• Shiva kumar Ramavath,
• Dr. Munish Kumar

In recent years, accurate forecasting of supply chain retail data has become essential for efficient inventory management, demand prediction, and overall operational optimization. Traditional time series forecasting models, while effective in certain cases, struggle to capture the intricate dependencies inherent in multivariate supply chain data. This paper presents a novel approach using a Multi-Head Attention (MHA) mechanism to address these challenges. MHA, originally developed for natural language processing tasks, offers a powerful tool for modeling complex interdependencies between various features within multivariate time series. By leveraging multiple attention heads, this mechanism allows the model to simultaneously focus on different temporal patterns and relationships, enhancing its ability to capture both short- and long-term dependencies. Our approach is applied to a dataset comprising key supply chain metrics, such as sales, inventory levels, lead times, and external factors like promotions and seasonal events. Experimental results demonstrate that the MHA-based model significantly outperforms traditional time series models, such as ARIMA and LSTM, in terms of forecasting accuracy. Additionally, the model's ability to handle varying temporal dependencies leads to more robust and reliable predictions. The proposed method provides a scalable and flexible solution for supply chain managers seeking to optimize their forecasting systems, offering enhanced performance through the effective capture of intricate data patterns and interdependencies. This work highlights the potential of Multi-Head Attention in supply chain applications, offering a pathway to improved decision-making and resource allocation.

Multi-Head Attention, multivariate time series forecasting, supply chain data, demand prediction, inventory management, temporal dependencies, attention mechanism, LSTM, ARIMA, forecasting accuracy, machine learning in supply chain.

"A Multi-Head Attention Mechanism for Capturing Complex Dependencies in Multivariate Time Series Forecasting of Supply Chain Retail Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.b261-b279, February-2025, Available :http://www.jetir.org/papers/JETIR2502127.pdf

"A Multi-Head Attention Mechanism for Capturing Complex Dependencies in Multivariate Time Series Forecasting of Supply Chain Retail Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppb261-b279, February-2025, Available at : http://www.jetir.org/papers/JETIR2502127.pdf