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A robust autonomous method for blood demand forecasting

BACKGROUND: Blood supply chain management requires estimates about the demand of blood products. The more accurate these estimates are, the less wastage and fewer shortages occur. While the current literature demonstrates tangible benefits from statistical forecasting approaches, it highlights issue...

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Autores principales: Turkulainen, Esa V., Wemelsfelder, Merel L., Janssen, Mart P., Arvas, Mikko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325496/
https://www.ncbi.nlm.nih.gov/pubmed/35383944
http://dx.doi.org/10.1111/trf.16870
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author Turkulainen, Esa V.
Wemelsfelder, Merel L.
Janssen, Mart P.
Arvas, Mikko
author_facet Turkulainen, Esa V.
Wemelsfelder, Merel L.
Janssen, Mart P.
Arvas, Mikko
author_sort Turkulainen, Esa V.
collection PubMed
description BACKGROUND: Blood supply chain management requires estimates about the demand of blood products. The more accurate these estimates are, the less wastage and fewer shortages occur. While the current literature demonstrates tangible benefits from statistical forecasting approaches, it highlights issues that discourage their use in blood supply chain optimization: there is no single approach that works everywhere, and there are no guarantees that any favorable method performance continues into the future. STUDY DESIGN AND METHODS: We design a novel autonomous forecasting system to solve the aforementioned issues. We show how possible changes in blood demand could affect prediction performance using partly synthetic demand data. We use these data then to investigate the performances of different method selection heuristics. Finally, the performances of the heuristics and single method approaches were compared using historical demand data from Finland and the Netherlands. The development code is publicly accessible. RESULTS: We find that a shift in the demand signal behavior from stochastic to seasonal would affect the relative performances of the methods. Our autonomous system outperforms all examined individual methods when forecasting the synthetic demand series, exhibiting meaningful robustness. When forecasting with real data, the most accurate methods in Finland and in the Netherlands are the autonomous system and the method average, respectively. DISCUSSION: Optimal use of method selection heuristics, as with our autonomous system, may overcome the need to constantly supervise forecasts in anticipation of changes in demand while being sufficiently accurate in the absence of such changes.
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spelling pubmed-93254962022-07-30 A robust autonomous method for blood demand forecasting Turkulainen, Esa V. Wemelsfelder, Merel L. Janssen, Mart P. Arvas, Mikko Transfusion Blood Donors & Blood Collection BACKGROUND: Blood supply chain management requires estimates about the demand of blood products. The more accurate these estimates are, the less wastage and fewer shortages occur. While the current literature demonstrates tangible benefits from statistical forecasting approaches, it highlights issues that discourage their use in blood supply chain optimization: there is no single approach that works everywhere, and there are no guarantees that any favorable method performance continues into the future. STUDY DESIGN AND METHODS: We design a novel autonomous forecasting system to solve the aforementioned issues. We show how possible changes in blood demand could affect prediction performance using partly synthetic demand data. We use these data then to investigate the performances of different method selection heuristics. Finally, the performances of the heuristics and single method approaches were compared using historical demand data from Finland and the Netherlands. The development code is publicly accessible. RESULTS: We find that a shift in the demand signal behavior from stochastic to seasonal would affect the relative performances of the methods. Our autonomous system outperforms all examined individual methods when forecasting the synthetic demand series, exhibiting meaningful robustness. When forecasting with real data, the most accurate methods in Finland and in the Netherlands are the autonomous system and the method average, respectively. DISCUSSION: Optimal use of method selection heuristics, as with our autonomous system, may overcome the need to constantly supervise forecasts in anticipation of changes in demand while being sufficiently accurate in the absence of such changes. John Wiley & Sons, Inc. 2022-04-05 2022-06 /pmc/articles/PMC9325496/ /pubmed/35383944 http://dx.doi.org/10.1111/trf.16870 Text en © 2022 Finnish Red Cross Blood Service. Transfusion published by Wiley Periodicals LLC on behalf of AABB. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Blood Donors & Blood Collection
Turkulainen, Esa V.
Wemelsfelder, Merel L.
Janssen, Mart P.
Arvas, Mikko
A robust autonomous method for blood demand forecasting
title A robust autonomous method for blood demand forecasting
title_full A robust autonomous method for blood demand forecasting
title_fullStr A robust autonomous method for blood demand forecasting
title_full_unstemmed A robust autonomous method for blood demand forecasting
title_short A robust autonomous method for blood demand forecasting
title_sort robust autonomous method for blood demand forecasting
topic Blood Donors & Blood Collection
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325496/
https://www.ncbi.nlm.nih.gov/pubmed/35383944
http://dx.doi.org/10.1111/trf.16870
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