PREDICTIVE ANALYTICS AND DEMAND FORECASTING: APPLYING AI TO PREDICT FUTURE DEMAND, OPTIMIZE INVENTORY MANAGEMENT AND STREAMLINE SUPPLY CHAIN LOGISTICS BASED ON HISTORICAL DATA AND CONSUMER BEHAVIOR PATTERNS WITH TESTING BY ODOO ERP

In the contemporary business environment, which is increasingly characterized by rapid digital transformation and the accumulation of vast amounts of data, predictive analytics and artificial intelligence (AI) have emerged as pivotal technologies driving significant changes in the way enterprises operate. Organizations across industries are facing unprecedented challenges, including fluctuating consumer demand, complex supply chains, and heightened competition, which necessitate the adoption of advanced analytical tools that can not only interpret historical data but also anticipate future trends. Predictive analytics, powered by AI algorithms, provides businesses with the ability to extract actionable insights from complex datasets, enabling more informed decision-making, minimizing risks associated with uncertainty, and optimizing resource allocation across various operational domains. The growing integration of AI into business intelligence systems represents a paradigm shift from reactive to proactive management strategies, allowing organizations to anticipate market needs and respond with agility. This paper focuses specifically on the integration of AI-driven demand forecasting within Odoo ERP, a widely used open-source enterprise resource planning platform. By embedding advanced forecasting models directly into ERP systems, organizations can streamline their operational workflows, reduce inefficiencies, and enhance overall productivity. The study evaluates the effectiveness of this integration through rigorous experimental analysis, demonstrating how AI models can interact seamlessly with ERP data to generate accurate and actionable predictions. In particular, this research investigates the performance of three widely recognized forecasting models—ARIMA, Random Forest, and Long ShortTerm Memory (LSTM) neural networks. ARIMA, a classical time-series model, is evaluated for its ability to capture linear trends and seasonal variations, while Random Forest, a machine learning ensemble technique, is analyzed for its capacity to model nonlinear interactions and complex relationships among multiple demand drivers. LSTM networks, representing deep learning approaches, are leveraged to detect long-term dependencies and temporal patterns in the data that traditional methods often fail to capture. The results of the study demonstrate that integrating these AI-driven models into Odoo ERP significantly improves forecasting accuracy compared to traditional approaches, with measurable benefits for inventory management, supply chain optimization, and strategic planning. Quantitative outcomes are presented through comparative tables and prediction plots, providing clear visual evidence of the enhanced performance of AI-based models. Furthermore, empirical examples from diverse industry contexts— including retail chains managing fluctuating customer demand, manufacturing firms optimizing raw material procurement, and logistics companies coordinating regional shipment schedules—illustrate the International Journal of Grid Computing &amp; Applications (IJGCA) Vol.16, No.4, December 2025 18 transformative potential of AI-integrated ERP systems. These case studies underscore how predictive analytics can reduce stockouts, prevent overproduction, optimize transportation routes, and ultimately drive cost savings and improved customer satisfaction. Beyond the immediate operational advantages, this research highlights the broader strategic implications of AI-enabled demand forecasting. By incorporating predictive models into ERP platforms, businesses can achieve more responsive and resilient supply chains, make data-driven inventory decisions, and support scenario planning for a variety of market conditions. The integration also facilitates real-time monitoring of demand patterns, enabling adaptive responses to sudden changes, such as promotional events, seasonal peaks, or unexpected market disruptions. In addition, embedding AI capabilities within ERP systems democratizes access to sophisticated analytical tools, allowing small- and medium-sized enterprises (SMEs) to leverage cutting-edge forecasting techniques that were previously available only to large corporations with dedicated analytics teams. Overall, this study demonstrates that AI-driven predictive analytics is not merely a technological enhancement but a strategic enabler for modern enterprise management. By combining historical data analysis, advanced modeling techniques, and ERP system integration, organizations can achieve higher operational efficiency, improved decision-making, and competitive advantage in a complex and rapidly evolving market landscape. The findings presented in this paper provide a foundation for future research and practical implementation, encouraging further exploration of hybrid models, real-time adaptive forecasting, and cross-industry applications to maximize the benefits of AI in enterprise operations.