The Next Shift in Smart Manufacturing: Standards Over Systems

Why interoperability, not platforms, will define the next decade of Industry 4.0

By: G Vikram

Reviewers: Murugan Boominathan, David Cameron & Suraj Sriram

Introduction: Smart Manufacturing Is at an Inflection Point

For more than a decade, Smart Manufacturing initiatives have been driven by systems. ERP, MES, PLM, IIoT platforms, analytics stacks, and now AI have dominated transformation roadmaps.

Yet across industries and geographies, in many current implementations, a consistent pattern has emerged:

This is no longer purely a technology gap.
It is increasingly a standards and data-governance gap.

Manufacturers have successfully connected systems—but often struggle to scale meaning, trust, and reuse across operations.

Smart Manufacturing is now entering a new phase, where how data is defined, governed, and consumed matters more than which system produces it.

For manufacturing operations specifically, this shift affects:

• Production execution consistency
• KPI reliability (including OEE alignment)
• Quality traceability
• Cross-plant performance benchmarking

This marks a structural shift in how digital manufacturing ecosystems must be designed.

Why the Current Approach Is Reaching Its Limits

Traditional manufacturing digitalization has relied heavily on:

• Platform-centric data models
• Custom point-to-point integrations
• Project-specific interpretations of data meaning

While this approach enables connectivity, it does not consistently scale operational consistency or lifecycle continuity.

The consequences are increasingly visible in manufacturing execution outcomes:

• Inconsistent OEE calculations across sites, due to varying definitions of Availability, Performance, and Quality (misalignment with ISO 22400)
• Digital Twins stagnating after commissioning, as-built and as-maintained data lack governed semantic continuity
• AI models failing to scale across plants, because contextual definitions differ
• Genealogy breaks impacting compliance and recall management

These are not integration failures.

They are semantic alignment failures.

In many organizations, semantic technical debt is becoming systemic.

A Fundamental Shift in How Manufacturing Data Is Handled

What is changing today is not just tooling, but the philosophy of interoperability.

A new paradigm introduces a critical mindset shift:

• Data is consumed, not copied
• Access is governed, not hard-coded
• Meaning is standardized, not inferred

Instead of moving data between systems, systems reference trusted, structured digital representations governed by usage policies and contracts.

For manufacturing operations, this reduces:

• KPI reinterpretation across plants
• Quality context loss between MES and QMS
• Supplier-to-OEM genealogy discontinuities
• Manual reconciliation during audits

This aligns directly with the MESA Manufacturing Operations Management (MOM) Capability Framework, where production, quality, maintenance, and inventory processes must share a consistent semantic foundation.

From Integration to Governed Interoperability

This shift moves Smart Manufacturing away from one-off integration projects toward policy-driven data ecosystems.

In such architectures:

• Suppliers, OEMs, operators, and service partners collaborate without duplicating data
• Intellectual property remains protected
• Ownership and control are preserved across organizational boundaries

Interoperability becomes:

• Architectural rather than contractual
• Embedded rather than renegotiated
• Governed rather than improvised

Within the context of the MESA MOM Capability Framework, this strengthens continuity across:

• Production Operations Management
• Quality Operations Management
• Maintenance Operations Management
• Inventory Operations Management

Operational consistency becomes structurally enabled rather than manually enforced.

Lifecycle Continuity as a Strategic Advantage

With standardized digital representations, manufacturing data no longer breaks across lifecycle stages.

Information flows continuously from:

• As-designed
• As-ordered
• As-built
• As-delivered
• As-maintained

For manufacturing execution, this continuity directly supports:

• Consistent OEE benchmarking across lifecycle stages
• Traceable genealogy across supplier and production events
• Preserved quality context from design through field service
• Audit-ready regulatory compliance

This continuity forms the foundation required for:

• Living Digital Twins
• Scalable AI in production
• Predictive and prescriptive operations
• Regulatory traceability

Without semantic continuity, Digital Twins degrade into dashboards and AI remains experimental in production environments.

A Constraint the Industry Must Acknowledge

Based on real-world implementation experience across multiple manufacturing programs, a consistent constraint emerges:

• Limited widely adopted end-to-end data standards
• Lack of enterprise-wide canonical operational data models
• Vendor optimization for platform integration rather than interoperability
• Data semantics defined per project rather than per lifecycle

The outcome is predictable:

Technology is deployed, but operational meaning remains fragmented.

Without standards, Smart Manufacturing becomes intelligent—but only within isolated system boundaries, limiting cross-site performance comparability and lifecycle traceability.

Why This Matters Now

AI, Digital Twins, and autonomous manufacturing systems are fundamentally context-dependent.

Without standardized semantics:

• AI scales experiments, not stable production outcomes
• Digital Twins inform monitoring, not closed-loop decisions
• OEE comparisons lack consistent definitions across plants
• Genealogy and compliance reporting require manual reconciliation
• Ecosystem collaboration remains fragile

Standards-led interoperability is increasingly becoming a prerequisite for scalable and auditable manufacturing operations.

The Strategic Takeaway for Manufacturing Leaders

The next phase of Smart Manufacturing will not be led by:

• The largest platform
• The most features
• The fastest deployment

It will be led by organizations that:

• Standardize operational data meaning
• Align KPI definitions across sites
• Govern data usage across lifecycle and partners
• Enable ecosystem-level interoperability

Standards are becoming the operating system of manufacturing collaboration.

Within the MESA reference model, this reinforces execution integrity across MOM capabilities rather than replacing systems.

Final Thought

Smart Manufacturing is evolving:

• From systems to standards
• From integration to interoperability
• From projects to ecosystems

For manufacturing operations leaders, the fundamental question becomes:

Is our operational data defined and governed in a way that preserves meaning consistently across systems, partners, and lifecycle stages?

That answer determines whether Smart Manufacturing scales sustainably—or remains technically connected but operationally fragmented.

References

• MESA International – MES Reference Models
• MESA International – Manufacturing Operations Management (MOM) Capability Framework
• ISO 22400 – KPIs for Manufacturing Operations
• RAMI 4.0 – Reference Architecture Model Industry 4.0
• Heidel, R., Hoffmeister, M., Hankel, M., Döbrich, U., 2019. The Reference Architecture Model RAMI 4.0 and the Industrie 4.0 component, 1st ed. VDE Verlag, Berlin, Germany.
• Grüner, S., Hoernicke, M., Stark, K., Schoch, N., Eskandani, N., Pretlove, J., 2023. Towards asset administration shell-based continuous engineering in process industries. at - Automatisierungstechnik 71, 689–708. https://doi.org/10.1515/auto-2023-0012
• Möller, F., Jussen, I., Springer, V., Gieß, A., Schweihoff, J.C., Gelhaar, J., Guggenberger, T., Otto, B., 2024. Industrial data ecosystems and data spaces. Electron Markets 34, 41. https://doi.org/10.1007/s12525-024-00724-0
• https://industrialdigitaltwin.org/en/
• https://vdma-interoperability-guide.orghttps://internationaldataspaces.org