Advance Financial Modeling Techniques for Reducing Costs: A
Review of Strategies and Effectiveness in Manufacturing Sectors
Ikilidih, Joy N. Dibua, Ekene C., and Bala M. Adejoh,
Abstract
This study investigate the effectiveness of financial modeling techniques reducing inventory cost within the manufacturing sector, with emphasis on the integration of forecasted analytics, artificial intelligence (AI), and machine learning. Using a systematic literature review and content analysis, the research explained academic journals, conference proceedings, and industry reports published between 2018 and 2024. The methodology rest on predefined inclusion and exclusion criteria to ensure the relevance and quality of the selected literature, followed by a thematic analysis. Key findings reveal that advanced financial modeling significantly enhances demand forecast accuracy, thereby optimizing inventory levels and reducing other costs. The use of AI and machine learning technologies not only streamlines inventory management processes but also enables manufacturers to adapt swiftly to market fluctuations, thus reducing waste and improving operational efficiency. Despite the benefits, challenges such as data quality, technology 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 among other things 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 connectivity technology, integrated sensing and communication capabilities, and block chain in inventory management. Finally, the strategic implementation of advanced financial modeling techniques enhances competitiveness, achieve cost efficiencies, and navigate the complexities of the digital era in inventory management.
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