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Abstract
Effective management of financial flows is essential for sustaining liquidity stability and operational efficiency within multi-tier supply chain networks. Traditional optimisation models
tend to prioritise either cost reduction or risk mitigation, often neglecting the balance between
working capital efficiency and financial stability. This study proposes a Hybrid Mathematical Framework that integrates dynamic discounting mechanisms and risk mitigation strategies to optimise financial flows across supply chains. The framework addresses four primary objectives: developing a financial flow optimisation model, incorporating dynamic discounting into the
model, embedding stochastic variables representing demand volatility and credit risk, and
evaluating model performance through numerical simulations. A quantitative modelling approach is employed, formulating an objective function that minimises total financial costs while controlling for risk using Conditional Value at Risk (CVaR). The model integrates early payment incentives, late payment penalties, and financial risk thresholds to support strategic decision-making. Numerical simulations using synthetic financial data were conducted to assess the model’s performance. Results indicate that the Hybrid Model offers a superior trade-off between cost efficiency and financial stability. Dynamic discounting reduces total financial costs, while CVaR integration ensures liquidity remains risk-sensitive. The study recommends adopting dynamic discounting with risk-sensitive optimisation models and exploring technologies like real-time analytics and AI. Future research could refine the framework via industry-specific adaptations and block chain enabled contracts.
tend to prioritise either cost reduction or risk mitigation, often neglecting the balance between
working capital efficiency and financial stability. This study proposes a Hybrid Mathematical Framework that integrates dynamic discounting mechanisms and risk mitigation strategies to optimise financial flows across supply chains. The framework addresses four primary objectives: developing a financial flow optimisation model, incorporating dynamic discounting into the
model, embedding stochastic variables representing demand volatility and credit risk, and
evaluating model performance through numerical simulations. A quantitative modelling approach is employed, formulating an objective function that minimises total financial costs while controlling for risk using Conditional Value at Risk (CVaR). The model integrates early payment incentives, late payment penalties, and financial risk thresholds to support strategic decision-making. Numerical simulations using synthetic financial data were conducted to assess the model’s performance. Results indicate that the Hybrid Model offers a superior trade-off between cost efficiency and financial stability. Dynamic discounting reduces total financial costs, while CVaR integration ensures liquidity remains risk-sensitive. The study recommends adopting dynamic discounting with risk-sensitive optimisation models and exploring technologies like real-time analytics and AI. Future research could refine the framework via industry-specific adaptations and block chain enabled contracts.
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