International Journal of Science, Engineering and TechnologyAuthors: Dhanusha Mol K P, Dr. S. Ilankumaran
Abstract: Supply chains around the world have been becoming more prone to various forms of disruptions from pandemics to geopolitical tensions, and it has revealed the inadequacy of existing forecasting methods. Successful demand forecasting is the foundation of a resilient supply chain, allowing for strategic inventory management and capacity planning. This study develops an advanced demand forecasting system based on deep learning that leverages a Temporal Fusion Transformer (TFT) and multiple sources of external data such as weather information, economic statistics, social media trends, and supply chain disruptions. Using the data collected for five years (2019-2025) from the multinational retail supply chain that amounts to 50 million SKU-location-weeks, the TFT model demonstrates WAPE = 12.4% when making forecasts for four weeks ahead, surpassing other forecasting models (ARIMA – 24.8%, XGBoost – 18.2%, and LSTM – 15.6%). Moreover, the developed system features a unique disruption-aware training process that increases forecast precision during disruptions by 28%. When tested in conjunction with a multi-echelon inventory management system, the forecasting system was able to cut the amount of safety stocks by 19% and improve on-time delivery performance by 31%.
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