About Pirmin Mutter

From Averages to Reliable Ranges in Liquidity Planning. Companies often still manage their liquidity based on averages, thereby systematically underestimating the uncertainty in cash flow. However, actual payment behavior often deviates significantly from contractual assumptions and is a key driver of forecasting errors. This whitepaper shows how process mining and AI close this gap: By analyzing real transaction data, reliable payment profiles and forecast ranges are generated instead of static point values. This enables companies to manage their liquidity in a significantly more transparent, precise, and risk-adjusted manner while simultaneously gaining new insights for optimizing their working capital.  

Today, companies are increasingly exposed to unforeseen risks – ranging from physical supply shortages and cyberattacks to geopolitical trade conflicts. This volatility directly impacts supply chains and can severely affect financial stability. However, our latest simulation shows that preparation is the key differentiator: With a forward-looking steering model and enhanced transparency, cash positions in crisis scenarios can be improved by more than 100%. To secure long-term competitiveness and liquidity, companies must implement preventive measures spanning from strategic planning to operational interventions like an inbound freeze.

In the metals and raw materials industries, rising energy and material costs, intense international competitive pressure, and volatile sales markets have placed significant pressure on margins since 2020. Capital efficiency has declined by more than 30% during this period, as many companies have failed to offset declining profitability through targeted cash flow and working capital management. To stabilize capital efficiency, companies must increasingly address both levers—profitability and capital employed—and optimize them jointly within an integrated project approach.