Title: Algorithmic Approaches to Dynamic Pricing for Real-Time Inventory Management in the FMCG Sector
Authors: Julius Olatunde Omisola, Emmanuel Augustine Etukudoh , Ekene Cynthia Onukwulu and Grace Omotunde Osho
Volume: 9
Issue: 4
Pages: 90-111
Publication Date: 2025/04/28
Abstract:
Dynamic pricing has emerged as a pivotal strategy for optimizing inventory management in the Fast-Moving Consumer Goods (FMCG) sector. This study focuses on developing algorithmic approaches to dynamic pricing that leverage real-time market data, enabling businesses to respond swiftly to fluctuations in demand and supply. The proposed dynamic pricing algorithms integrate various data sources, including competitor pricing, customer behavior, and seasonal trends, to adjust prices in real time. By employing these algorithms, FMCG companies can maximize revenue, minimize excess inventory, and enhance overall market competitiveness. The study outlines a framework for implementing dynamic pricing strategies, emphasizing the importance of data collection and analysis. Key components include automated data gathering from various platforms, data preprocessing for accuracy, and the development of algorithms that facilitate rapid pricing adjustments. The algorithms are designed to consider multiple factors, such as elasticity of demand, inventory levels, and promotional strategies, ensuring that pricing decisions align with broader business objectives. Future research directions will explore the integration of machine learning techniques to refine predictive pricing strategies further. By leveraging historical sales data and market trends, machine learning models can enhance the accuracy of demand forecasting, allowing for more informed pricing decisions. These models will enable FMCG companies to anticipate market shifts and adjust prices proactively, rather than reactively. This research highlights the transformative potential of algorithmic dynamic pricing in the FMCG sector, suggesting that effective implementation can lead to significant improvements in inventory turnover rates and profit margins. By adopting a data-driven approach, companies can optimize their pricing strategies to reflect real-time market conditions, ultimately enhancing customer satisfaction and loyalty. In conclusion, this study provides a comprehensive overview of dynamic pricing algorithms tailored for the FMCG industry, advocating for the integration of machine learning for future pricing innovations. The findings underscore the necessity for FMCG businesses to adopt adaptive pricing strategies to thrive in an increasingly competitive landscape.