Enhancing Enterprise Risk Management in a complex energy company
Client: A major European energy company
Challenge to be solved: Insufficient risk analysis in the face of major investments
Starting point
For many companies, risk analysis happens in silos. Each business unit builds its own forecasts, runs a few scenarios, and reports up, but nobody truly sees how the whole system behaves under stress. That kind of an approach may work in a simple business, but in today’s interconnected energy markets it can be dangerously incomplete.
We recently worked with a European energy company that faced exactly this challenge. The group operated across electricity generation, district heating and cooling, and electricity sales and networks. Their production portfolio was diverse from renewables to thermal assets and heat pumps, and the company was weighing major new investments that could reshape both their asset base and capital structure.
Each investment carried significant implications. Some would increase production capacity but reduce demand for other assets. Others would add new revenue streams but require large amounts of debt. Because power prices acted simultaneously as a cost driver for heating and a revenue driver for generation, assessing the total group risk was a multidimensional puzzle.
Our solution
Our starting point was simple: whatever the company decides, it should never endanger its long-term viability. But since nobody can forecast the future with certainty, we shifted focus from prediction to resilience.
Instead of trying to guess what the market will do, we built hundreds of market scenarios to represent plausible futures to reflect uncertainty in prices, regulation, and technology development.
Next, we developed a so-called financial twin of the company – a model that linked all business units, balance sheet items, and key financial ratios such as EBITDA, Net Debt/EBITDA, and cash flow. This allowed us to simulate the entire group under each market scenario and for every investment combination under consideration.
The end result
Our approach to the challenge transformed risk management into foresight. Instead of static sensitivity tables, management could now see full distributions of outcomes: what profitability, leverage, or liquidity might look like in good years and in bad.
For example, we found that while median leverage levels were acceptable, certain downside scenarios pushed Net Debt/EBITDA above thresholds that would be considered risky. We then traced which market conditions caused these outcomes and identified possible mitigations including hedging, adjusted financing structures, or rephasing investments.
Enterprise Risk Management (ERM) isn’t about eliminating uncertainty. It’s about understanding how your system behaves when the world moves in unexpected ways. Scenario-based modelling and financial twins help leadership teams stress-test strategy, not just budgets.
In a capital-intensive industry, that can be the difference between hoping for the best — and being ready for it.
The methods used
- Risk analysis
- Scenario-based modelling
- Developing a financial twin of the business
- Market scenario simulations
The benefits achieved
- Clear view of full outcome distributions (profitability, leverage, liquidity, etc.)
- Stress-testing strategy
- Transforming risk management into foresight
- Identifying possible mitigations to outcomes
