1. What's New in Forward-Looking Models

Subtitles Enabled
Replay Lesson

Next lesson: Decision Trees

Watch next lesson

Forward-Looking Models

18 lessons , 4 exercises , 1 exam

Start Course


This lesson explains the characteristics and challenges of building forward looking models, and introduces the SupraChem case.

Lesson Notes

Forward-looking models

- Try to predict the future based on a set of assumptions
- Can provide better insights into future performance
- Investment decisions almost always require forward looking models

Weaknesses of forward-looking models

- Highly dependent on quality of assumptions
- Can deviate substantially from reality - particularly in later years


The reason we build models is to help us make better decisions. Sometimes we do this by analyzing historical data for insights and other times we try to predict the future with forward-looking models. The first modeling in case of Zippy Airways examined historical flight information to see if an overbooking policy could deliver additional profitability. In this course, our case study SupraChem will need a forward-looking model to help the company make an investment decision on a new technology. So why build forward-looking models? Well unsurprisingly, forward-looking models can provide better insights on the future than models based on historic data. Indeed forward-looking models typically utilize historical data when developing their projection assumptions. Certain types of decisions simply require forward-looking models. In business, many of the decisions management make are investment decisions. When you make an investment, forward-looking models are essential because you'll want to know how the money will be spent, what returns you will get on your investment and when you will realize these returns. Forward-looking models do come with a big health warning, however. Firstly, they require a lot more assumptions often many years into the future. As a result, forward-looking models have the potential to deviate hugely from reality. When presenting to a client or to superiors, you should always be acutely aware of the accuracy of your assumptions and qualify your recommendations accordingly. In this course, we'll introduce a number of new concepts and tools for forward-looking models. This includes decision trees, tornado charts, scenario analysis and much more. With that introduction to forward-looking models out of the way, let's introduce the case. Our client SupraChem is a large chemical manufacturer in the US. For the past five years, a team of engineers have been working on a new renewable fuel product. This is a new market for SupraChem outside their core competency and the executive team are anxious and excited by the opportunity. The engineering team have built a test plant that currently produces the renewable fuel at $7 dollars per gallon. The market price however is currently $2.80 per gallon. The next phase of the project, a larger reference plant will incorporate new technology and confirm the exact production cost that Supra could hope to achieve at commercial scale. This is a high-risk investment. The reference plant has only a 15 percent chance of succeeding and hitting the target price of $1.80 per gallon. There is an 85 percent chance that the reference plant will be written off as a bad investment. However, if the reference plant is a success, the subsequent commercial plant will be able to generate 200 million gallons per year and the potential revenues are huge. So the question is, should SupraChem proceed with the reference plant? To help us get started, I've included some additional numbers with regard to the project. I suggest downloading the file beneath this video and keeping this slide at hand as we walk through the model. In contrast to our previous case Zippy Airways, there's much more at stake in this decision. The investment numbers are larger, the risks are larger, particularly because this is a go, no-go decision. While Zippy Airways could tweak their overbooking policy over time and adjust the model with new data, this investment once committed cannot be undone. In companies, investments such as these can make or break careers, so it's critical that excellent analysis is conducted before making a recommendation. Let's get started in the next lesson with diagraming the problem.