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An intelligent collaborative environment for sharing information in a blood supply network

Published: 25 October 2015 Publication History

Abstract

This paper presents a proposal of a collaborative model for a supply chain of a blood bank, which uses the concept of Collective Intelligence to integrate the various actors, which include donors, hospitals, clinics and government. They need to share information about: (i) types of blood in supply channel, (ii) production and inventory needs of the Blood Center process; (iii) blood component needs in the demand channel, and, as well as, (iv) financial resources from supporting entities of society, like government. The paper reviews the aspects of the blood supply chain of a Blood Center, describing the aspects of the interrelated customer-supplier relationship. Then the blood components related to forecasting demanded by downstream of the chain are introduced. We shortly discuss its impact on the inventory levels of the Blood Center, and the third part includes a brief discussion on mechanisms to make this chain a more collaborative Ecosystem with the use of Collective Intelligence.

References

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Armstrong, J. S. 1985. Long range forecasting: from crystal ball to computer, John Wiley, New York
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Beliën, J., & Forcé, H. 2012. Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1--16.
[3]
Andres, F., Silva Filho, O. S., and Cezarino W. 2013. Anatomy of a Collective Intelligence Blood Supply Chain. Proceeding MEDES '13 Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems, pages 309--313, ACM New York, NY.
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Van Dijk, N., Haijema, R., Van der Wal, J., and Sibinga, C. S. 2009. Blood platelet production: a novel approach for practical optimization, Transfusion, Volume 49, pages 411--420.
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Gardner, Jr., E. S. 1979. Box-Jenkins vs. multiple regression: some adventures in forecasting the demand for blood tests, Interfaces, Vol. 9, No. 4, pp. 49--54.
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Chatfield, C. 2004. The Analysis of Time Series: An Introduction, Sixth Edition, Chapman & Hall/CRC.
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Silva Filho, O. S.; Carvalho, M. A.; Cezarino, W.; Da Silva, R. S.; Salviano, G. R. 2013. Demand Forecasting for Blood Components Distribution of a Blood Supply Chain, Management and Control of Production and Logistics, Volume # 6 | Part# 1, IFAC Papers Online, Elsevier.
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Chapman, J. F., C. Hyman, and R. Hick. 2004. Blood inventory management, Vox Sang, 87, pp. 143--145
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Pereira A. 2005. Blood inventory management in the type and screen era, Vox Sang., Vol. 89, N. 4, page:245--250
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Bowerman B. L. & O'Connell, R. T. 1987. Time Series Forecasting: Unified Concepts and Computer Implementation, Duxbury Press, Boston.
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Box, G. E. P. & G. M Jenkins. 1994. Time Series Analysis-Forecasting and Control, Holden-Day, San Francisco, (Third Edition)
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Silva Filho, O.; Silva, R. S.; Carvalho, M. A: SARIMA identification models for demand forecasting in a Blood Center. In portuguese: XVI Simpósio de Administração da Produção, Logística e Operações Internacionais., 2013, São Paulo. Anais do XVI SIMPOI, 2013.

Cited By

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  • (2022)Horizontal collaboration in a decentralised system: Indonesian blood supply chainSupply Chain Forum: An International Journal10.1080/16258312.2022.216128724:3(334-350)Online publication date: 25-Dec-2022

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    MEDES '15: Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems
    October 2015
    271 pages
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • IFSP: Federal Institute of São Paulo

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    New York, NY, United States

    Publication History

    Published: 25 October 2015

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    Author Tags

    1. blood supply chain
    2. cloud computing
    3. collaboration
    4. collective intelligence
    5. forecasting

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    Overall Acceptance Rate 267 of 682 submissions, 39%

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    • (2022)Horizontal collaboration in a decentralised system: Indonesian blood supply chainSupply Chain Forum: An International Journal10.1080/16258312.2022.216128724:3(334-350)Online publication date: 25-Dec-2022

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