TIMO. Amsterdam. Jan van der Wal. May 31, 2013
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1 TIMO Amsterdam May 31, 2013 Jan van der Wal
2 TIMO Thrombocytes Inventory Management Optimizer A Decision Support System
3 Overview History Platelets Blood Bank FIFO LIFO Compatibility Hospital Red Cells
4 History PhD Rene Haijema (UVA) Cees Smit Sibinga Wim de Kort Eric Jansen UVA Nico van Dijk Nikky Kortbeek Michiel Janssen
5 Platelets (Perishable) Short shelf life in relation to demand Unlimited shelf life Simple strategies If perishable, optimal strategies are highly complex
6 Decision structure: 24 hour model (simplification) At time T (e.g am) stocks are counted Next the production decision is taken (based on replenishment level and current stock) During the day platelets are issued to the hospitals The next day these pools are available
7 Back to complexity (Shelf life 7 days) State (situation) can be written as (n 1 n 2 n 3 n 4 n 5 n 6 n 7 ) States (20, 10, 10, 10, 20, 20, 20) and (20, 20, 20, 20, 10, 10, 10) are different
8 Fortunately Using only the total inventory one can obtain already a very good strategy (See: Thesis Haijema)
9 Therefore, TIMO computes The best Order-Up-To strategy (S Mo S Tu S We S Th S Fr S Sa S Su )
10 The Netherlands Compatibility (Red Cell) Mainly buffy coats (> 90 percent) Uses buffy coats from 60 to 70 percent of the WB (T&B) donations
11 Using only 60 to 70 percent implies An ample supply of buffy coats to make pools The freedom to choose, so blood groups O and A That compatibility allows full aggregation Verified in earlier (very detailed) simulations. Also depends on the size and details of the Dutch blood bank case. See thesis Haijema.
12 Focus: Central bloodbank Production Blood bank Demand
13 Focus: Bloodbank Stock keeping at the blood bank Production at the blood bank All hospital demand satisfied by the blood bank Only statistical information! mean and standard deviation for every day of the week no information about what will happen in the hospitals tomorrow
14 TIMO: Input data? Demand: mean and standard deviation for each day of the week Shelf life Production capacity for each day Issuing policy, e.g., FIFO Costs per shortage and per outdate
15 TIMO
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22 LIFO instead of FIFO
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25 More random demand
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28 So we have seen that TIMO Computes the order-up-to strategies Gives detailed insight into performance Also helps you to live with uncertainties
29 TIMO can also quantify the value of a longer shelf life additional capacity in and around weekend
30 Compatibility (Blood Bank) Example: Total demand 600 per week Very small (AB neg) 1%: 6 per week Small (B neg, AB pos) 3%: 18 per week Medium (A neg, B pos, O neg) 8%: 48 per week
31 Costs: Shortage is a lesser problem if it can be solved with a compatible blood group. Outdating for a small blood group is only a small part of the total outdating. Let us choose: Costs shortage 10 Costs outdating 100
32 Start with very small demand (1%)
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36 Continue with small demand (3 %)
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39 Next consider medium demand (8 %) (Shortage costs again at 10)
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42 Results Per week Outdating % Shortages % 6 (AB-) (AB+,B-) (B+,A-,O-)
43 So, for (the scale of) this example For very small demand one gets a few percent outdating and many compatible deliveries For small demand most of the deliveries will be identical For medium demand outdating is almost zero with almost only identical deliveries
44 Hospital
45 Hospital Differences? Demand low, thus more variability Shelf life in practice shorter Shortages are a lesser problem An order is available almost immediately
46 Back to the Blood Bank in order to deal with Hospital inventories
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53 Red Cells Hospital The same Easy Blood Bank, Production different Ok if some donors are very flexible
54 INFORMS 2011
55 Publications Dijk, N.M. van, R. Haijema, J. van der Wal, C. Smit Sibinga (2009), Blood platelet production: a novel approach for practical optimization, Transfusion 49: W. de Kort, M. Janssen, N. Kortbeek, N. Jansen, J. van der Wal and N.M. van Dijk (2011), Platelet pool inventory management: theory meets practice, Transfusion 51: Several publications in Operations Research journals
56 Coefficient of variation (cv) and shelflife cv cv Risk of outdating in % Shelflife Risk of shortage in % Shelflife Hamburg example.inp
One of the most fragile products of the blood
TRANSFUSION PRACTICE Platelet pool inventory management: theory meets practice_3190 2295..2303 Wim de Kort, Michiel Janssen, Nikky Kortbeek, Naud Jansen, Jan van der Wal, and Nico van Dijk BACKGROUND:
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