When drivers change

With forecasting increasingly determining operational decisions for businesses, an awareness of changes to the drivers behind the predictions must be constantly maintained.

The key drivers behind most financial forecasts are operational metrics that have a direct mathematical relationship with financial outcomes. Applying changes to those factors is a quick and easy way of establishing sensitivities and testing alternative scenarios.

The most skilful planners will drill down into the granular data behind a model to identify the most impactful drivers and gain better understanding into how they work. There is often more of a story behind the apparent control factors.

The detail behind the drivers

For example, footfall might appear to be the logical determinant of retail sales. However, it’s more nuanced than this as in each case this is constrained by store size and impacted by location. Simply applying a global adjustment in footfall would provide an incomplete picture of possible future performance.

A more developed model might classify stores by location and apply an additional calculation for capacity. This would allow a global change in demand which affected footfall to be applied more appropriately across the individual outlets.

But what happens when patterns change dramatically? What do you do when external factors make the old model redundant?

The COVID-19 crisis and its impact during the 2nd quarter of 2020 have thrown up challenges for FP&A teams in every sector. We’ve shared previously how our clients have been variously turning to scenario planning and rolling forecasts to cope with the new demands.

Whichever approach organisations take, the models that underpin the process have to be looked at very closely to determine the real impact of recent changes on fundamental drivers.

Taking new conditions into account

Consider the retail model used above. The footfall consideration and underlying assumptions around square footage will now have to be superseded by the consequences of social distancing guidelines. How do you convert store capacity into a figure that takes socially-distanced shoppers into account? Is it even possible? And yet at previous points in the crisis footfall was rendered irrelevant; when panic-buying resulted in empty shelves the only driver that mattered was stock availability further up the supply chain.

Disparities in the variations to drivers across the operation require more complex planning capabilities again. How quickly can your team adapt your forecast to react to different sets of changing local conditions? Take construction in the UK – projects across Scotland, Wales, England (and within that, areas such as currently locked down Leicester) will all be returning to operation at different times and under potentially variable conditions. How easy is it for you to separate those elements and determine their specific impact on the overall forecast?

Can your planning system cope?

When operating within a spreadsheet-driven system, the pains involved in adapting your forecast models are immeasurable. With everyone working remotely, trying to collaborate and maintain a single version of the truth is posing even more of a challenge.

IBM® Planning Analytics provides your team with a centralised application delivering the flexibility and speed which are going to be essential to successful businesses in the coming months. Operating within a multi-dimensional system allows for quick updates of individual elements of the forecast and the isolation of certain aspects of drivers of the model.

The sandboxing capability opens up the possibility of easily testing alternative approaches without having a direct impact on the underlying model. It also allows for an alternative model to be adopted for a set period of time to take specific short-term scenarios (i.e. temporary lockdown in certain areas) into account. One of our construction clients, a regionally controlled business, has been using this function to provide assessments for their concerns over specific areas using ‘what-if’ analysis alongside their monthly forecast.  This has provided the basis for boardroom analysis, leading to focused and informed decision making.

Enterprise-style planning is not reserved entirely for larger organisations. The launch of Planning Analytics on Demand for SMEs gives access to the same powerful toolset at an accessible price. One authorised user can be up and running on a 2GB application database for £36 per month with additional users costing £32 per month. 

With this software, your FP&A team will gain the time and the insight required to support agility within your business, allowing them to supply more valuable analysis that will drive profitable decision making in these turbulent times. Get in touch today to take the first steps on your journey to a smarter method of working.