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Schneider Electric is calling on industrial companies to take a more disciplined approach to digital transformation, arguing that sophisticated technologies such as digital twins should be introduced to solve identifiable operational problems rather than adopted simply because they are considered strategically important.

The message comes as manufacturers, energy producers and infrastructure operators increase investment in industrial automation, artificial intelligence, connected assets and advanced analytics. While these technologies can improve asset visibility and operational decision-making, their commercial value depends heavily on how effectively they are connected to real engineering and production requirements.

Elijah Daniel, Country Sales Director for Process Automation in Sub-Saharan Africa at Schneider Electric, said industrial organisations should first define the business problem they intend to address before deciding which digital technology should be deployed.

That approach places greater emphasis on measurable outcomes such as lower maintenance expenditure, reduced engineering rework, improved equipment reliability, faster project execution and higher production availability.

Schneider Electric official website

Digital twins have become one of the most closely watched technologies in industrial digitalization. A digital twin can create a virtual representation of physical equipment, production facilities or industrial processes by combining engineering information with operational data collected from sensors, PLC systems, control platforms and industrial networks.

When properly implemented, the technology allows engineering and operations teams to examine asset behaviour, evaluate different operating scenarios and identify potential problems without interfering with the physical process.

However, the rapid growth of the digital twin market has also raised questions about return on investment. Market research cited in the report projects the global digital twin market to expand from approximately $21.14 billion in 2025 to more than $149.81 billion by 2030.

Such growth reflects strong interest from manufacturing, energy, infrastructure and process industries. Yet a larger technology market does not automatically translate into better results for individual industrial operators.

For many companies, the first obstacle is not a shortage of software but the fragmented nature of their operational data.

Mechanical engineering, electrical engineering, instrumentation, process control, maintenance and production departments often maintain information in separate systems. Engineering drawings may sit in one database, equipment information in another, while maintenance histories and production records remain isolated from the control environment.

This fragmentation creates practical problems during both new projects and ongoing plant operations. Engineers may need to recreate information that already exists elsewhere, maintenance teams may have incomplete asset histories, and plant managers may lack a unified view of equipment performance.

Digital engineering platforms can address part of this problem by establishing a common information environment. Instead of repeatedly transferring or rebuilding engineering data, teams can work from a shared source throughout the project lifecycle.

The same principle becomes increasingly important once operational data is added to the equation.

Information from industrial controllers, sensors, historians, SCADA systems, DCS platforms and maintenance management systems can provide a much broader picture of how an asset behaves in the field. When these datasets are connected correctly, companies can move beyond static engineering documentation toward continuously updated operational models.

This creates opportunities for predictive maintenance, condition monitoring and performance optimisation.

For example, an industrial operator may use historical equipment data to identify patterns associated with abnormal operating conditions. Maintenance teams can then investigate assets showing signs of deterioration before a failure results in an unplanned shutdown.

The potential benefits are particularly significant in industries where equipment availability has a direct impact on production economics. Oil and gas facilities, power plants, mining operations, chemical processing facilities and large manufacturing sites often depend on a relatively small number of critical assets.

A failure involving a compressor, turbine, pump, motor, control system or electrical distribution component can create consequences far beyond the cost of replacing the failed component.

This is where the combination of automation and industrial analytics becomes increasingly relevant.

Schneider Electric's broader digital strategy includes connected automation, software, industrial control and energy management technologies. Its official portfolio covers areas including PLC and PAC controllers, motion and drives, industrial automation, software and digital services.

For industrial buyers, the important question is therefore shifting from whether a company should adopt digital technology to where digital technology can generate the greatest operational improvement.

A manufacturing company may have a clear business case for using analytics to reduce unplanned downtime. An engineering contractor may gain more value from eliminating duplicated engineering work. A refinery may prioritise asset reliability and process optimisation, while a power utility may focus on equipment condition, maintenance planning and availability.

The technology stack can be similar, but the commercial justification can be very different.

This distinction is becoming more important as AI automation becomes integrated into industrial platforms.

Artificial intelligence can analyse large volumes of operational data, identify correlations and generate recommendations that would be difficult for engineering teams to produce manually. However, AI systems remain dependent on the quality of the information available to them.

Poor historical records, inconsistent tag structures, missing sensor data and weak data governance can significantly reduce the usefulness of advanced analytics.

In other words, artificial intelligence does not eliminate the importance of industrial data management. It increases it.

A digital twin connected to inaccurate asset information may produce an impressive visual model without providing reliable operational insight. Similarly, a predictive analytics system trained on incomplete maintenance records may generate recommendations that engineers cannot confidently act upon.

This makes data quality a strategic issue rather than simply an IT concern.

Industrial companies also need to consider how digital systems connect with existing infrastructure. A modern digital architecture may involve PLC and DCS systems, industrial Ethernet, sensors, historians, cloud platforms, ERP systems, maintenance software and machine-learning applications.

Keeping these environments connected without creating additional cybersecurity or data-management problems requires careful engineering.

The challenge can be even greater in older plants, where control systems may have been installed over many years and equipment from different generations and vendors continues to operate together.

For these facilities, digital transformation does not necessarily mean replacing every existing control component.

In many cases, the more practical route is to improve connectivity around existing automation infrastructure and gradually introduce higher-level monitoring, analytics and asset-management capabilities.

That approach can reduce the technical and financial risk associated with large-scale modernization projects.

For African industrial markets, the issue is particularly relevant. Energy infrastructure, manufacturing facilities, mining operations and large industrial projects are expanding while operators continue to face pressure to control operating expenditure and improve productivity.

Nigeria provides a significant example because of the scale of its energy infrastructure and its substantial oil and gas resources. As industrial assets become more complex, operators have increasing opportunities to apply connected automation and digital engineering tools to maintenance, project execution and asset performance.

But infrastructure and skills remain important considerations.

Advanced software alone cannot compensate for inadequate connectivity, unreliable field instrumentation or a shortage of engineers capable of interpreting operational data. Successful digital transformation therefore requires a combination of technology, engineering expertise, data governance and organisational processes.

The same principle applies to predictive maintenance software.

A system may be technically capable of identifying equipment anomalies, but the value is ultimately determined by what happens after an anomaly is detected. If maintenance teams cannot access the asset, obtain replacement components or schedule an intervention, the analytical capability may have limited commercial impact.

Industrial digitalization therefore needs to connect information with action.

This is also changing the way industrial procurement teams evaluate automation technologies. Instead of looking only at software features, buyers increasingly need to consider integration capability, lifecycle support, data accessibility, cybersecurity, compatibility with installed control systems and the availability of engineering expertise.

For equipment-intensive industries, this can make the relationship between legacy automation hardware and modern digital platforms particularly important.

Existing PLCs, DCS controllers, I/O systems, communication modules and industrial sensors can represent valuable sources of operational data. Preserving that investment while adding modern monitoring and analytics capabilities can sometimes deliver a more practical result than pursuing a complete system replacement.

Schneider Electric's own product and support ecosystem reflects this broader shift toward connected automation, with resources covering product documentation, software, product selection, replacement solutions and digital services.

The growing role of digital twins also highlights an important change in industrial engineering: data is becoming part of the asset itself.

Historically, engineering information was often treated as documentation produced during a project and archived after commissioning. Increasingly, companies are treating engineering data as a continuously usable resource that supports commissioning, operation, maintenance and eventual asset replacement.

That transition can reduce information loss between project phases.

It can also improve communication between engineering contractors, equipment suppliers, plant operators and maintenance teams. When stakeholders work from consistent asset information, fewer resources are required to recreate drawings, specifications and equipment records.

The long-term objective is not simply to create a more sophisticated digital representation of a plant.

The real objective is to make industrial decisions faster, more accurate and more economically effective.

That distinction is likely to influence future investment decisions as companies increase spending on AI, industrial IoT, automation and digital engineering.

Executives will increasingly ask whether a digital project has reduced downtime, lowered maintenance costs, shortened engineering cycles, increased asset availability or improved production performance.

Those metrics provide a much stronger basis for investment than the number of connected assets or the sophistication of a digital model.

ents at the plant level.

The next phase of industrial digitalization is therefore unlikely to be defined solely by how much technology companies deploy.

It will be defined by how effectively they connect automation, engineering data, AI and operational decision-making to measurable business performance.

For companies considering digital twins or advanced industrial analytics, the starting point may be surprisingly simple: identify the equipment failure, engineering bottleneck, maintenance expense or production constraint that needs to be improved first.

Only then should the technology be selected.

Written by: Michael Carter

Michael Carter is an industrial automation and process-control writer with more than 12 years of experience covering PLC, DCS, SCADA, industrial networking and asset-management technologies. His work focuses on the practical impact of automation investment on plant reliability, engineering productivity and industrial operations.

For automation suppliers and industrial technology vendors, this creates a more demanding market. Technology demonstrations may attract attention, but long-term adoption will depend on whether suppliers can demonstrate practical improvements at the plant level.