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Case Z/Yen Graphic

Visualizing an uncertain sales pipeline at Z/Yen
From the Innovative Cases series

by Matthew Leitch 21 May 2010, with help from Z/Yen

The Innovative Cases series features practices in use by organizations that illustrate innovation in risk control. They are simple, specific ideas that can be applied elsewhere, even though they are often part of a wider approach. These innovations go beyond generally recognized thinking. The individuals and organizations responsible for these innovations have their own reasons for using them and I comment on what impresses me.

The idea

Z/Yen is a small but influential consultancy based in the City of London with a track record of coming up with innovative ideas for just about anything, so it is no surprise that their internal management systems are full of interesting ideas.

Their approach to visualising their sales pipeline is typical of them but not typical. Like most consultancies, Z/Yen's income often arrives in large lumps, whose arrival, timing, and size are highly uncertain. Z/Yen's directors, Ian Harris and Professor Michael Mainelli, wanted a method that would give more information than just a list of sales in progress with best guesses for their value.

Their system produces various displays that show the probability distribution of sales out across future periods. (In fact it is a sales tracking system with a lot of other functionality, but it is the visualization of sales forecasts that is the subject of this Innovative Case.)

The system has been used monthly by Z/Yen for several years. Internally it is known as Z/EAL. It has also been implemented by a number of clients and is marketed under the names PPRISM and PPRIFM.

The design

In this first picture you can see the total sales actually agreed each month (red blobs), their annual moving average (the fairly steady line with black squares), and various predictions of expected value and high and low values month by month. In this example the sales forecasts seem to have been somewhat optimistic, as revealed by the weighted 12 month rolling average win value, which is typically lower than the weighted expectation.

This second picture shows another style of display. Here the coloured bands indicate confidence intervals, showing in effect a sequence of probability distributions over time. In this example the uncertainty in forecasts is fairly low – much lower than in the first picture.

The tool is built with Excel and Access. It calculates the distributions by applying Monte Carlo simulation to the database of possible sales, using the probability distribution information for each sale, which is captured using triangular distributions. In the example below the system has run just 1,000 scenarios, but this is enough to show that a wide range of outcomes is possible.

The tool can provide non-probabilitistic displays too. The picture below cuts potential sales into different types of work and different stages in the sales pipeline.

Why this is a good idea

The information in these displays helps Z/Yen make decisions about things like when to get associated consultants involved (because their own employees may be unavailable), when to take holidays, when to stop selling, and when to sell harder. They can also learn about their forecasting tendencies.

All this is on the basis of displays that counter the natural tendency towards uncertainty suppression (i.e. acting as if more certain than you are or should be). This helps Z/Yen stay aware of the value of flexibility and of when to get more information to help with difficult staffing and pricing decisions. Without this perspective they would feel constantly surprised by the difference between forecasts and actual sales.

Z/EAL is also a classic example of an intelligent internal control. It involves explict thinking about uncertainty, modelling, and management decision making. It leads to additional risks responses being put in place, usually in the form of information seeking and flexible agreements.

Finally, it is an outstanding example of fully embedded risk reporting. Rather than reporting risks on a report separate from sales forecasts, Z/EAL simply shows the uncertainty within the forecast report itself.

Further information

Further information is provided by these articles on the Z/Yen website.


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I would like to thank Z/Yen for permission to feature their innovative system on my website and to thank Michael Mainelli for introducing me to Z/EAL and providing information on its use.

Text © 2010 Matthew Leitch
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If you found any of these points relevant to you or your organisation please feel free to contact me to talk about them, pass links or extracts on to colleagues, or just let me know what you think. I can sometimes respond immediately, but usually respond within a few days. Contact details

Matthew Leitch - Author

About the author: Matthew Leitch is a tutor, researcher, author, and independent consultant who helps people to a better understanding and use of integral management of risk within core management activities, such as planning and design. He is also the author of the new website,, and has written two breakthrough books. Intelligent internal control and risk management is a powerful and original approach including 60 controls that most organizations should use more. A pocket guide to risk mathematics: Key concepts every auditor should know is the first to provide a strong conceptual understanding of mathematics to auditors who are not mathematicians, without the need to wade through mathematical symbols. Matthew is a Chartered Accountant with a degree in psychology whose past career includes software development, marketing, auditing, accounting, and consulting. He spent 7 years as a controls specialist with PricewaterhouseCoopers, where he pioneered new methods for designing internal control systems for large scale business and financial processes, through projects for internationally known clients. Today he is well known as an expert in uncertainty and how to deal with it, and an increasingly sought after tutor (i.e. one-to-one teacher). more

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