Legacy and non-IP equipment, such as Modbus, M-Bus, DLMS/COSEM, and Profibus controllers, still manage key plant processes but don't expose modern data interfaces.
Building and energy systems, typically using BACnet or proprietary APIs, hold rich information on energy consumption, temperature, and environment — essential for sustainability and efficiency tracking.
New-generation IoT sensors, including LoRaWAN and NB-IoT devices, and mobile assets such as AGVs or robots, generate data in lightweight, event-driven formats not designed for MES ingestion.
In regulated or air-gapped environments, data movement is deliberately restricted, making it difficult for MES to maintain real-time synchronisation.
The Next Frontier for MES: Securing the Last Mile of Industrial Data
For more than two decades, Manufacturing Execution Systems have been the backbone of digital manufacturing — but their next phase of growth depends on solving the complexity of industrial data integration.
They bridge enterprise planning and plant operations — turning production orders into machine instructions, coordinating materials and people, and recording every step of production in real time.
According to Mordor Intelligence (2025), the global MES market is valued at around £15 billion and is forecast to grow at a compound annual rate of roughly 10 percent through 2030. This expansion reflects the growing need for real-time visibility, traceability, and governance, along with the rising use of AI, analytics, and digital twins in manufacturing operations.
MES has already delivered major improvements in productivity, quality, and compliance. But as factories evolve, it is encountering new data and integration challenges that were never part of its original design. These challenges are now defining the next phase of MES maturity.
The Origins of MES
MES first emerged in the late 1980s, when manufacturers needed a bridge between enterprise resource planning (ERP) systems and plant-level automation such as SCADA, DCS, and PLCs. By the mid-1990s, standards like ISA-95 had established MES as the coordination layer between business systems and process control.
Initially focused on tracking and reporting, MES grew to include quality, maintenance, logistics, and scheduling. Today, it remains one of the most established and mission-critical components of industrial IT.
What MES Has Delivered
MES has provided a consistent foundation for operational excellence. It improves throughput by optimising schedules, reduces downtime, ensures traceability across materials and batches, and enforces quality standards automatically. By standardising workflows, MES brings structure and reliability to even the most complex manufacturing environments.
The result is greater control, higher compliance, and a clearer link between enterprise planning and shop-floor performance.
What End Users Now Expect
Having proved its value, MES is now being asked to deliver at greater scale and speed. Manufacturers want to deploy one standard MES model across all sites, not build new integrations every time. They seek shorter project cycles measured in weeks, not months. They expect real-time connectivity across increasingly diverse equipment — from legacy PLCs to smart sensors. And they need data they can trust, verified, timestamped, and traceable from source to system. At the same time, new data regulations mean sovereignty and governance must be built in from the start.
These priorities reflect how manufacturing has changed: MES is no longer just a control layer, but part of a wider ecosystem for operational intelligence.
Why Technical Challenges Slow MES Potential
While MES platforms are evolving, they still depend on the underlying infrastructure of each site. In practice, this makes data acquisition, validation, and replication harder than it appears on paper.
1. Equipment and protocol diversity
Factories often combine decades of investment in automation — Siemens, Rockwell, Beckhoff, Schneider, ABB and others — each using different communication standards. MES connectors handle common interfaces well, but every variation in protocol or firmware introduces integration work. Scaling a solution across multiple plants becomes slow and resource-intensive.
2. Incomplete data context
Raw machine data rarely includes the metadata that makes it meaningful. A value of "3.4" might represent volts, bar, or degrees; timestamps may not be synchronised; sensor calibration may be unknown. MES can collect the numbers, but without this context, analysis and AI rely on assumptions rather than verified information.
3. Security segmentation
Operational (OT) and IT networks are kept separate for safety and cybersecurity. However, MES increasingly relies on data that crosses these boundaries. Traditional network trust models don't fit modern zero-trust principles, and manual workarounds often delay deployments.
4. Replication and consistency
Even with a strong MES template, every site has its own device lists, naming conventions, and policy rules. Deployments must be adapted locally, so what should be configuration work becomes a custom integration project. As a result, MES rollouts can lose consistency and speed over time.
5. Integration overhead
Each connector or script adds long-term maintenance effort. When devices are replaced or upgraded, integrations must be revalidated. Cumulatively, these small tasks form a significant cost barrier to scaling MES quickly.
Together, these challenges limit how rapidly MES can be deployed and how much data it can meaningfully use. They are not problems with MES itself, but with the heterogeneous data landscape it must operate within.
The Expanding Data Landscape
Modern factories contain an enormous variety of data sources beyond traditional PLCs and SCADA systems. These are not incompatible with MES, but they are harder for it to access and normalise at scale.
Each of these data sources adds valuable context, but connecting and governing them securely is complex. The challenge lies in collecting this information reliably and presenting it to MES in a structured, trusted form.
How Altior Helps
Altior was designed to address precisely this challenge — bridging the last mile between diverse industrial assets and the digital systems that depend on them. It provides a federated operational data infrastructure that connects every device, validates every signal, and enforces governance from the edge to the enterprise.
Altior supports more than thirty industrial and IoT protocols — from OPC UA and Modbus to DLMS/COSEM, BACnet, MQTT, and LoRaWAN — allowing both IP and non-IP devices to share data securely without custom gateways or code.
Before data leaves the production line, Altior performs edge-level validation, checking units, calibration, and acceptable ranges. Invalid or inconsistent readings are quarantined, so MES and analytics always receive clean, verified data.
Security is built into every connection. Altior uses mutual authentication (mTLS) and policy-as-code to control data access and actions, while all transactions are logged immutably to meet standards such as IEC 62443 and NIS2.
Once configured, Altior's schemas and policies can be replicated across sites. Each plant inherits the same data definitions, validation rules, and governance model — enabling consistent, auditable MES deployments at scale. For enterprises with strict residency requirements, Altior can operate as a white-label platform-as-a-service, ensuring full data sovereignty while supporting enterprise-level analytics.
The Result: MES That Scales
By introducing Altior beneath MES, manufacturers turn integration into infrastructure. Deployments that once took months can be completed in weeks. Data reliability routinely exceeds 99.9 percent, while zero-trust security ensures every exchange is authenticated and traceable.
The same MES template can now be deployed globally with predictable performance and behaviour. Manufacturers gain faster rollouts, lower total cost of ownership and a more complete, real-time view of their operations.
Who Benefits
Altior strengthens the entire MES ecosystem:
End users (manufacturers) gain dependable, validated data and can deploy MES faster and more consistently across sites.
MES and platform providers gain a neutral, standards-based data layer that simplifies integration, reduces delivery cost, and strengthens compliance.
Consultants and system integrators gain a repeatable, configurable framework that replaces bespoke engineering with scalable, maintainable deployments.
MES and platform providers gain a neutral, standards-based data layer that simplifies integration, reduces delivery cost, and strengthens compliance.
Consultants and system integrators gain a repeatable, configurable framework that replaces bespoke engineering with scalable, maintainable deployments.
In Summary
The MES market continues to expand rapidly, but accelerating its next phase of growth depends on addressing the complexity of industrial data. Altior provides the secure, validated, and standardised foundation that allows MES to evolve — connecting every device, enforcing governance, and turning fragmented data into trusted intelligence.
Altior turns the last mile of industrial data into the first mile of operational intelligence — enabling MES to scale faster, work smarter, and deliver its full potential.