the pros and cons of this new technology.

New Industrial IT/OT Architecture vs Purdue Model

A new industrial IT/OT architecture is emerging to improve data accessibility and reduce asset vulnerability. It will be built upon a hub/spoke integration pattern that will use MQTT brokers, the Sparkplug B standard and a Unified Namespace (UNS) design strategy.

Unified Namespace (UNS)

  • Unifies IT & OT, LIMS, & warehouse data for multiple plants into an enterprise-wide real-time data hub
  • All business data flows into and out of this central space in real time for a single point of truth
  • Data standards include ISA95 Part 2 and Sparkplug B
  • Data is published into the Unified Namespace in a standardized method with context
  • IT/OT architecture is built based on a hub-spoke distribution paradigm
  • Data brokers based on MQTT protocol are an essential feature of this architecture

Knowledge Graphs

A knowledge graph transforms a unified namespace from a simple mapping system into an intelligent, self-improving semantic layer that understands context, relationships, and meaning

  • Put data in context by linking and semantic metadata to provide a framework for data integration, unification, analytics and sharing
  • Ontologies function as the foundational framework for defining formal meaning within the data structure
  • Organize information as interconnected entities and relationships, creating a web of semantic connections that AI systems can navigate and reason over
  • AI systems can traverse these relationship paths to make logical inferences, enabling more sophisticated reasoning than pattern matching alone
  • Allow for fact verification and consistency by providing a more reliable foundation than relying solely on statistical patterns learned from training data

Data Example: Product Order

Artificial Intelligence (AI)

AI transforms manufacturing operations—from the plant floor to enterprise-wide decisions.

  • Process Optimization
  • Quality Assurance, Control and Inspection
  • Production Planning and Scheduling
  • Digital Twins
  • Energy Management

The key to successful AI implementation in manufacturing lies in starting with specific use cases where the value proposition is clear, ensuring proper data infrastructure is in place, and gradually expanding AI applications as capabilities and confidence grow.

Digital Transformation Maturity Assessment (DTMA)

Let Continua facilitate the development of your DX strategy with a DTMA. We will:

  • Meet with your company to assess your digital maturity
  • Develop a list of hardware and software platforms
  • Meet with Engineering, IT, Operations, Quality, & Leadership Teams
  • Deliver your Maturity Score
    • Recommend a Proof-of-Concept project
    • Recommend the architecture
    • Deliver a DTMA Final Report

Contact us to learn more.