Saving Lives with Data: How Blood Supply Chain Optimization Reduces Shortages by 7%

DOI

10.2991/978-2-38476-052-7_53How to use a DOI?

Keywords

Blood; Inventory Control; Blood Bag; Red Cross

Abstract

This study aims to predict the demand for blood bags considering their expiration date, to help address the uncertainty and potential shortages in the Red Cross's blood stock. The ARIMA method was used to predict the future demand for blood bags, particularly during the COVID-19 pandemic. The simulation results show a 45% reduction in excess stock and a 7% reduction in stock shortages, indicating that the predictive model can effectively optimize the blood supply chain. These findings have significant implications for humanitarian activities, particularly in times of crisis when the demand for blood is high and uncertain, as it enables better planning and management of blood supply, which ultimately saves lives.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Andreas Handojo
AU  - Tanti Octavia
AU  - Gabriela Consuelo Heriyanto
PY  - 2023
DA  - 2023/05/22
TI  - Saving Lives with Data: How Blood Supply Chain Optimization Reduces Shortages by 7%
BT  - Proceedings of the International Conference on Intellectuals’ Global Responsibility (ICIGR 2022)
PB  - Atlantis Press
SP  - 487
EP  - 494
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-052-7_53
DO  - 10.2991/978-2-38476-052-7_53
ID  - Handojo2023
ER  -