1964
DOI: 10.1111/j.1537-2995.1964.tb02844.x
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Blood Bank Inventory Control

Abstract: A method of statistical analysis of blood bank inventories, intake and blood usage is presented, Future inventories, intake and usage may be predicted on the basis of this analysis. The relationship of demand and usage and the effect of this relationship on outdating is analyzed. This statistical analysis can then be used as a basis for rational decisions as to the size and maintenance of the blood bank inventory to meet demand and to obtain a reasonable outdating percentage. The importance of the National Cle… Show more

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Cited by 16 publications
(4 citation statements)
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“…• Target Inventory Levels and Order Policies: Several models are developed to set inventory levels and determine ordering strategies for whole blood and blood products to achieve some balance between shortages and wastage at the regional and hospital blood bank levels (Brodheim et al 1975(Brodheim et al , 1976Chazan and Gal 1977;Elston and Pickrel 1963;Fontaine et al 2009;Friedman et al 1982;Fries 1975;Haijema et al 2007;Hesse et al 1997;Hurlburt and Jones 1964;Jagannathan and Sen 1991;Jennings 1973;Kaspi and Perry 1983;Katz et al 1983;Kendall 1980;Kopach et al 2008;Ledman and Groh 1984;Nahmias 1976;Pereira 2005;Rytila and Spens 2006;Sirelson and Brodheim 1991;Vrat and Khan 1976). These include (1) simulation models (Abbott et al 1978;Brodheim et al 1976;Jennings 1973), (2) Markov chain models (Brodheim et al 1975;Chazan and Gal 1977;Jagannathan and Sen 1991;Pegels and Jelmert 1970), and (3) dynamic programming models (Fries 1975;Nahmias 1975;Nahmias and Pierskalla 1973;Prastacos 1981).…”
Section: Existing Literature On Blood Supply Chainmentioning
confidence: 99%
“…• Target Inventory Levels and Order Policies: Several models are developed to set inventory levels and determine ordering strategies for whole blood and blood products to achieve some balance between shortages and wastage at the regional and hospital blood bank levels (Brodheim et al 1975(Brodheim et al , 1976Chazan and Gal 1977;Elston and Pickrel 1963;Fontaine et al 2009;Friedman et al 1982;Fries 1975;Haijema et al 2007;Hesse et al 1997;Hurlburt and Jones 1964;Jagannathan and Sen 1991;Jennings 1973;Kaspi and Perry 1983;Katz et al 1983;Kendall 1980;Kopach et al 2008;Ledman and Groh 1984;Nahmias 1976;Pereira 2005;Rytila and Spens 2006;Sirelson and Brodheim 1991;Vrat and Khan 1976). These include (1) simulation models (Abbott et al 1978;Brodheim et al 1976;Jennings 1973), (2) Markov chain models (Brodheim et al 1975;Chazan and Gal 1977;Jagannathan and Sen 1991;Pegels and Jelmert 1970), and (3) dynamic programming models (Fries 1975;Nahmias 1975;Nahmias and Pierskalla 1973;Prastacos 1981).…”
Section: Existing Literature On Blood Supply Chainmentioning
confidence: 99%
“…Based on raw data from the Milwaukee Blood Center (MBC) which has been previously reported,6 the purpose of this paper is to: (1) present a procedure for accurately estimating the EOR of each hospital in the MBC regional blood bank system, and (2) to demonstrate how this rate may be used to compare hospital performance.…”
Section: Eor =mentioning
confidence: 99%
“…where T, (j) is the number of transfusions of k-day old blood that is transfused by the reduced model (that is, ignoring the jth hospital). In order to determine the p, for each hospital, let (1)(2)(3)(4) A be the input E needed by the mathematical model obtained by deleting the jth hospital (b., using B (j) and C (j) to give eT(j) equal to eT-tj, the number of transfusions obtained without hospital j. By a series of successive approximations the p, is found such that…”
Section: Markov Model Of Regional Blood Bank Which Allows Recyclingmentioning
confidence: 99%
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