One of the easiest ways to think about how an industry might develop in future is to look across unrelated industries and see how developments and techniques common there could be brought over and adopted.

Business fundamentals are relatively constant across most industries, but approaches, metrics and monitoring typically vary quite significantly, so seeing how different groups of people solve the same fundemental problems can be a good source of inspiration.

For example, if we consider the investment industry and the problem of choosing appropriate investments, it would be common sense for analysts to factor in not only the average expected return of a given investments, but also things like the expected uncertainty of the return as well as the range of potential returns and how likely each was to occur.

In fact, we can go further than this and say that an investment firm that didn't consider these aspects would generally be considered to be failing in it's obligations and analyses.

Yet, in almost every other industry we work with, we see that the vast majority of work completely ignores the potential variation of outcomes that can result from a given business decision, with most analyses simply stating the 'most likely' outcome.

However, unlike the investment industry, this can't be considered a weakness of the teams performing this work! When we discuss this challenge with analytics teams, the teams will almost inevitably be aware of this risk, and this awareness often runs right through the organisation, from the newest graduate to the most seasoned board member.

In our experience, the fundemental cause of this weakness is that there simply aren't tools readily available to analyst teams that allow them to calculate these metrics. This unfortunately places the team in a situation where they either need to ignore the issue and 'hope for the best', or recognise the issue and manually do double or triple the work.

Since at least some of their competitors will choose to ignore the issue, companies that do double or triple the work will struggle to be as agile and ultimately are likely to fall behind in performance.

So, we end up in a situation where everyone from the newest graduate to the most seasoned board member recognises a problem, but no ready solution exists!

This is a situation we hope to help our customers with in the coming years.

Our Portfolio Management Engine is able to produce not only the expected outcome of any analysis, but also the spread of possible outcomes around this analysis, and the customisable code area enables larger business risks to be easily modelled and subsequently included in all future analyses.

If companies use these features in a disciplined and regular way, this can allow a much greater understanding of risks to permeate across the entire business. It will become possible for every decision maker in the business to suggest strategies based not only on the most likely outcome, but with full awareness of any potential negative scenarios, as well as any potential additional upside.

For example, it may be that a product is currently written to a given profit margin and the expectation is that in ~90% of years this product will achieve a margin +/-2% of the target. Historically, if a potential change was identified that improved this margin without impacting sales volume, then this change would always have been implemented.

However, by using the Quill Systems scenario modelling tools your team would be able to identify that this change, whilst improving the average expected profit margin also increases the variance to say, +/-20% then this change no longer looks as promising.

The choice to go forwards with this option, or to ignore it can still be made by the business, but now with a full understanding of the available risk and reward.

We have already made considerable progress in this area with our Portfolio Management Engine and Knowledge Hub, but this remains an active area of research for us. If you'd like to learn more about this please contact us.