Partner type
Global
Function
Finance
Industry
Banking & Capital Markets, Business Services, Consumer Goods & Retail, Consumer Products, Energy & Resources, Healthcare & Pharmaceuticals, Insurance, Life Sciences & Healthcare, Manufacturing, Technology & Software, Telecom, Travel & Entertainment
Region
Global
In today’s fast-paced world, there are many evolving factors that impact a business. Manual scenario planning isn’t enough to build resiliency in capital planning.
BCG’s solution applies a modern methodology to capital planning with automated driver-based modeling through Monte Carlo simulations. Using integrated historical data, 1000+ simulations forecast drivers of value and build distributions of key financial metrics, such as cash flow. Using seamless integration with Anaplan and powerful reporting, the business gains deeper insights into performance under the simulated scenarios. Users can conduct cash flow at risk modeling, as well as automate scenario-based financial forecasts. BCG’s predictive solution offers profound insights into risks faced by the enterprise, and empowers the organization to build resiliency when making decisions.
Solution features
Key solution features include:
- Integrated financial forecasting:
Analyze histograms of key financial metrics (e.g. NPV, revenue, cash flow) in order to view a probabilistic set of outcomes for these KPI values. Histograms allow leadership to see the cash flows that the company could face over a variety of confidence levels across selected time horizons. - Centralized historical data:
Understand the behavior of drivers in the past with distribution views of historical data. Access historical data to gain insights on the data behind the predictive models. - Option to connect planning models:
Monte Carlo simulations embedded into Anaplan produce forecasts efficiently and can integrate automatically with project portfolio optimization. When combined with optimization, simulations can confirm the robustness of a scenario. - Customizable risk models:
Operational risk loss modeling can layer in additional complexity to capture business disruptions, including timing and severity, and the expected losses.
