17. Recommendations to SupraChem

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In making our recommendations to SupraChem, it's important not jump to the final analysis too early and to recognise other considerations outside of your model and scope of work.

Lesson Notes

Tips for presenting recommendations

- Don't jump to final analysis too quickly
- This is likely to confuse or mislead the audience
- Instead start from the problem statement and tell the story step-by-step

Understanding investment recommendations

- Investments are often neither good or bad, but dependent on the investor's perspective
- Decisions are often influenced by factors outside your control
- Always highlight the risk level of an investment before applying risk-weightings
- Always highlight the limitations of your analysis


In the first lesson of this course, SupraChem's executives stated that they required a return of five times the original invested capital over 10 years to justify proceeding with the project. With scenario modeling, risk analysis, and tornado checks now complete, it's very tempting to jump straight to our final conclusions. For example, we might want to show our risk-weighted returns under each scenario, showing that for the base case and the optimistic case, that we exceed the five times threshold. We might also want to show the sources of risk that could push our base case underneath our target return.

However, when presenting recommendations to a client, jumping to the end of your analysis can be at best, confusing, and at worst, misleading. From the previous chart, an executive might quickly conclude that if the project wasn't a success, the company would only lose 24% of its investment. This is quite a reasonable conclusion, but because you haven't explained how risk-weighted returns work, it's also totally wrong. A better strategy is to start from the beginning with a diagram that steps through the various stages of investment. The first step would obviously be the $15 million investment, and the likelihood is that this project will not be a success. In fact, it is an 85% chance of failure. And if the project does fail, we simply have an investment loss of $15 million.

If the project is a success, then we invest an additional $230 million to build a commercial plant, and we receive the operating cashflows from that completed plant, and these cashflows are substantial.

If we take a look at the returns if the reference plant is successful, we can see that even in the pessimistic case, they're much greater than our five times return target. To obtain these figures, I simply set the probability of success input in my model to 100%. At this stage, the company's executives clearly understand the binary nature of this investment. They will most likely lose all of the $15 million, but there's a small chance they will make a huge return. This investment is a tricky one to analyze because its attractiveness depends hugely on the investor in question. A company that would invest in this project needs to be comfortable holding high-risk projects in its portfolio. It would also need to have strong cashflows, first to be willing to write off the $15 million, and second, to build a commercial plant if the reference plant was successful. An investing company might also be under pressure to find new opportunities for growth, and excited by the technological advances and learnings that can be transferred to other divisions from this project.

On the other hand, a company that would not invest in this project might be very risk-averse and simply does not tolerate losses easily. Another reason it might not invest is due to cashflow constraints, and it simply couldn't commit to funding the expensive commercial plant in two years' time. Alternatively, the declining company might have other R&D projects that have more potential that we as a consultant don't know about, or the company may still not be comfortable with the risks associated with the project, and want evidence that the investment hurdle rate has been met. If the final two points were raised by the executives in the region, we could show our risk-weighted returns analysis and the fact that the base case and optimistic case exceed the five times return target.

Our risk analysis would also highlight the variables that could push our investment returns below the threshold.

Armed with these insights, the company should be much closer to a final decision on whether to invest. But before they make that decision, we'll highlight the limitations of our model in the next lesson.