Transforming Adbri Data to Actionable Insights

Taming the financial modelling beast

The client

An ASX 200 industrial materials manufacturer with more than a century of operating experience in Australia was undergoing a strategy refresh for a business line which required financial modelling.

Having previously commissioned a scenario-based excel model, our client was grappling with a  complex model that had limited KPIs and financial insights, which hindered their decision-making process. Our client needed us to restructure the model and extract the full value with a model that was easy to use and provided real-time updates to KPIs, financial outcomes and other data points.

Our approach

In the first phase, our analytics expert reviewed the previous financial model’s results, reporting and usability to provide a recommendation on either continuing to use the model or create an entirely new model.

The review process began by testing the model using our in-house model analytics tool to check for consistency, bugs, errors, and provide high-level insights into the size and structure of the model.

The Churchill approach to problem solving is always considering the optimum solution; which may not always be the most obvious. For this project, we identified a third option that sought to leverage previous sunk costs and harness the power of the current model with a rebuild.

By capturing the value of the current model and simplifying its complexity, we could then apply our own expertise for extracting insights and designing decision facilitating KPIs. We considered the needs of both the leadership team and the model owner to design a new model that was ready for board presentation in less than a month.

The outcome

The model rebuild created a flow of information and developed a trust in the insights provided, and now includes:

• Simplified and clear one-stop-shop for inputs
• Protected engine room for calculations and interactions
• Series of dashboards offering at-a-glance comparisons and increasing granularity for key driver scrutinisation

Inputs-wise, the model user has gone from needing to review almost 500,000 cells for potential inputs to under 500. Outputs-wise, after providing only a few tabulated measures, the model dynamically produces dashboards, cash flow forecasts, asset efficiencies, and a range of other KPIs in easy-to-read format. All of this was accomplished without the adjusting the logic of calculations.

The leadership team now has a powerful resource to facilitate strategic decision making, and the confidence that it is used appropriately and produces dependable results.