Weather Forecasting and Risk Management: From Failure to Success

Weather forecasting and risk management share a few commonalities: both weather forecasting and risk management use numbers to describe what may happen in the future. A structured approach underpins both these areas. The same way the weather forecasting is an essential Application of a smartphone owner, risk management is an essential tool for a company owner. First and foremost, uncertainty is the biggest challenge they address.

Because of this inherent uncertainty, weather forecasting and risk management find their value in their accuracy. While a flawed weather forecast could potentially have limited impact, such as affect the success of a planned sporting event, a flawed risk management activity can be disastrous for the success of an investment. Consequently, the accuracy of risk management determines the quality of decision-making.

How do you measure the accuracy of your risk management activities? The Met Office relies upon a state-of-the-art computer, a complex atmospheric model, and millions of daily weather observations to check and improve the accuracy of their weather forecast. How is your company identifying lessons from the gap between what was predicted and what really happened? How is your company transforming lessons identified into lessons learned in order to relentlessly improve the accuracy of your quantitative risk analysis?

When we start reflecting on risk management activities, a handful of key questions arise: Why is estimating risks so difficult? What should we do before assessing risks? Is there a way to improve our ability to forecast? How can we make our stakeholders better at assessing risks?

In ‘The Failure of Risk Management’ (2009), Douglas W. Hubbard asserts a handful of key principles to keep in mind for successful risk management activities:

  • Humans misperceive and systematically underestimate risks
  • ‘Calibrate’ all stakeholders before their assessment of probability/impact
  • Check actual outcomes against original forecasts
  • Incentivize stakeholders to follow up on previous forecasts to see how well they did
  • Reduce uncertainty on highly uncertain variables through empirical observations

If the above is still clo for you, then come and visit us.

Written By – Laurent Nicourt, Senior Consultant, Sytel Reply