If you’ve switched on the news recently, you’ll have noticed how the words “supply chain” have now become part of Britain’s everyday vocabulary. From fuel shortages at petrol stations to the terrifying prospect of a Christmas turkey shortage, we’ve suddenly become aware of how precarious our society is, and how quickly we can slip from order into chaos.
Of course, for anyone working in the utilities sector, this is hardly news. But supply chain risk has never been more apparent to the public, as sudden rises in wholesale gas prices have played havoc with the retail energy market. Several smaller suppliers have gone into administration, while others have had to compensate by increasing prices for consumers—putting the industry in the headlines for all the wrong reasons.
Predicting the future
As usual when an industry is hit by market volatility, everyone wants to know what will happen next. And that’s an especially challenging question for financial planning and analysis (FP&A) teams because there are so many variables to consider. What happens to our margins if gas prices continue to rise? If we increase our tariffs, how much of an increase in bad debt can we expect? If more of our competitors go bust and we take on their customers, what impact will that have on our bottom line?
The problem for most utilities is that while they already create a variety of forecasts for accounting, cashflow modelling and financial planning, their forecasting processes still depend heavily on cumbersome, error-prone spreadsheets or relatively basic financial modelling tools.
As usual when an industry is hit by market volatility, everyone wants to know what will happen next.
As a consequence, instead of forecasting based on data from individual customer accounts, FP&A teams have to aggregate customer data into broad buckets. This makes their forecasts insensitive to emerging behaviour patterns and often results in large margins of error. Moreover, creating forecasts is so time-consuming that it’s impractical to model multiple different scenarios or perform “what if” analyses—so the business can’t make detailed contingency plans for different situations.
Advancements in forecasting
Fortunately, these issues are far from insurmountable. Since the 2008 global financial crisis, the financial services sector has been under pressure from regulators to improve its financial forecasting capabilities, which has led to significant advances in modelling techniques and technology platforms. SAS clients such as Standard Chartered Bank now routinely run stress-tests that simulate thousands of different scenarios in a matter of hours, enabling much deeper analysis based on much more granular data at the individual customer level.
Today, we’re working with our energy and utilities clients to adopt these same technologies and techniques for smarter financial forecasting. Together, we’re integrating new sources of data, from customer records to market data and macroeconomic forecasts. We’re introducing a far wider range of forecasting algorithms, including artificial intelligence and machine-learning-based approaches. And we’re making scenario modelling and what-if analysis into core capabilities by delivering platforms that accelerate forecasting processes from hours to seconds.
Today, we’re working with our energy and utilities clients to adopt these same technologies and techniques [that banks use]for smarter financial forecasting.
Focusing on business value
With these new forecasting capabilities, FP&A teams can accurately predict the impact of market forces, customer behaviour and regulatory changes on a utility’s finances. This means they can provide the insight the CFO and board need to navigate volatile markets, mitigate supply chain risks, and gain a competitive edge. By helping the business make smarter decisions, FP&A becomes more than just a cost centre—it becomes a key source of business value.
If you’d like to learn more about how SAS can help your team improve its forecasting capabilities and deliver greater business value, reach out to me today at email@example.com.