This site uses cookies to provide you with a more responsive and personalized service. By using this site you agree to our use of cookies. Please read our cookie notice for more information on the cookies we use and how to delete or block them.

Bookmark Email Print this page

Balancing the Risk-Return Equation

How CFOs can use risk-adjusted forecasting and planning to protect and enhance value, boost confidence, and manage risk.


DOWNLOAD  

Financial forecasts and plans carry a lot of weight in the business world. But how much confidence do companies and CFOs really have in their forward-looking numbers – especially in a business environment that is increasingly complex, uncertain and risky?

Companies are looking to move beyond traditional approaches to forecasting by incorporating multivariable risk modeling and analysis. The result? An improved approach – which we call risk-adjusted forecasting and planning – that shows a broad range of likely outcomes and their associated probabilities.

Download this white paper to find out how you can have greater confidence in forward-looking plans and forecasts and to manage risk more effectively.

Explore case studies

Risk-adjusted forecasting to support strategic decisions

A Pharmaceutical Preparation Manufacturer’s franchises were the cornerstones of its portfolio. The company developed risk-adjusted forecasts to better understand, assess and prioritize disease areas based on commercial attractiveness and strategic fit. In this way, the company identified high-potential assets and M&A targets. The risk-adjusted forecasts indicated that over the next 10 years, one of their franchises would face declining revenue, while another franchise would grow at a moderate rate. Each franchise forecasted growth rate was below the organization’s targeted growth rate, highlighting the need to explore inorganic growth opportunities.

Risk-adjusted forecasting and scenario modelling to optimize asset performance

The main operating sites of this Global Metals and Mining Company frequently failed to meet planned production targets and budgets. The underlying planning process relied on averaged values based on historical performance, and did not take process variance into account during the limited scenario modelling that was conducted. By identifying and analyzing key input variables, quantitative distributions were able to be developed for each risk driver, and risk-adjusted forecasting models were generated for each the sites. This resulted in an improved understanding of how the underlying volatility was impacting production performance. On the back of this, more effective decision making was enabled through enhanced scenario modelling, increasing confidence in plans and budgets – and ultimately improving profitability.

Risk-adjusted economic forecasting to generate probabilistic KPIs

This Energy Utility Company was frustrated with the deterministic nature of the organization’s long-range economic forecasting. The organization targeted the development of advanced, stochastic economic forecasting models that could effectively analyze the business in such a way that the organization could understand their business issues from a risk-weighted probabilistic perspective. The company developed an economic forecasting engine that utilized Monte Carlo simulation and linear programming techniques to simulate the behavior of commodity markets (power, natural gas, coal), interest rates (treasury rates, credit spreads etc.), customer demand, power generation dispatch, and other uncertain factors in order to generate a set of defined probabilistic key performance indicators.

Related links

Share this page

Email this Send to LinkedIn Send to Facebook Tweet this More sharing options

Stay connected