The need to weave agility throughout an energy business

• 5 months ago (2024-07-16)
• Junior Isles

Renewables 780

---

John Craig Swartz , SVP of Risk360 at POWWR

Profitability within the energy sector hinges on three distinct pillars. The company's ability to manage risk, forecast accurately, and adapt swiftly. Unfortunately, the first pillar, to manage risk, can be exceedingly difficult for an energy supplier as each day is different from the last. Yet, the ramifications of not managing risk effectively are high. A minor misstep in risk management, caused by an inputting error or faulty data, can swiftly plunge an energy supplier into severe financial trouble or even lead to bankruptcy.

To help manage risk it is important that energy suppliers centralise all their disparate data silos throughout the business to ensure that there is complete transparency across their risk management teams and various executive stakeholders. Comprehensive data integration is crucial. It is important that it includes everything from demand forecasting and hedging to scheduling, daily settlements, churn analysis, and book valuation.

When it comes to accurate forecasting, the same is true. However, many energy suppliers are limited by manual, outdated tools that are no longer fit for purpose. This presents a real barrier. Without timely access to the requisite data, it is very difficult for any business to forecast accurately. This can lead to poor positions that will cause an energy supplier’s margins to shrink and can lead to higher prices for customers. Modernising these processes is vital to maintaining competitiveness.

With the energy market being more dynamic than ever before, the ability to swiftly adapt has never been more important for suppliers. Plus, customers are now far less brand loyal than ever before and willing to shop around. Whether it is changes to rules and regulations, unexpected weather events, or large-scale customer signup and churn, it is important that an energy supplier can quickly react so that they can buy the energy needed as efficiently as possible.

The need to be smart

To be truly agile requires intelligence and insight. Thankfully, the advent of smart meters has helped provide a window into what energy a customer is using, and when. However, as we know, the industry has been somewhat slow to move towards them, with global penetration currently languishing at just 43 %.

Other barriers to agility for suppliers have been financial. There still remains a credit crunch within the industry, with suppliers struggling to negotiate attractive lines of credit for the energy they need to buy. Energy suppliers need to, therefore, establish risk management policies and procedures internally. They must also understand the impact of risk measures such as Value at Risk (VaR) which can be used as part of the calculation to require cash and collateral from the supplier. It is worth the effort. Energy suppliers that use VaR as a daily risk measure can also show stakeholders that they have implemented a level of sophistication that is protecting the liquidity of the organisation.

The other main barrier seen is that of knowledge of what it truly takes to be able to compete. Many energy suppliers have been set up by brokers who unfortunately do not understand the nuances of being one.

Unlocking a pathway to future agility

Another growing barrier to agility is the difficulty in gaining the insight that is locked within the ever-growing data mountain. The widespread move towards renewables, though to be applauded, has led to more data than ever before. In fact, the industry is now thought to generate up to 200 exabytes of new data each and every year . With much of the data locked up within older, stand-alone software systems it is not a problem that is going to go away either. Something needs to change.

Thankfully, there is now technology available – underpinned by the latest artificial intelligence (AI) and machine learning (ML) capabilities – that can sift through this mountain of data quicker than ever before. Unlocking it and providing a pathway to future agility. One of the main benefits of AI and ML models is that they can be constantly retrained and are granular right down to the individual meter level. This makes them extremely accurate and circumvents the inaccuracies common with manual data entry.

For instance, during extreme weather events like the 2021 Uri storm in Texas, AI can quickly validate and extrapolate mountains of new data, enabling suppliers to manage such crises effectively and avoid catastrophic losses.

Making the right decisions, first time, every time

The global energy industry has never been more competitive. Yet, it has coincided with an era of unprecedented weather extremes. This, in turn, has led to unparalleled price volatility. This has made it a particularly challenging time for suppliers. With margins so tight, price too high and a supplier will be priced out of the market, price too low and they will lose money.

To stay competitive, energy suppliers must swiftly respond to market dynamics by embracing tools that are accessible from anywhere and rely upon automation to analyse a wealth of real-time data. This will ensure suppliers have the intelligence and insight to make the right decision, first time, every time.