The client organisation required a more sophisticated insight into cost uncertainty for a large programme of works. The programme was made up of multiple sub projects covering civil, heavy and systems engineering disciplines. There were many complex interfaces and scheduling dependencies between the sub projects.
The client had an existing risk management & quantitative cost / Monte Carlo forecast system. Like most commercially available systems this used a simplistic additive approach in which the overall risk was simply the sum of the individual risks. The client recognised that this might not be robust for their programme.
Into Risk provided a forecast that incorporated the effects of commonality in root causes and knock on impacts. This was done using the CASM (Cause Association / Systems Mechanisms) framework. The modelling was undertaken using the Into Risk’s Monte Carlo modelling engine.
The results of the analysis were significantly different to those provided by the clients system. CASM suggested level of contingency funds given to each sub-project manager should be halved; it also informed that the level of management reserve held by the programme director to stabilise subproject interfaces would need to be significantly higher.
As there were over 1,500 input across 20 sub-projects the use of CASM created a significant savings in terms of analytical effort and consultancy fees. A more conventional approach would use a ‘correlation matrix’ in the Monte Carlo software, which potentially requires a specification of the relationship between each input. In this case there were over 50,000 comparison points – this is one of the reasons many analyses do not incorporate correlation.