Digital Twin enabling the green transition

What is the green transition in manufacturing?

The green transition in manufacturing is the shift toward more sustainable production by reducing environmental impact while maintaining productivity, competitiveness, and operational performance. For industrial companies, this means using energy, materials, equipment, and other resources more efficiently while reducing waste and carbon emissions across production processes.

A key part of sustainable manufacturing is improving both energy efficiency and resource efficiency. Manufacturers can achieve this by identifying unnecessary energy consumption, optimizing production processes, improving material utilization, and reducing waste. These measures can lower the environmental footprint of industrial operations while also helping companies control manufacturing costs.

The green transition also supports broader goals such as decarbonization and the circular economy. Instead of focusing only on increasing output, manufacturers increasingly need to understand how production decisions affect energy use, material consumption, emissions, and overall environmental performance throughout the operational lifecycle.

Achieving these goals requires reliable information about what is happening in production. By combining operational data with better visibility into equipment, processes, and resource consumption, manufacturers can identify inefficiencies and make more informed decisions about where sustainability improvements can have the greatest impact.

How digital twins support the green transition

A digital twin connects operational data with a virtual representation of a physical asset, production line, process, or facility. This gives manufacturers a clearer view of how their operations perform and how different factors, such as energy consumption, equipment condition, and resource use, affect overall efficiency.

In manufacturing, digital twins can bring together data from machines, sensors, automation systems, and other industrial data sources into a shared digital environment. This makes it easier to monitor production, identify inefficiencies, and understand how operational changes may affect both production performance and environmental impact.

By using real-time and historical data, manufacturers can analyze how resources are consumed and identify opportunities to improve energy efficiency, reduce material waste, optimize processes, and support lower carbon emissions. Digital twins can also help evaluate different operating scenarios before changes are implemented in the physical production environment.

This connection between operational data and the physical production environment makes digital twins an important tool for sustainable manufacturing. Instead of treating sustainability as a separate initiative, manufacturers can incorporate environmental and resource-efficiency considerations into everyday operational decision-making.

The role of digital twins becomes particularly valuable as manufacturing environments grow more connected and data-driven. Their applications range from monitoring individual assets to improving the performance of entire production systems. These applications and their benefits are explored in more detail in our article on digital twins in manufacturing.

Improving energy efficiency with digital twins

Energy efficiency is an important part of sustainable manufacturing, particularly in production environments where machines, production lines, and supporting systems consume significant amounts of energy. A digital twin can help manufacturers understand how energy is being used across operations by combining energy-related information with other operational data.

This makes it possible to examine energy consumption in the context of production rather than as an isolated metric. Manufacturers can compare energy usage with production volumes, equipment performance, operating conditions, and other process data to identify where energy is being used inefficiently.

Digital twins can also support the optimization of energy-intensive processes. By analyzing historical and current operating data, manufacturers can identify patterns, compare operating conditions, and evaluate opportunities to improve production efficiency without compromising performance or output.

Monitoring energy consumption in real time

Real-time monitoring gives manufacturers greater visibility into how energy consumption changes during production. When energy data is combined with information from machines and industrial systems, unusual consumption patterns or inefficient operating conditions can be identified more quickly.

This is especially useful when changes in equipment performance lead to increased energy use. A machine operating outside its normal parameters, for example, may consume more energy while delivering the same or lower production output. Making these relationships visible helps operators identify where further investigation or optimization may be needed.

Real-time energy monitoring can form part of a broader industrial monitoring approach in which equipment condition, process performance, production data, and resource consumption are viewed together. This gives manufacturers a more complete understanding of production performance and supports more informed decisions about energy efficiency.

Reducing material consumption and production waste

Improving material efficiency is another important part of the green transition in manufacturing. Digital twins can help manufacturers understand how materials move through production processes and where unnecessary resource consumption, losses, or waste occur.

By combining information about production volumes, process conditions, equipment performance, and material flows, manufacturers can gain better visibility into how efficiently raw materials and other resources are being used. This can help identify process stages where excessive material consumption, scrap, or other forms of production waste are generated.

Digital twins can also support process optimization by making it easier to compare operating conditions and understand how changes in production parameters affect material use. With better insight into these relationships, manufacturers can identify opportunities for waste reduction and improve overall resource utilization without compromising production quality or performance.

Using materials more efficiently has both environmental and economic benefits. Lower resource consumption means less waste and can also contribute to lower manufacturing costs, particularly when improvements can be achieved across frequently repeated or resource-intensive production processes.

Better material utilization can also support circular economy principles by helping manufacturers reduce unnecessary resource use and make production processes more efficient. Together with improved energy efficiency, these measures help reduce the environmental impact of manufacturing while supporting more sustainable industrial operations.

Reducing carbon emissions through process optimization

Reducing carbon emissions in manufacturing requires a clear understanding of where energy and resources are consumed and how different production processes contribute to the overall environmental impact. Digital twins can support this work by bringing operational information together and helping manufacturers identify processes or operating conditions with potential for improvement.

Through process optimization, manufacturers can analyze how changes in production parameters, equipment operation, energy consumption, and resource utilization may affect production performance. This makes it possible to identify opportunities where operational improvements can contribute to emissions reduction while maintaining required levels of productivity and quality.

Digital twins can also support more effective energy management by providing visibility into how energy consumption changes across equipment, processes, and operating conditions. Historical and real-time data can help manufacturers recognize inefficient patterns and evaluate where adjustments could reduce unnecessary energy use and associated emissions.

Importantly, a digital twin does not reduce emissions on its own. Instead, it provides manufacturers with data, visibility, and analytical capabilities that can support operational changes aimed at reducing their carbon footprint. This makes digital twins a useful tool for companies working toward broader decarbonization goals and improved environmental performance.

Using real-time data to monitor sustainability performance

Improving sustainability performance requires more than setting environmental targets. Manufacturers also need reliable information about how production is performing in practice. By combining real-time data with historical operational data, digital twins can provide continuous visibility into energy use, resource consumption, equipment performance, and other factors that influence environmental performance.

This makes performance monitoring more closely connected to everyday production activities. Instead of evaluating sustainability only through periodic reports, manufacturers can use current production data to identify changes, detect inefficiencies, and make more informed data-driven decisions.

Sustainability KPIs and data visualization

Relevant sustainability KPIs can be monitored alongside operational and production metrics. Depending on the process and available data, these may include energy consumption, resource utilization, material waste, emissions-related indicators, or other measures of environmental performance.

Effective data visualization makes these indicators easier to understand and use. Presenting sustainability data in the context of production helps operators, managers, and other stakeholders see how environmental performance relates to equipment conditions and process performance, and where improvement opportunities may exist.

Combining data from different industrial systems

Sustainability-related information is often distributed across machines, sensors, automation systems, production systems, and other industrial data sources. Data integration makes it possible to bring this information together and examine relationships that may not be visible when individual systems are monitored separately.

Once the data is connected, manufacturers can use industrial data analytics to identify patterns, compare performance over time, and turn large volumes of operational and sustainability data into actionable information.

Combining data from different industrial systems within a shared digital environment can therefore provide a more complete view of both production and sustainability performance. This helps manufacturers move from isolated measurements toward continuous, data-driven improvement of industrial operations.

Digital twins enable data-driven sustainability decisions

Digital twins can support data-driven decision-making by giving manufacturers a more complete view of how production processes, equipment, resources, and environmental factors interact. Instead of relying only on isolated measurements or historical reports, decision-makers can use operational data to better understand current conditions and evaluate where improvements may have the greatest impact.

Through scenario analysis and simulation, manufacturers can explore different operating alternatives before making changes in the physical production environment. For example, they can evaluate how adjustments to production parameters, equipment utilization, or resource consumption could influence operational efficiency and environmental performance.

Historical and real-time information can also support forecasting by helping manufacturers recognize patterns and anticipate how changes in operating conditions may affect future performance. This provides a stronger basis for planning improvement actions and identifying potential inefficiencies before they become more significant.

By combining these capabilities, digital twins can support continuous optimization of industrial operations. Manufacturers can compare alternatives, prioritize improvement actions, and make decisions based on their potential impact on productivity, resource use, energy consumption, and overall environmental impact.

I also wouldn’t force a different internal link into this section just because we want links. No link is needed here. The section’s job is to deepen the semantic coverage of the Green Transition article, while the earlier sections already establish the appropriate connections to your other articles.

How predictive maintenance supports sustainable manufacturing

Predictive maintenance can support sustainable manufacturing by helping companies maintain equipment based on its actual condition and performance rather than relying only on fixed maintenance schedules or reacting to unexpected failures.

By using operational and equipment data to identify changes in performance, manufacturers can detect potential problems earlier and plan maintenance activities more effectively. This can help reduce unplanned downtime and improve asset utilization by keeping equipment operating under appropriate conditions.

Better insight into equipment condition can also help avoid unnecessary maintenance activities and premature replacement of components. When assets and components can be used effectively for longer, manufacturers may reduce the consumption of spare parts, materials, and other resources associated with maintenance.

Predictive maintenance can therefore contribute to both operational efficiency and sustainability goals. Fewer unexpected failures, more efficient maintenance activities, and better use of existing assets can help manufacturers reduce unnecessary resource consumption while supporting reliable production.

The section intentionally stays relatively short because the detailed explanation of methods and benefits belongs on the dedicated Predictive Maintenance page.

UKK