MES and IoT Integration: Transforming Manufacturing Data into Intelligence
- Why viewing MES not just as a monitoring tool but a data contextualizer is critical to digital transformation, as it provides meaning to disparate machine and sensor data.
- How integrating control and analytics ensures visibility without losing real-time action capabilities.
- With advanced data correlation capabilities, manufacturers can link process deviations to specific products, enabling predictive quality and operational optimization.
The manufacturing floor has long been a complex ecosystem of sensors, machines and software. Each of these manufacturing components generate streams of data that, more often than not, were siloed and underused. For manufacturers looking to capture the advantages of industry’s digital transformation, the challenge isn't just collecting and aggregating all these data points; it's how to turn this raw information into intelligence that can drive real operational improvements.
At its recent MES & Industry 4.0 Summit, Critical Manufacturing showed how its MES (manufacturing execution system) software and Connect IoT platform can bridge the gap between unstructured shop floor data generation and the structured world of digital manufacturing systems.
The point of this demonstration was not just to highlight the technical achievement of integrating MES and IoT platforms but to show how manufacturers can realize the practical benefits of Industry 4.0 initiatives.
By maintaining the balance between comprehensive data collection and operational control, while solving the critical challenge of data correlation and contextualization, approaches like the one shown by Critical Manufacturing provide a blueprint for how digital transformation can be realized without a complete restructuring of equipment and processes.
The data contextualization problem
"It's very hard to turn data into information," explained João Roque, architect and advocate for automation and connectivity at Critical Manufacturing. The shop floor generates enormous volumes of data from disparate sources, such as temperature sensors, machine controllers, quality inspection systems and material tracking devices. But without proper context, this data remains largely meaningless.
Critical Manufacturing's approach to addressing this issue lies in understanding that MES software can serve as more than just a monitoring tool as it’s historically been used. It can act as a "contextualizer" of plant floor reality, said Roque. Unlike raw data streams that simply report numbers, MES provides the organizational framework that gives meaning to those numbers. It understands the relationships between sites, areas, facilities and resources. “It knows about your products, workflows and process steps,” he said.
This “understanding” is why Roque said, "MES is opinionated about reality." This "opinionated" approach is a strength, explained Roque, because it provides the structured framework needed to make sense of the data chaos inherent in manufacturing operations.
With MES providing the contextual framework for disparate plant floor data, Critical Manufacturing’s Connect IoT serves as the interface between the physical and digital worlds. Acting as an "interpreter of reality," Roque said the platform connects with machines and sensors to bring together disparate data streams and contextualize them within the MES framework.
To highlight this integration, Roque presented a demonstration of its application in SMT (surface mount technology) manufacturing. SMT processes involve mounting electronic components directly onto printed circuit board surfaces, a process that appears simple but involves complex interactions between multiple systems, materials and environmental factors.
SMT (surface mount technology) application details
The demonstration used by Roque focused on reflow oven operations, a critical step in SMT manufacturing where solder paste is heated to permanently attach components to PCBs. Using the IPC-2591 Connected Factory Exchange (CFX) standard — a relatively new protocol that merges big data collection with peer-to-peer machine control — Roque and Ricardo Magalhães, architect and advocate for data and analytics at Critical Manufacturing, explained how manufacturers can achieve both comprehensive analytics and real-time operational control by showing dashboards that successfully link lot numbers, parent lot information and product specifications with real-time equipment data.
In the SMT demo, this system tracked individual panels through the entire SMT process — from material expansion and serialization through printing, pick-and-place operations, reflow soldering and final optical inspection.
Highlighting detailed dashboard data during the demonstration, Roque showed examples of real-time temperature curves for the reflow oven zones, setpoint versus actual temperature deltas and complete material genealogy tracking to spotlight the kind of visibility manufacturers can access with these software systems. More importantly, Roque, said, the software enables drill-down analysis, so that operators can click on any material identifier and see its complete processing history, including all parameters collected at each step.
The correlation capabilities of the IoT Data Platform extend to quality analysis as well. “The system can identify when process parameters deviate from specifications and correlate those deviations with specific products and process steps,” said Magalhães. “For example, if a reflow oven zone shows a 20-degree temperature delta from its setpoint, the system can identify exactly which products were affected and predict potential quality issues, such as component ‘popping’ due to rapid moisture vaporization in moisture-sensitive components.”
Implementation framework and testing
Roque pointed out that Critical Manufacturing not only provides data integration tools via its Connect IoT and its IoT Data Platform, but also frameworks for testing and validation of applications like the SMT example he highlighted. He said Critical Manufacturing offers development and testing frameworks that allow manufacturers to build and validate integrations before deploying them in production environments.
The virtual SMT line simulation running on a standard computer in Roque’s demo was an example of this, complete with IPC CFX handlers and message brokers.
This approach “allows manufacturers to develop and test integration logic without disrupting production operations,” said Roque — a critical capability for high-volume, precision manufacturing environments.
The ultimate goal of this data integration capability extends beyond simple monitoring to enabling predictive analytics and optimization. “By streaming correlated data to platforms like Kafka and presenting it through tools like Grafana and Power BI, manufacturers gain unprecedented visibility into their operations,” Roque added.
Beyond monitoring to control and understanding
What sets this approach apart from typical IoT data collection for analysis is its emphasis on maintaining control capabilities alongside data collection. Many Industry 4.0 initiatives focus primarily on monitoring and analytics, but Critical Manufacturing's approach preserves the ability to take direct action based on insights gained.
"We don't want to lose this control aspect," Roque emphasized. "It's actually one of the most fascinating things about how the shop floor operates."
This maintenance of control is possible because Connect IoT handles two primary categories of information: transactional control data (such as material tracking events when products enter and exit processes) and analytics data (detailed process parameters like temperature profiles, timing and equipment performance metrics). This dual approach ensures that manufacturers gain visibility without sacrificing the ability to intervene when necessary.
One of the most significant technical challenges in manufacturing data integration involves correlating information from systems that operate on different time bases and data structures. Roque noted that equipment controllers and MES systems often use different timestamps, data formats and reference systems, making it difficult to create coherent views of operations.
The reflow oven example used in the demo showed how Critical Manufacturing is addressing this issue — the system tracks materials through work start and work completed events while simultaneously capturing detailed process data from each heating zone. When integrated with the MES, this information becomes contextualized, meaning that operators don't just see that a temperature reading occurred; they understand which specific product lot was being processed at the time, what the target specifications were and how the actual performance compared to requirements.
