Authors - Yohan Ranasinghe, Janice Abeykoon, Samantha Kumara Senavirathna Abstract - Efficient blood supply chain management is a critical global impera tive in healthcare, yet it is consistently hampered by significant post-expiry blood wastage. This issue, prevalent across diverse healthcare systems, represents a considerable loss of a vital and non-substitutable resource, primarily stemming from challenges in accurate demand forecasting and dynamic inventory coordi nation. To address this pervasive problem, this research proposes and validates a novel data-driven framework. The approach leverages a multivariate deep learn ing forecasting model, specifically a Multivariate Long Short-Term Memory (LSTM) network, integrated into a comprehensive platform designed for proac tive inventory management. The model's development and empirical validation utilize historical blood collection and transfusion data (January 2020 – December 2024) from a cluster center of the National Blood Transfusion Service (NBTS) in Sri Lanka, serving as a representative case study to demonstrate real-world applicability. The framework incorporates multivariate factors such as historical transfusion patterns, seasonal variations, and interdependencies between blood groups to generate more accurate demand predictions. The integrated system, de signed to support real-time inventory monitoring, automated near-expiry track ing, and digital blood request and redistribution mechanisms, aims to align blood supply with anticipated demand. The findings of this research demonstrate that this integrated deep learning and inventory optimization framework significantly improves blood stock utilization, minimizes wastage, and enhances the overall efficiency of blood supply systems. It offers a scalable and ethically governed solution, contributing broadly to efforts in sustainable healthcare delivery world wide.
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