Nicola Barton - Consultant, MEng Chemical Engineering at Bath, joined (full time) 2 years ago
Coming from a STEM background, I applied to EFC for my one-year industry placement because I was interested in finding a job that allowed me to apply the critical thinking and quantitative skills I’d developed throughout my degree to a different context. At the end of my placement, FTI offered me a full-time job and so I came back to EFC as a graduate after completing the final year of my degree.
I was attracted to EFC compared to other types of consulting because of the analytical nature of its work and the opportunity to think deeply and carefully about the correct approach to tackle complex problems.
I did not study finance or economics but EFC has been supportive in helping me learn and develop the necessary knowledge by providing ongoing training and, since I’ve been back, sponsoring me through the ACA qualification.
During my time at EFC I have worked on projects across a wide range of industries, from clean energy to commodities and agriculture. Each project has presented new challenges as there is never a one-size-fits-all approach, so I have been able to build on a variety of skills. For example, on some projects I have built detailed financial models in order to value companies, while on others I have learned how to review licensing agreements. In one of my cases, I analysed agreements between the largest players in the mobile phone industry to estimate what handset manufacturers should pay to use the technology behind 4G and 5G.
I really like that the project teams are relatively small – generally only three to six people. I have worked with a different group of people on each project. This has meant that I have worked closely with people at a range of levels and had the opportunity to learn new ways of working from each team.
Project: Assessing losses in a dispute relating to wind turbine components.
Background
Our client was a wind farm operator claiming to have been provided with faulty turbines by their supplier. The supply of faulty turbines meant that they had not been able to generate as much electricity as they had originally projected. As a result, they were claiming compensation for not being able to generate and sell as much electricity.
We worked alongside a team of FTI’s clean energy experts and external wind engineers, who provided us with information about how well the turbines were working relative to original expectations. To make matters more complicated, one of the turbines burned down while we were working on the project! This meant that the engineers were unable to carry out a physical examination of the turbine to determine how it was actually operating, therefore increasing the uncertainty of our valuation and requiring us to think about appropriate assumptions.
To determine our client’s losses, we forecast how much money the wind farm would make over the next 20 years, based on the actual performance of the turbines. We then compared this to a forecast assuming that the turbines perform as originally expected. We then used the discounted cash flow approach, which accounts for the riskiness of those future payments and the time value of money, to convert this into a lump sum that our client was owed by the supplier to cover the future losses.
My role
When I began working on the project, there were only three team members. This meant that I was able to take responsibility for analysing the data we were provided and building our valuation model in Excel, in line with the conceptual approach determined by the more senior team members and industry experts.
We had already received a report from the experts employed by the turbine manufacturer which described what they thought the damages should be. My first task was to review and critically evaluate the analysis conducted by the opposing experts to determine whether we agreed with their approach and their assumptions, and whether they had made any errors.
Next, I worked on building our own financial model. We worked closely with the clean energy experts to come up with reasonable assumptions to feed into our discount rate calculation. As a cross-check to our model, we also considered the performance of similar companies in the industry. To do this, I used Capital IQ (a provider of financial data) to identify similar companies in the wind industry. I then analysed their performance to check that our forecasts were reasonable.