1 Finance Department, Henkel.
2 Finance Department, Temple University, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 14(01), 665-678
Received on 25 March 2022; revised on 26 April 2022; accepted on 29 April 2022
The consumer goods sector operates within a complex financial ecosystem, characterized by inherent volatility and diverse risk exposures. Effective risk management is fundamental for sustaining operational efficiency and market competitiveness. This article presents a comprehensive examination of advanced quantitative methods for optimizing financial risk within consumer goods enterprises, specifically leveraging Monte Carlo simulation and machine learning techniques. We delineate the theoretical underpinnings and practical applications of these methodologies, assessing their capacity to model and mitigate risks such as market fluctuations, supply chain disruptions, and credit exposures. The analysis synthesizes current research on hybrid modeling architectures that integrate probabilistic simulations with predictive analytics, illustrating how such approaches can enhance decision-making under uncertainty. Furthermore, we address the systemic impacts of these advanced tools on risk mitigation strategies, discussing the organizational, technological, regulatory, and ethical considerations pertinent to their successful implementation. Our exploration details how data-driven risk management offers a strategic advantage, fostering greater resilience and adaptability in dynamic market conditions. The findings offer insights for both practitioners and researchers seeking to implement robust financial risk optimization frameworks in the consumer goods industry.
Financial Risk Optimization; Monte Carlo Simulation; Machine Learning Analytics; Consumer Goods Supply Chains; Value-at-Risk (VaR); Hybrid Predictive–Stochastic Modeling
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Samuel Oladapo Taiwo and Collins Kwadwo Amoah-Adjei. Financial risk optimization in consumer goods using Monte Carlo and machine learning simulations. World Journal of Advanced Research and Reviews, 2022, 14(1), 665-678. Article DOI: https://doi.org/10.30574/wjarr.2022.14.1.0385