Industrial Digitalization: Designing Information

What Information Is Really Needed

The digitalization of industrial machinery has significantly expanded the ability to observe what happens while a system is operating. More accessible sensors, advanced control technologies, and supervision systems now make it possible to measure and retain an increasing number of parameters, providing information that in the past would have been more difficult to collect continuously.

This broader technological availability, however, introduces a design question that should be addressed before selecting the tools themselves: what information is actually needed to understand machine behavior, monitor operating conditions, and support its management over time?

This question directly affects the way the system is conceived. Every measurement point introduces information that must be acquired, interpreted, and, in many cases, made available to other levels of the machine or plant. It therefore becomes useful to begin with the functions that need to be observed and the conditions that need to be understood, establishing a coherent relationship between physical behavior, measured variables, and the way the resulting information will be used.

From Machine Components to Machine Functions

A machine can be described through the components that make it up, but this representation only explains part of its behavior. Understanding how the system operates as a whole also requires considering the function performed by each element and the relationships established between the different parts. A component may contribute directly to machine operation, monitor a specific condition, or generate a signal used to observe what is happening. Its role within the system therefore depends on the function it performs and on how it interacts with other elements.

This becomes particularly important when the machine must make information available beyond its local operating level. A signal used exclusively to manage an internal function may require relatively simple handling. When the same information must also be interpreted by other systems, its meaning, the conditions under which it is acquired, and the purpose for which it will be used must be defined more clearly.

The complexity of an industrial system therefore also depends on the quality of the relationships between its components. Two machines built with similar elements may be managed in very different ways because the functions associated with the available data and the way that data is used can differ considerably. Looking at the system from a functional perspective helps define more precisely which information should accompany machine operation and which information is genuinely useful for understanding its condition.

Define What Needs to Be Known First

The ease with which measurement devices can now be added may encourage the collection of numerous parameters before their actual usefulness has been clearly established. From a design perspective, a more effective approach is to reverse this sequence: begin with the conditions that need to be understood and then identify the variables required to describe them. Simple conveying system provides a useful example. To understand the operation of a conveyor belt, relevant information may include motor status, product movement, speed, or the presence of unexpected stops. Temperature, humidity, and other environmental parameters may be highly relevant in some applications while having only a marginal influence in others. Their usefulness therefore depends on the phenomenon being observed.

Before adding a measurement point, it is useful to determine which condition needs to be identified, which behavior needs to be monitored, and how the resulting information will be used. These requirements can then guide the selection of the physical variable to be measured, the required accuracy, and the frequency at which the data should be made available.

This approach also reduces the risk of accumulating information that is difficult to use. A large number of parameters requires additional effort to organize and interpret, while a more selective data set built around genuinely relevant functions can make machine behavior easier to understand.

Digitalization therefore becomes part of the design process itself: information collection is defined in relation to the technical objectives of the system, rather than solely according to what current technology makes possible.

Consider Monitoring During the Design Phase

When information requirements are defined during the early stages of a project, they can be incorporated more naturally into the machine architecture. Addressing them later is often still possible, but it may require changes involving more elements than initially expected. Adding a new sensor, for example, may require mechanical provisions, new connections, changes to the control system, and updates to the levels where the information is displayed or used. A requirement that appears limited in scope can therefore affect several areas of the machine.

Initial design can take these possible developments into account without attempting to predict every future requirement. Industrial machines often have long operating lives, during which operating methods, production needs, and available technologies may change. What can be done, however, is to create conditions that make future developments easier to manage. Providing suitable space, access points, potential monitoring locations, and a sufficiently readable system architecture can simplify later modifications. The same principle applies to technical documentation, which should make it possible to understand which components are associated with specific functions and which information depends on them.

The ability to evolve can therefore be considered a design characteristic. The system is developed not only around current requirements, but also with an awareness that new phenomena may need to be monitored or additional control levels integrated during its operating life.

Giving Data the Context It Needs

Once acquired, information must be interpreted in relation to the conditions under which it was generated. A numerical value, considered in isolation, rarely provides a complete description of a machine's condition. A temperature of 80°C, for example, may represent normal operation or indicate a problem depending on the component involved, the sensor location, the operating regime, and the limits defined for that specific function. The same applies to pressure, speed, electrical consumption, and other operating parameters.

For this reason, the meaning of the information should remain understandable even when the data is used outside the point where it was originally generated. Those who understand the physical behavior of the machine must be able to define what needs to be observed; control specialists must translate this requirement into usable information; and supervision systems must receive data that retains a clear relationship with the actual condition it represents.

Coordination between different disciplines therefore plays an important role. Mechanical engineering, automation, and information management observe the same system from different perspectives, but they need to converge on the same functional requirement.

This convergence helps establish which information is necessary, how it should be acquired, and what meaning it must retain as it moves through the different levels of the system. Interfaces therefore become primarily a question of coherence between the different parts of the system, rather than simply a matter of the tools used to make them communicate.

A System-Level View to Connect Different Disciplines

The distribution of responsibilities across different disciplines and suppliers is a normal condition in industrial projects. Each party has detailed knowledge of a specific part of the system and works within its own technical domain. This specialization makes it even more important to maintain an overall view of the functions the machine must perform and the information that must move across its different levels.

Mechanical engineers understand the relevant physical phenomena and the operating conditions of the machine. Automation specialists manage the signals and logic required for control. Other systems may subsequently use the same information for supervision, maintenance, or analysis.

Coordination is therefore required to ensure that these contributions remain connected to the same objective. Expected outcomes, the information needed to achieve them, and the way that information must be made available should all be clearly defined.

This becomes especially important in complex systems, where each subsystem may be correctly designed when considered individually, while difficulties emerge in the relationships between different parts. A system-level view makes these relationships easier to identify during development and easier to understand throughout the machine's operating life.

Changes as Part of the Machine Lifecycle

The clarity with which functions and information have been defined becomes particularly valuable when the machine needs to be modified. Replacing a component, adding a measurement point, or changing a function may also affect the information produced by the system. If a signal is used across several levels, a local change may require updates to control logic, visualization, or the systems that consume that data.

When these dependencies have already been considered during design and are documented clearly, assessing the impact of a modification becomes more straightforward. It becomes possible to identify which functions are involved, which information is affected, and which additional elements may require an update.

This approach contributes to managing the entire machine lifecycle. The quality of a design also becomes visible in the ability to understand the system years later, when the people involved may have changed, components may have been replaced, or new operating requirements may have emerged.

Digitalization can therefore support the long-term evolution of a machine, provided that information and relationships have been organized coherently from the beginning.

Industrial Digitalization: Designing Information as Part of the Machine

The evolution of industrial digitalization and the growing range of technologies incorporated into machinery make it increasingly important to consider physical components, functions, and information together.

The discussion surrounding Italy's Transition 5.0 framework also reflects this evolution. The scope of an industrial investment can now include mechanical elements, control systems, software, and data collection tools, making it more difficult to understand the system solely through a list of individual components.

From a design perspective, this development suggests paying greater attention to the functions that connect the different parts of the system. The initial question becomes which machine conditions need to be visible during operation, which phenomena are important for assessing its behavior, and which information is required to manage anomalies or future modifications. These requirements can then be used to define sensor selection, acquisition methods, and the way data should be used. Information becomes part of the design with a specific purpose and maintains a clear connection with the physical phenomenon from which it originates.

This approach makes it possible to address digitalization with a level of complexity proportionate to the actual requirements of the machine. The resulting system can use available data to support the interpretation of operating behavior, improve coordination across disciplines, and make changes throughout the lifecycle easier to assess. Machine design therefore also includes defining the information that must accompany the system throughout its operating life. It is within this relationship between function, measurement, and data use that digitalization finds a coherent place within industrial engineering.