Schneider advances digital tools for industrial efficiency

Schneider Electric is urging industrial companies to focus on measurable business outcomes when adopting digital technologies, warning that investments in tools such as digital twins may deliver limited returns when they are not linked to specific operational problems.

Country Sales Director, Process Automation, Sub-Saharan Africa at Schneider Electric, Elijah Daniel, argued that companies should identify the challenges they want to solve before choosing the technologies to deploy as industrial organisations increase spending on digital transformation.

Digital twins create virtual representations of physical equipment, facilities or industrial processes by combining engineering information with data from sensors, control systems and other operational sources, allowing companies to monitor assets, test scenarios and identify potential problems without disrupting physical operations.

Research by MarketsandMarkets projects that the global digital twin market will grow from about $21.14bn in 2025 to more than $149.81bn by 2030, driven by adoption across manufacturing, energy, infrastructure and process industries.

The growth in investment has also exposed a problem for industrial companies, as some digital twin projects have struggled to produce expected returns because technology deployment has taken precedence over clearly defined business objectives.

Daniel’s comments reflect an approach increasingly promoted across the industrial technology sector, where companies are encouraged to begin with problems such as equipment failures, engineering delays, excessive maintenance costs or inefficient production before selecting digital tools.

Fragmented data remains a challenge for industrial organisations, with mechanical, electrical, instrumentation and process engineering teams often working across separate systems that can produce inconsistent datasets, duplicated work and limited visibility.

Digital engineering platforms can bring these datasets together, allowing teams to work from a common source of information and reducing the need to recreate data across different stages of a project.

Companies can also combine engineering information with data from control systems, sensors, maintenance records and performance monitoring tools to support activities ranging from project design and execution to asset management and production optimisation.

The approach could be relevant to African industrial markets, where energy, manufacturing and infrastructure companies are managing increasingly complex projects while seeking to improve productivity and control operating costs.

Nigeria has about 37.01 billion barrels of proven crude oil reserves and about 215.19 trillion cubic feet of natural gas reserves, giving the country a large base of energy infrastructure where digital technologies could be used to improve project delivery, maintenance and asset performance.

Digital systems can help companies reduce engineering rework, identify equipment problems earlier, and improve the availability of critical assets, although the benefits depend heavily on the quality and accessibility of the underlying data.

Advanced digital twin systems often connect with enterprise applications, cloud platforms, operational historians, weather information, enterprise resource planning systems, and machine-learning models, increasing the amount of data required to operate them effectively.

Companies with fragmented records, unreliable historical data or weak data governance may therefore struggle to achieve the predictive maintenance and operational optimisation promised by sophisticated digital twin deployments.

This is becoming more important as industrial companies combine digital twins with artificial intelligence, automation and advanced analytics, technologies that require reliable data to generate useful insights and support operational decisions.

For African businesses, infrastructure and skills constraints can add to the difficulty, but the expansion of energy, manufacturing and other industrial projects is creating opportunities for companies to use connected digital systems to improve efficiency.

Daniel’s argument is that digital twins should be treated as a means of solving operational problems rather than as an end in themselves, with companies measuring their value through outcomes such as lower maintenance costs, reduced engineering rework, higher asset reliability and improved production.

As companies increase spending on artificial intelligence, automation and connected industrial systems, demonstrating those returns will become increasingly important for executives deciding whether to scale digital investments.

The companies that benefit most may therefore be those that combine digital technology with reliable data, clear business objectives and the organisational capacity to turn information into faster and better decisions.