A Study on Predictive Sales Forecasting Models for Inventory Optimization at Reliance Smart, Nagpur

Abstract

Effective sales forecasting plays a critical role in optimizing inventory management, especially in dynamic retail environments such as Reliance Smart, Nagpur. This study investigates the implementation of predictive sales forecasting models and their impact on inventory optimization. By analysing historical sales data and integrating advanced techniques, including machine learning algorithms and statistical tools, the research aims to reduce overstock and stockout scenarios, enhance cost efficiency, and improve customer satisfaction. The study employs a mixed-methods approach, combining quantitative analysis of sales trends with qualitative insights from retail managers and supply chain experts. The findings reveal that adopting data-driven forecasting strategies significantly enhances inventory turnover rates and aligns stock levels with consumer demand. Furthermore, the research identifies key challenges such as data quality, model accuracy, and operational integration, offering actionable recommendations to overcome these obstacles. The practical implications of this research extend to retail businesses seeking to optimize supply chain operations, minimize losses, and strengthen decision-making frameworks. By leveraging predictive analytics, organizations can achieve a balance between maintaining adequate stock and minimizing holding costs, ultimately fostering sustainable growth. This paper underscores the importance of continuous model refinement and cross-functional collaboration in ensuring the reliability and applicability of predictive sales forecasting in the retail sector.