Many people working with energy commodities will use an ETRM or know this type of software well. Fewer recognise the name Commodity Management, but it is now an established part of the commodity technology landscape. It is the more modern approach, and in the age of AI, often the only realistic way to manage all your trading and operations data in a way that AI can reliably act on.
Commodity Management Systems grew out of the ETRM. As ETRM vendors added functionality to support the entire physical supply chain, a software category developed. Commodity Management incorporates the functions of an ETRM, but does far more as well, extending to counterparty management, logistics, and inventory management. That wider scope matters even more now that companies are adding AI to their technology stacks. AI delivers value when it can work from all of your data, but an ETRM alone only holds part of it.
What Is ETRM?
ETRM stands for Energy Trading and Risk Management, and is a type of software that supports the business processes needed to trade energy commodities. ETRM software helps companies manage volatile commodity prices across physical and financial contracts, including supporting deal capture and price management, risk reporting, valuation and regulatory reporting.
ETRM systems were originally designed to manage a somewhat limited range of financial risks, but have evolved over time to provide a wider variety of risk management and better data. The goal of a good ETRM is to become a source of data for better commercial decision-making.
ETRM and Operational Risk
In practice, ETRM systems have historically struggled to capture everything that happens in the business. Many organisations using legacy ETRM systems have therefore found themselves managing parts of their processes using generic tools such as spreadsheets, and only updating the ETRM with what has occurred after the fact.
This leaves businesses open to a wide range of risks. Accidentally overwriting formulas, changing the wrong cell value or using multiple versions of the same spreadsheet have all resulted in material losses for organisations managing their trading workflows in this way. And even when the spreadsheet is used with no mistakes, there is still the time taken out of busy traders’ and operators’ days to update the ETRM as well as carrying out the actual work, with all the attendant operational risk of copying information between systems.
There is a further cost. Because the ETRM is updated after actions are taken, its data never reflects the live situation in the business. Risk and commercial decisions are made on incomplete data, or delayed until a report is finished at the end of the day, week, or month. With energy markets becoming increasingly volatile and fast-moving, organisations have been demanding better from their software.
The Data Problem for AI
The data gaps become even more of an issue when companies add AI into the mix. An AI system can only reason about what it can see. If contracts sit in the ETRM, shipment details in a logistics spreadsheet, inspection results in email, and inventory in the ERP, the AI sees fragments, and its answers will reflect those fragments.
For AI to deliver value, all of this data needs to be in one place, structured, and kept up to date as things change in the real world. That single, connected data set is the structural foundation for effective AI.
The big risk is that AI rarely tells you when it does not have the full picture. It can give a confident answer without having seen the latest allocations, or while relying on information that is out of date. A position summary that misses a contract looks like one that includes it, but it means that somebody is making a decision without all the facts, and without knowing it.
From ETRM to Commodity Management
The way to close that data gap is to bring the work into one system, and that is where Commodity Management comes in. Many ETRM vendors have improved their systems in response to the market's need for better management of the commodity supply chain, and Commodity Management software is the result. Good ETRM systems manage market and financial risk well, but Commodity Management also addresses many of the causes of operational risk, because the work happens in the system rather than around it. That keeps the data your AI relies on complete and current.
What Is Commodity Management Software?
Commodity Management software handles a much broader range of commodity trading, procurement, and processing workflows than an ETRM. It builds on features that support physical trading, so that every aspect of contracts, logistics, and commodity processing is managed in one system. This is the approach behind CommOS, Gen10's Commodity Management System.
For example, operators can manage all of their logistics in the system, including allocating stock to contracts and shipments, managing multimodal transport, and recording inspections. They can blend or split lots and convert between weights and volumes, all in the same system that instantly updates trading and risk teams as things change.
Traders and risk managers can also carry out every aspect of their role in the system. Traders capture deals, work out pricing strategies with automated calculations, and create contracts in one place, without copying information or manually updating risk reports after the fact. Because risk managers use the system and the general ledger is integrated, traders can check credit lines and pre-approvals in real time without slowing down.
The shared live information also lets risk managers adapt to changes throughout the day. A Commodity Management System gives them full business visibility at all times. That means smarter decisions, more accurate hedging, and better protection of profitability.
Commodity Management is not only an advantage for trading firms. Power generators and their fuel procurement teams benefit too. CommOS is built for interoperability, so contract and logistics data can flow to an ERP. It makes it easier and faster to manage inbound fuel shipments and storage, to record sustainability data including lifecycle carbon dioxide emissions, and, for biomass procurement, to manage inspections, assays, and burn rates for each shipment.
The Benefits of Commodity Management
By integrating logistics, trading, risk, and compliance, Commodity Management Systems benefit each of these teams and the wider organisation. Whatever your position in the supply chain, improvements in traceability, compliance, and business agility are among the results you can expect.
Good Commodity Management Systems are also usually hosted in the Cloud, which typically brings greater flexibility, the ability to customise the solution to your own workflows, faster implementation, and better access to data anywhere and on any device.
And the biggest benefit now is the foundation it creates for AI. Because all processes are carried out in one system and data is shared across the organisation instantly, Commodity Management provides the single source of data that AI needs to be useful.
And if you want AI to take actions, such as creating a contract, it needs to operate directly within the system of record. The more business processes that already sit in one connected system, the more actions AI can take, and the more benefit you see. With contracts, logistics, quality, pricing, and risk held together, AI can draft a contract from the text of an email, show which counterparties are close to their credit limits, or answer a question about your position, working from the full picture.
Where to Start
Start by looking at where your trading and operations data lives today. Contracts, shipments, inventory, quality, and risk all need to be in one place before AI can use them well. Every process you bring into one connected system adds to what your AI can see, and to what it can do.
Discover the energy-specific functionality in Gen10's Commodity Management software.