EDITOR’S CHOICE
F
actories today operate in an environment shaped by volatile demand, rising quality expectations, energy constraints and increasingly complex production processes. These pressures are universal.
What differentiates leaders is not the presence of data, but their ability to transform data into shared understanding and coordinated action. Factories today operate in an environment
shaped by volatile demand, rising quality expectations, energy constraints and increasingly complex production processes. These pressures are universal. What differentiates leaders is not the presence of data, but their ability to transform data into shared understanding and coordinated action. Factories must evolve because data has
become the only scalable response to this complexity. Human expertise remains essential, but it cannot keep pace with the volume, velocity and interdependence of modern operations alone. Competitors are already building connected, intelligence-driven factories. Every improvement cycle increases their ability to respond faster, waste less and maintain quality under changing conditions. Those who fail to evolve do not merely lag behind. They lose the structural capability to compete.
FROM CONNECTED MACHINES TO CONNECTED MEANING Industry 4.0 is often associated with connectivity and automation, but its real impact begins when information becomes meaningful across the factory. Machines generate signals, sensors report values and systems log events, yet without structure and shared definitions, this data
ADAPTING TO THE NEW DEMANDS OF FACTORIES IN THE AGE OF INDUSTRY 4.0
By Francisco Almada Lobo, CEO, Critical Manufacturing
Manufacturing is entering a phase where competitiveness is no longer defined by who installs the most automation, but by who can learn, adapt and act faster
remains fragmented. The same concept can be represented differently in multiple systems, making correlation difficult and analytics fragile. A Common Data Model provides the foundation
for connected meaning by defining how manufacturing data is represented and related. It establishes consistent definitions for assets, processes, materials, states and events, allowing information from different sources to align rather than conflict. This consistency is what allows analytics, machine learning and AI to scale beyond isolated use cases. Contextualisation builds on this foundation
by turning raw signals into operational understanding. A sensor value on its own is just a number. It gains meaning only when connected to the machine it came from, the production order being executed, the material in use, the operating state of the equipment and the historical behavior of similar conditions. Context allows both humans and machines to understand not just what happened, but why it happened and whether
24 MARCH 2026 | FACTORY&HANDLINGSOLUTIONS it matters.
MES AS THE OPERATIONAL CORE OF CONTEXTUALISATION Manufacturing Execution Systems play a unique role in contextualisation because they already describe how the factory operates. MES connects production orders, routings, equipment, recipes, quality checks, operator actions and execution states in real time. In practice, this makes MES an operational digital twin of the factory, not in the sense of detailed physics-based simulation, but as a living representation of what is being produced, where, how and under which conditions. This operational digital twin becomes the
primary source of context for otherwise un- contextualised machine data. Sensor readings, PLC signals and historian data gain meaning when they are linked to MES information such as which product is running, which step of the process is active, whether the machine is starting up, running, idling, or being cleaned and which quality
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