The Purdue Model Is Evolving. Manufacturing Must Evolve With It

For decades, the Purdue Model has given manufacturers a structured way to organize operational technology (OT) and information technology (IT). Its hierarchy helped establish boundaries between physical processes, control systems, plant operations and enterprise technology.

Those principles remain valuable. But today's manufacturing environments don't always fit neatly into those layers.

Cloud platforms, connected applications, advanced analytics and new methods of industrial communication are changing where applications run and how data moves.

The result isn't the end of the Purdue Model. It's an opportunity to evolve it from a primarily location-based hierarchy into a hybrid framework built around workload requirements, data flows and operational risk.

In a traditional Purdue architecture, it was relatively straightforward to associate technology with a particular layer. Control systems remained close to physical production, while enterprise applications operated higher in the architecture.

Hybrid infrastructure makes those boundaries more fluid.

Consider Level 4, traditionally associated with enterprise IT. Back-office applications such as enterprise resource planning, warehouse management and other business systems may still operate on-premises. But some or all their functionality can also reside in the cloud.

The same principle can extend further into manufacturing operations.

A manufacturing execution system (MES), for example, doesn't necessarily have to be treated as a single workload with a single destination. Instead of asking, "Which Purdue level does this application belong to?" manufacturers increasingly need to ask, "Which functions need to run here, and which can safely run somewhere else?"

This shift mirrors the broader move toward hybrid infrastructure, where organizations evaluate workloads according to latency, resiliency, security and business outcomes rather than assuming every application belongs entirely on-premises or entirely in the cloud.

That shift makes workload requirements more important than physical location alone.

Systems controlling machinery and physical processes generally have very different requirements from systems analyzing production trends.

Analytics and trending applications may have greater flexibility. Historical production data, quality information and predictive maintenance models can often benefit from scalable cloud resources without directly controlling the production process.

A hybrid Purdue architecture allows manufacturers to place workloads according to factors such as:

• Latency: How quickly must the application respond?
• Availability: Can operations continue if cloud connectivity is lost?
• Data requirements: What information needs to leave the plant?
• Security: Who or what needs access to the application and its data?
• Operational impact: What happens to production if the workload becomes unavailable?

This approach preserves the structure of the Purdue Model without assuming every application must exist entirely within a single layer or location.

As applications become distributed, communication between layers becomes increasingly important.

The hybrid model introduces a more interconnected data architecture in which industrial protocols can serve different purposes across the environment. At lower levels, communication remains focused on physical assets, controllers and real-time operations. Higher in the architecture, secure industrial communication, APIs and publish/subscribe models can help move information between operations, enterprise systems and cloud applications.

This creates an opportunity for a shared data layer that makes plant information available to authorized applications without requiring every system to communicate directly with every other system.

The Purdue hierarchy still helps manufacturers understand what different systems do. The evolving architecture must also help them understand how those systems communicate.

That connectivity also changes the security equation.

Manufacturers need visibility into what information moves between on-premises and cloud environments, which applications consume it and who is authorized to access it. Encryption, secure connections, identity controls and clearly defined access policies become increasingly important as manufacturing data crosses traditional boundaries.

This is particularly important when cloud infrastructure becomes part of the manufacturing ecosystem. Responsibility for security is shared, meaning manufacturers must understand which protections are provided by their cloud environment and which remain their responsibility.

Rather than protecting only the perimeter between Purdue levels, security increasingly needs to follow workloads, identities and data wherever they operate.

Hybrid manufacturing introduces another important architectural consideration: dependency.

If part of a manufacturing execution system (MES), enterprise resource planning (ERP) platform or analytics environment resides in the cloud, what happens when the plant loses connectivity?

A fiber cut or provider outage shouldn't automatically become a production outage. Manufacturers need to determine which functions must continue locally and which can tolerate interruption.

For critical data, that may mean maintaining an on-premises copy while synchronizing information to the cloud. If connectivity is lost, the plant can continue collecting information locally and synchronize that data once the connection is restored.

Resilience therefore becomes part of workload placement:

The more important a function is to immediate production, the less dependent it should be on external connectivity.

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