Liquidity management models in a VUCA environment: Research focus shift

Effective cash management remains a persistent challenge in dynamic economic landscapes, particularly under VUCA conditions. While traditional cash management models, including stochastic programming, have offered valuable insights, their limitations in capturing rapid changes and unknown uncertainties are becoming increasingly apparent. This paper addresses this gap by conducting a comparative analysis between a Stochastic Goal Programming model and a Particle Swarm Optimization model for cash flow management within a simulated VUCA environment. The study hypothesizes that advanced optimization techniques, specifically the metaheuristic PSO, will demonstrate superior performance across key metrics in such conditions. Through a rigorously designed simulation the results affirm this hypothesis. The PSO model consistently exhibited lower overall costs, improved risk mitigation, and enhanced cash balance stability compared to the SGP model. This superiority is attributed to PSO's distribution-agnostic nature, its ability to handle non-linear dynamics, and its adaptive search capabilities, making it more resilient to the unpredictable and multifaceted nature of VUCA environments. These findings advocate for a paradigm shift towards more adaptable optimization techniques in liquidity management research and practice, highlighting the potential for metaheuristics to offer more robust solutions in the modern financial world.