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Digital twin framework for liquidity management: Bridging the gap between theory and operations
The corporate cash management problem (CMP) constitutes a critical operational challenge focused on determining the optimal sequence of transactions between liquid cash holdings and alternative investment assets. The primary objective is to maintain sufficient liquidity to meet obligations while simultaneously minimizing the aggregate costs associated with idle capital and transaction execution. Despite profound theoretical advancements since the foundational inventory-based models of the 1950s, contemporary literature exhibits persistent methodological and practical deficiencies. A comprehensive multidimensional review by Salas-Molina et al. (2023) identified six critical open research questions that currently limit the applicability of CMP models: the overreliance on rigid bound-based heuristics, the unrealistic assumption of Gaussian cash flow processes, the limitation to linear cost functions, the narrow focus on single-objective cost minimization, the lack of robust solvers for high-dimensional spaces, and the systemic omission of multiple bank account networks. Concurrently, within the domain of business informatics, the emergence of digital twins, integrating simulation modeling and machine learning, has proven highly effective in mitigating financial disruptions. This research report proposes a novel Hybrid Multi-Account Cash Management Digital Twin (HMAC-DT). The HMAC-DT provides a strict mathematical formalization addressing nonlinear costs, multiobjective risk criteria, and complex multi-account dynamics. The framework proposed transitions from abstract mathematical heuristics to direct policy identification. Specifically, the algorithmic architecture utilizes Particle Swarm Optimization (PSO) to explore high-dimensional continuous action spaces and a Classification and Regression Tree (CART) algorithm to extract transparent IF-THEN operational rules. Simulation experiments demonstrate that the HMAC-DT significantly outperforms traditional models, reducing treasury costs while enhancing operational stability. Evaluated over a simulated horizon of 1,000 discrete days across three interconnected accounts, the digital twin reduced total treasury costs by 34% and mitigated variance risk by 42%. Additionally, the orchestration of cash flows resulted in a 26% improvement in the cash conversion cycle (CCC) metric. Furthermore, profound managerial implications are synthesized into a redefined, DT-driven treasury business process, transforming cash management from a reactive administrative task into a proactive system.