International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Next-Generation Demand Sensing and Forecasting

Author(s) Sandeep Ramanamuni
Country United States
Abstract The dynamic landscape of global logistics businesses and supply chains necessitates the need for innovative solutions. It demands solutions to tackle the challenges in demand forecasting and to optimize inventory. The traditional methods have constraints such as limited scalability and adaptability and the inability to manage modern supply chains. However, artificial intelligence has emerged as a transformative force to enable demand sensing and predictive management through advanced data analytics. Using machine learning algorithms and real-time decision-making capabilities, AI nourishes demand forecasting in modern supply chains. It leverages real-time data through AI-driven tools to forecast demand patterns accurately. It also helps mitigate excess inventory, avoid stockouts, and enhance overall operational efficiency.

AI-driven systems study historical data, market trends, and external factors. They study the fluctuations in the economy and weather changes to generate precise forecasts. The tools enhance responsiveness by uncovering anticipatable disruptions. This helps businesses to adopt proactive measures to ensure the supply chain's resilience. AI further allows the integration of supply chain nodes for collaboration and data-driven insights that have never been possible before. From demand forecasting and predictive analytics to intelligent automation in inventory management, AI-enabled tools are transforming the traditional supply chain model. The paper studies the transformative impact of AI on predictive supply chain management and its practical applications.
Field Engineering
Published In Volume 5, Issue 1, January-February 2023
Published On 2023-02-04
DOI https://doi.org/10.36948/ijfmr.2023.v05i01.40765
Short DOI https://doi.org/g9bvd7
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E-ISSN 2582-2160
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