What is the value of reducing the effects of weather on installation time? How big is the market for a new offshore foundation technology? Which sites in a given competitive market can produce energy for the lowest cost? To get robust, honest and valuable answers requires intelligent modelling.
In any model, getting the right input data is vital. In the real world, it means working with the imperfections of the available data, understanding its origins and talking it through with those with first-hand knowledge. Understanding uncertainties and applying engineering judgement to fill gaps in data or to address seemingly large inconsistencies is critical.
The most important thing to remember is that a detailed model is not necessarily an accurate model. Having more information is always good, but to generate results which are of value, significant judgement is required. It would be naïve to trust the output of a model without questioning the assumptions and checking unexpected results. We have learnt that regularly subjecting results to expert review really helps capture changes in the rapidly evolving market we work in. Results should always be challenged by asking if they make sense in terms of wider market, economic and technological environment.
Models can never produce perfect predictions of the future or descriptions of different scenarios. Uncertainties can have a significant impact on the results and these impacts need to be presented so that they can be considered in making decisions.
Combining engineering-based algorithms with solid experience in an effective way requires an open and collaborative approach. Without both, all you have is a set of sterile equations or a set of opinions. Only by bringing both elements together can models help drive real business benefit.

Kate Freeman

Junior Associates