Advanced financial modeling techniques for reducing inventory costs: A review of strategies and their effectiveness in manufacturing

Abstract

This study investigates the effectiveness of advanced financial modeling techniques in reducing inventory costs within the manufacturing sector, with a focus on the integration of predictive analytics, artificial intelligence (AI), and machine learning. Employing a systematic literature review and content analysis, the research scrutinizes academic journals, conference proceedings, and industry reports published between 2015 and 2024. The methodology hinges on predefined inclusion and exclusion criteria to ensure the relevance and quality of the selected literature, followed by a thematic analysis to distill key insights. Key findings reveal that advanced financial modeling significantly enhances demand forecasting accuracy, thereby optimizing inventory levels and reducing associated costs. The integration of AI and machine learning technologies not only streamlines inventory management processes but also enables manufacturers to adapt swiftly to market fluctuations, thus minimizing waste and improving operational efficiency. Despite the evident benefits, challenges such as data quality, technological integration, and ethical considerations in AI implementation were identified. The study recommends that manufacturers prioritize the adoption of these advanced models, invest in relevant technologies, and foster a culture of continuous learning and adaptation. Future research directions include exploring the scalability of these models for SMEs, assessing the long-term sustainability of cost reductions, and investigating the potential of emerging technologies like blockchain and IoT in inventory management. Finally, the strategic implementation of advanced financial modeling techniques offers a pathway for manufacturers to enhance competitiveness, achieve cost efficiencies, and navigate the complexities of the digital era in inventory management. Keywords:  Advanced Financial Modeling, Inventory Cost Reduction, Predictive Analytics, Manufacturing Sector.