Abstract
This paper presents a novel application of operations research, data mining and geographic information-systems-based analytics to support decision making in blood supply chain management. This, blood reserve availability assessment, tracking, and management system (BRAMS), research project has been funded by the Office of the Secretary of Defense. (This DoD funded SBIR project is performed by the researchers at Knowledge Based Systems, Inc. (KBSI).) The rapidly increasing demand, criticality of the product, strict storage and handling requirements, and the vastness of the theater of operations, make blood supply-chain management a complex, yet vital problem for the department of defense. In order to address this problem a variety of contemporary analytic techniques are used to analyze inventory and consumption patterns, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The current implementation of the system is being actively used by 130 mangers at different levels in the supply chain including facilities at Osan Air Force Base in South Korea and Incirlik Air Force Base in Turkey.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
ASBP (2008). Armed Services Blood Program. Accessed on 12/17/2007 at http://www.militaryblood.dod.mil/.
Bolstad, P. (2005). GIS Fundamentals: A first text on geographic information systems (2nd ed.). White Bear Lake: Eider Press.
BRAMS (2007). Blood reserve availability assessment, tracking, and management system. Phase II Year 1 Final Report. Knowledge Based Systems Inc., OSD SBIR Contract Number W81XWH-05-C-0074.
Brodheim, E., & Prastacos, G. P. (1979). The Long Island blood distribution system as a prototype for regional blood management. Interfaces, 9(5), 3–20.
Chapman, J. (2007). Unlocking the essentials of effective blood inventory management. Transfusion, 47, 190–196.
Cios, K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26, 1–24.
Delen, D., Hardgrage, B. C., & Sharda, R. (2007). RFID for better supply-chain management through enhanced information visibility. Production and Operations Management, 16(5), 613–624.
Evans, J. (2008). Tracking the nation’s blood supply. The Daily Cardinal, at http://www.dailycardinal.com/article/2254 (accessed on 9/19/2008).
Hammer, P. L., & Bonates, T. O. (2006). Logical analysis of data—an overview: From combinatorial optimization to medical applications. Annals of Operations Research, 248, 203–225.
Katsaliaki, K., & Brailsford, S. C. (2007). Using simulation to improve the blood supply chain. Journal of the Operations Research Society, 58, 219–227.
Pierskalla, W. P., & Roach, C. D. (1972). Optimal issuing policies for perishable inventory. Management Science, 18(11), 603–614.
Prastacos, G. P. (1984). Blood inventory management: An overview of theory and practice. Management Science, 30(7), 777–800.
Rytila, J. S., & Spens, K. M. (2006). Using simulation to increase efficiency in blood supply chains. Management Research News, 29(12), 801–819.
Sapountzis, C. (1984). Allocating blood to hospitals from a central blood bank. European Journal of Operational Research, 16(2), 157–162.
Sime, S. L. (2005). Strengthening the service continuum between transfusion providers and suppliers: Enhancing the blood services network. Transfusion, 45, 206–223.
Sullivan, P. (2005). Developing an administrative plan for transfusion medicine—a global perspective. Transfusion, 45, 224–240.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Delen, D., Erraguntla, M., Mayer, R.J. et al. Better management of blood supply-chain with GIS-based analytics. Ann Oper Res 185, 181–193 (2011). https://doi.org/10.1007/s10479-009-0616-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-009-0616-2