How do energy and raw material traders act

Energy and raw materials trading 4.0

In order to be able to optimally use the potential of digitization, a regular assessment of the degree of maturity of individual technologies and the prioritization of individual use cases is required.

Blockchain, Robotics, Prescriptive Analytics, Deep Learning, Artificial Intelligence ...

For a long time now, these keywords should not be missing in any good discussion about digitization. For a long time, the nebulous desire of a company to go digital was offset by questions about use cases and implementation. But after several years of discussion, research and practical application, some of the technologies are already mature and established to be able to approach the energy and raw materials industry 4.0 in small steps.

In order to remain profitable in the very competitive and rapidly changing market, companies can no longer avoid digitalising their business model. But what exactly does digitization mean? We follow the common definition as "Optimization of processes and methods through extensive use of data, which is made possible by computer-aided user interfaces and powerful software".
What this can mean in detail for energy and raw materials trading is shown below using three specific application examples.

Applications of digitization

Robotic Process Automation (RPA) for back office processes
Due to the large number of systems and interfaces, back office processes in energy and raw materials trading companies offer considerable potential for optimization, as they are currently still very manual and time-consuming. The use of RPA in the form of so-called bots, i.e. programs that control other programs - or the new generation of "virtual colleagues" - can provide efficient remedies. These can run 24/7 in the background and, when predefined events occur, such as the arrival of an e-mail, automatically read data from the trading system, process data using Excel, write e-mails and much more. This creates the possibility of automating all repetitive process steps that can be mapped based on rules.

Popular candidates for using RPA are, for example, deal validation, i.e. the comparison of transactions between the trading system and deal confirmation, the validation of incoming payments and specific regulatory reporting processes. Other use cases are comparisons between portfolios and the general ledger, triggering payments when certain events occur or the automated posting of invoices after comparison with trade data. All of these processes must be carried out carefully and usually promptly. However, they are time-consuming and only add little value and are therefore ideal candidates for RPA, as employees can focus on value-adding activities after successful automation. In addition, setting up RPA is usually a "quick win" because it can be implemented quickly and the integration into the IT landscape is not invasive.

OTC trading and settlement via the blockchain
Even if the blockchain technology is on the descending branch of the "hype cycle", it is undisputed that it will be used extensively in the not so distant future in energy and raw materials trading - especially in trading between decentralized "prosumers". The first blockchain transaction between two energy companies was made at the end of 2017, made possible by the Enerchain project.

The advantages are the direct trade without a central intermediary and the automation of the processes that are necessary to map the sometimes very small "peer-to-peer" traded quantities in accordance with trade.

The blockchain could act as a “single source of truth” for the trading cycle, on which the transaction is entered using “smart contracts”, followed by an immediate deal validation, the mark-to-market calculation and the final settlement. It thus offers the potential to automate the many manual processes in retail and to streamline the currently often highly heterogeneous IT system landscape.

Due to the many open questions about performance, security and scalability, it is not yet possible to say whether this will ever happen - it is definitely conceivable.

Liquidity planning using predictive analytics
One of the challenges in liquidity planning in the energy and raw materials industry is the large number of potential factors that can have an impact on future liquidity (interest rates, payment histories, sales from direct marketing, electricity and raw material prices, open positions in margining, weather data Etc.). The use of predictive analytics methods to optimize and automate liquidity planning can provide an efficient remedy here.

The prerequisites for implementation are the provision of detailed historical values ​​(e.g. SAP Liquidity Analyzer) and an existing big data architecture (e.g. SAP Predictive Analytics for HANA). Once put on, the application possibilities are very diverse. In this way, not only future cash flows, but also, for example, the margins to be deposited or various risk and P&L KPIs, such as Cash-Flow-at-Risk (CFaR) or sales revenue, can be predicted. The definition of the data that is relevant for the calculation of individual key figures is one of the initial key tasks when using predictive analytics methods.

Many companies have already recognized the great potential. The current trend is from decentralized planning to automated, self-learning extrapolation models and growing investments in the use of internal and external data sources to improve forecasting.

Intraday forecast and algo trading
Both the short-term optimization of the intraday load forecasts in order to avoid balancing energy and the resulting quarter-hour trading cannot be achieved without the use of digital technologies. The use of information that is only available at short notice for the purpose of deriving the corrections for the day-ahead forecast requires a quick link between the data from various sources and self-learning algorithms for pattern recognition. The immediate implementation in many small-scale buy and sell orders makes an automated trading system indispensable. The competition with market participants who are already using these possibilities, as well as the constant pressure to achieve efficiency, lead to an increasing demand for these solutions by energy trading units and thus to an increasing supply from system manufacturers.


set priorities


But what is the best place to start when it comes to digitization, or for the digitization pioneers, how do you proceed? Since with the large and steadily growing number of keywords related to the topic, the overview and, above all, the focus on the important things can quickly be lost, it is necessary to initially assess the individual applications and technologies. To do this, one compares the current level of maturity of individual technologies with the potential benefits in one's own energy and raw material trading and can thus quickly identify which use cases to ignore (low level of benefit and level of maturity), observe (high level of benefit and low level of maturity), research (high level of maturity and low level of benefit ) and are to be implemented (high benefit and degree of maturity).

In this context, however, it should always be checked which options the current system landscape already offers without being used sufficiently. In addition, it must always be checked to what extent further development of methods and processes is a necessary precondition for the implementation of digital solutions.

For a medium-sized municipal utility, for example, it can happen that

- Robotic process automation for back office processes implemented immediately,
- actively researching the topic of OTC trading and settlement via the blockchain,
- Initially, only observing the planning of cash flows using predictive analytics,
- The existing risk reporting is made more flexible and using the databases directly instead of time-consuming processing of numbers in Excel
- the long existing interface for the automated booking of commercial transactions should be activated at the expense of the manual booking, which is still retained, on the basis of inventory lists from the ETRM.

The matrix of all possible applications and technologies resulting from the work step should represent the basis for strategic discussions around the roadmap for energy and raw materials trading 4.0 and should be constantly updated.


Conclusion


The digital transformation is in full swing. The pioneers in the financial sector have proven that the search for meaningful applications and perseverance in implementation pay off strategically and economically.

Companies in the energy and raw materials sector are also catching up. Established technologies, such as increasingly sophisticated algorithmic trading systems for the intraday market or inexpensive sensors for measuring the need for maintenance of power plants and wind farms (keyword predictive maintenance) are already being actively researched and implemented by many companies. In addition to increasing sales and efficiency, they report improved customer loyalty and the opportunity to open up new markets. The regularly emerging and promising technologies show that we are only at the beginning of digitization and that it will remain an exciting journey.

Guest contribution by Robert Morys, Manager, Finance Advisory, [email protected]