Supply Chain Traceability with Digital Twins

Predictive Maintenance and Operational Efficiency 

Beyond tracking emissions, digital twins act as virtual testing grounds for your equipment and processes. They continuously compare sensor readings against expected performance models.  
 
Over time, they learn the normal patterns of a facility – how a gasifier hums at peak output or how a solvent recycler behaves as throughput varies. When something begins to shift from expected patterns, the digital twin can detect the change early, often before it’s visible through routine monitoring. 

This is the foundation of predictive maintenance. Consider a resource recovery plant where pumps circulate chemicals through a recycling system. If the pump’s energy draw creeps above its usual baseline or its output flow drops off slightly, the digital twin’s analytics might recognise an early sign of wear or fouling. It can alert engineers to schedule maintenance at the next convenient window.  

The benefit is twofold: avoiding unplanned downtime (which in a continuous process could cause safety incidents or costly shutdowns) and avoiding unnecessary maintenance (servicing equipment only when data indicates it's needed). 

According to Gartner research, leveraging digital twins can cut maintenance costs and boost overall productivity by reducing downtime. In an industry where uptime and efficiency directly impact output volumes and revenue, this improvement is significant. 

Digital twins also drive operational efficiency by enabling scenario testing and optimisation. Because the twin mirrors the real process, engineers can simulate adjustments in a risk-free virtual environment. What if a waste-to-fuel plant could try a different process temperature or catalyst concentration in the twin to see how it affects yields and emissions? Instead of guesswork, the twin would show the likely outcome (e.g., a 5% increase in throughput but a slight rise in energy use). The team can then decide the best course of action based on data, implementing changes in the real plant that have been vetted in silico. 

For companies in emerging green industries, these efficiencies can make the difference in scaling up profitably. It allows them to squeeze more output and reliability from the same infrastructure. Essentially, digital twins act as both a watchdog and a coach: preventing problems and constantly suggesting how to run a bit leaner, cleaner, and smarter. 

Supply Chain Optimisation and Carbon Intensity Optimisation

Your operations don’t happen in isolation — they are part of broader supply chains involving feedstock inputs, transportation, and distribution of outputs. Here too, digital twins can play a transformative role by extending visibility across the supply chain and optimising each link for sustainability and efficiency. 

Take a biodiesel producer as an example. They rely on feedstocks like used cooking oil or tallow collected from various sources, and they distribute their biodiesel to blending facilities or fueling stations. A digital twin of the production process can be expanded to model the supply chain: how feedstock moves from collection points to the refinery, how long it’s stored, how the refining process yields vary with feedstock quality, and how the final product is delivered.  
 
By simulating this chain, the company might discover, for instance, that transporting feedstock in smaller batches more frequently reduces spoilage and emissions from decomposition, or that coordinating deliveries to minimise truck idling time at the plant significantly cuts fuel use. 

For a resource recovery company dealing with hazardous waste or recyclable chemicals, supply chain efficiency is equally critical. Materials might come from multiple industrial clients and need segregation, processing, and then either disposal or sale as recycled product. A digital twin can track each batch from origin to final output. This ensures full visibility — if a certain batch of solvent has higher contamination, the twin can account for the extra steps or energy needed to treat it, and help plan the optimal processing order to avoid cross-contamination or process slowdowns. 

The point is: optimising the supply chains with a digital twin often leads to reduced energy consumption and waste. Companies can model inventory levels to ensure resources (like a buffer tank of biogas or a stockpile of waste plastic) are at optimal levels – not so low that operations starve, but not so high that materials degrade or capital is tied up unnecessarily.  
They can also forecast demand and supply fluctuations more accurately.  

Another supply chain aspect is collaboration and data-sharing. Digital twins can serve as a common data platform between partners, for example, a green hydrogen producer and the utility that buys their hydrogen could share certain twin data. This could optimise the scheduling of hydrogen production to match renewable electricity availability, benefiting both sides. Such integration fosters a more resilient supply chain that responds dynamically to real conditions (like a sudden renewable energy surplus or a disruption in feedstock supply due to a weather event). 

Ultimately, by optimising the entire value chain, organisations cut their own emissions and costs while providing a more reliable service to customers. They can guarantee delivery times, consistent quality, and transparent reporting of the product’s carbon footprint at each stage. In markets that increasingly value sustainability, that supply chain transparency becomes a competitive differentiator. 
 

Compliance, Reporting and Digital Certificates for SAF, Biofuels and Recycling

Regulatory compliance is a fact of life across the green economy, and it’s only getting more demanding. 
 
Whether you're producing SAF, refining biofuels, recycling industrial chemicals, or recovering materials, your business must document sustainability metrics like carbon intensity, emissions, and material traceability to qualify for incentives, meet certification standards, and access regulated or premium markets. 

Doing this with spreadsheets and paper logs is time-consuming, error-prone, and out of step with the real-time nature of modern operations. 

Digital twins simplify and strengthen compliance by automating data collection and reporting. Since the twin is already aggregating operational data, across emissions tracking, process monitoring, and supply chain flows, compiling a report becomes as easy as querying the platform. Instead of spending days gathering logs and reconciling multiple systems, teams can generate audit-ready reports in seconds, complete with timestamps, operating conditions, and verifiable data sources. 

Because the data is traceable and securely stored, audits become far less painful. If a regulator, buyer, or certification body needs proof — say, total CO₂ per unit of product, or verification that a batch was sustainably produced — the digital twin can provide it instantly. Every data point is backed by secure traceability.  

For example, Carbon Central can align with ISCC Plus, capturing all required data for sustainable product claims. A company recycling chemicals or producing SAF can easily show a full chain-of-custody, along with the carbon impact of each batch, simply by pulling up a tokenised certificate. 

Beyond compliance, this builds business advantage. 
 
With robust digital reporting, organisations can confidently pursue certifications like ISCC+, ISO 14001, or pathway approvals for sustainable fuels, unlocking access to new markets and customers that demand credible sustainability data. 

And as regulations evolve, digital twins help you stay ready. If tomorrow’s rules require reporting water usage, Scope 3 emissions, or circularity data, you’re already capturing it in your operational twin. 

In short, digital twins turn compliance from a burden into a strategic asset. 
They reduce admin work, improve accuracy, and build the transparency that today’s investors, regulators, and customers increasingly expect.