Recently, I had the good fortune to attend the Financial Planning & Analysis Transformation Summit. The event was well-organised and the format was perfectly suited to the subject matter. There were several insights I took away, particularly around the evolving role of finance from reflecting on historical data to forecasting future trends. Leveraging predictive analysis rooted in AI, it seems clear to me that those embracing FP&A are poised to leap ahead of their counterparts. I firmly believe that AI will revolutionise virtually every aspect of our lives, a transformation that is well underway and will only accelerate. It seems self-evident that intelligent, context-driven predictions will lead to superior management decisions.

Another insightful takeaway was the consensus among the speakers: successful transformation isn’t merely about having the right tools. Managing change among FP&A personnel and developing robust communication skills – ‌vital for analysts tasked with explaining these complex analyses to their managers – are equally critical for a successful transition. All in all, the day was well spent.

At BlueSky, our core focus is operational optimisation or, as it’s often termed, “Prescriptive Analysis”. Predictive models enable organisations to foresee future revenues and expenses, customer behaviour, market trends, and other crucial business indicators, fostering a proactive approach, better strategic planning, and more effective risk management. However, prescriptive analysis takes us one step further. If predictive analysis seeks to answer ‘What will happen?’, prescriptive analysis tackles ‘What should we do about it?’ Prescriptive analytics leverages optimisation and simulation algorithms to propose the best course of action, enhancing decision-making by providing data-driven recommendations on strategic issues like pricing, resource allocation, risk management, and more.

Imagine an FP&A team employing prescriptive analytics to determine the optimal pricing strategy for a new product. The model would evaluate factors like production costs, the competitive landscape, customer price sensitivity, and desired profit margins to propose the ideal price. Likewise, prescriptive models could assist capital budgeting decisions by examining various investment options based on their risk-return profile and recommending the best investment mix.

A recurring challenge we encounter when working with organisations to optimise specific operations or functions is data availability. I believe organisations embarking on FP&A transformation need to plan for the next phase – the move from prediction to prescription – by ensuring they’re collecting the necessary data now. A predictive model might indicate strong growth for a particular product, which is helpful information, but the company must also consider the potential consequences of this increased demand. The analysis must extend to the impact on production planning, logistics, supply chains, and staffing requirements. These interrelated variables – or constraints, as we call them – must be evaluated concurrently; it’s not viable to consider them separately or sequentially. Therefore, when defining the data points required for intelligent predictions, it’s equally important to capture all information related to implementing these predictions, such as machine setup times, warehouse storage constraints, and material handling equipment investment.

This is where FP&A becomes a crucial partner for Operational functions, integrating Financial and Operational Data to drive optimal outcomes. Who knows, maybe we’ll soon see another summit – “Transforming Operations and the role of FP&A!” – on the horizon.

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