Value Driver Tree

I have been involved in the development of a number of Value Driver Trees (VDT) related to the mining sector. However, before I provide some more detail on a specific example, I would like to explain what a VDT is and how it is used.



The orgins of the VDT appear to stem back to the 1920’s in the USA when Du Pont developed  the graphical depiction that allowed uses to see the relationship between the return on investment/return on equity of the organisation and say, the level of inventory holdings. An example of  the format is shown below:

In more recent years the term Value Driver Tree has been used to describe the concept more generically.  What I like particularly about the value driver tree is that it simplifies the representation of what could otherwise be complex information into a simple (and dynamic) graphical representation. This is a great way of getting the message across to a wide range of employees who work in their respective functions but have little or no insight into what role they play (or could play) on the financial performance of their company.


An example of a value driver tree format









I have been using VDT”s over a number of years but the most intensive application of this concept was during my period working in Chile and the USA where I completed a set of VDT’s for three mining operations owned by the same organisation. This work included both the design, development and creation of a set of user manuals to help to administer the VDT tools.

All of these VDT’s were developed in Excel and as  result had a number of inherent limitations due to the choice of that platform. However, it turned out that developing the VDT’s in excel caused a much more hands on approach to the use of the tool which resulted in a greater understanding of what information it was providing and how to interpret it.


What was unique about the way in which I designed the VDT’s was in the use of the banding limits that applied to each of performance KPI”s that existed within the VDT. There were 4 banding limits that could be set individually for each KPI. For example, a KPI for equipment availability might have the following banding parameters

Upper Critical   threshold    95%

Upper Warning threshold 93%

Lower Warning threshold  90%

Lower Critical threshold     88%

If the equipment was performing between 90 and 93% availability, then this level of availability would be considered normal (green). Performance in the band of 93 – 95% or 88 – 90%  would trigger a warning indicator (yellow) alerting management to information suggesting that monitoring is required.  Performance above 95% or below 88% would show up as a ‘red’ flag alerting management to intervene.  The primary differences between this approach and other VDT’s that I have seen being developed are:

Synchronize vs Maximize – You will not that in the example described above, there are upper and lower thresholds implying that excessively high performance is as bad as excessively low performance. Why? Because unless the particular equipment is the systems bottleneck, the primary objective of this non bottleneck equipment is to ‘synchronize’ availability (and utilization) with the systems bottleneck so that neither starvation, blockage or downtime is experienced.

Warning mean’s monitoring, not intervention – There is an automatic tendency by management to feel the need to intervene when the truth is that intervention was not required because the performance variance was short term and or infrequent.  Under these circumstances, any intervention aimed at correction can result in the process becoming unstable. Process stability is a prerequisite to Process capability.

Recognizing sprint – Sprint is a term used to describe the ability of an activity within a process to increase the velocity of its output upto a given level. For example, if a trucking fleet delivers ore mined at the rate of  1,000 tonnes per hour but can increase that rate of 1,200 tonnes per hour when required to do so then the extra 200t/hour would be described as sprint capacity. By applying the sprint capacity into the critical threshold values, the VDT becomes more realistic and enforces the synchronization requirements back to the system bottleneck . It also provides important information to management in instances where there is a cost reduction initiative underway which could seek to reduce the cost of maintenance to a point where sprint capacity is compromised therefore potentially affecting the systems throughput

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