• Limited Forecast Accuracy:
  
  • Cash flow forecasts were generated manually using spreadsheets
  • Forecast accuracy varied significantly across regions and business units
• Lack of Visibility:
  
  • Treasury teams had limited visibility into future cash inflows and outflows
  • It was difficult to identify potential liquidity risks several weeks in advance.
• Multiple Cash Flow Drivers:
  
  • Cash inflows depended on customer payment behavior, sales volumes, and receivables collections.
  • Cash outflows were influenced by raw material costs, payroll, utilities, logistics, and tax payments.
  • External factors such as inflation, energy prices, and interest rates impacted cash flow performance.
• Hierarchical Reporting Challenges:
  
  • Forecasts were required at company, region, and business-unit levels.
  • Significant effort was required to consolidate forecasts across organizational hierarchies.
• No Scenario Planning Capability:
  
  • The business could not easily evaluate the impact of delayed customer payments, rising energy costs, or changes in demand.
  • Strategic planning decisions were largely based on static assumptions.

A configurable Cash Flow Forecasting Platform was implemented to automate forecasting and provide treasury teams with actionable insights.

• Automated rolling cash flow forecasts to improve visibility into short-term and medium-term liquidity positions. 
• Enabled forecasting at multiple organizational levels, including company, region, and business unit hierarchies. 
• Incorporated key business drivers and external factors to improve forecast reliability and support planning activities. 
• Introduced scenario planning capabilities to assess the impact of changes in customer payments, operating costs, and market conditions. 
• Provided interactive dashboards and reporting capabilities to monitor cash positions, forecast performance, and experiment with changes in scenarios.
• Replaced manual spreadsheet-based forecasting processes with a scalable, data-driven forecasting solution. 
• Established a consistent forecasting methodology (experimented with various Stat and ML methodologies) and governance framework across finance and treasury teams. 
• Enabled proactive decision-making through improved forecast accuracy, transparency, and business insights.