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1 Applications of Operational Research to Blood System Management J. Blake 1,2, Matthew Hardy 1,2 1 Dalhousie University 2 Canadian Blood Services.

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Presentation on theme: "1 Applications of Operational Research to Blood System Management J. Blake 1,2, Matthew Hardy 1,2 1 Dalhousie University 2 Canadian Blood Services."— Presentation transcript:

1 1 Applications of Operational Research to Blood System Management J. Blake 1,2, Matthew Hardy 1,2 1 Dalhousie University 2 Canadian Blood Services

2 2 Canadian Blood Services Canadian Blood Services (CBS) is a not-for-profit organization. It manages the blood and blood products supply for Canadians. It operates in all parts of Canada (except Quebec). Succeeds the Canadian Red Cross (operations) and the Canadian Blood Agency (funding). Instituted in 1998 as part of the outcomes of Kreever. CBS is a federally regulated organization (via a branch of Health Canada). It receives funding for its operations from the provinces. –Cost sharing formula. The provinces cannot direct operational decisions of CBS. There has been consistent grumbling about cost amongst the provinces and lack of control One of the more expensive blood systems in the world

3 3 Blood Products in Canada CBS collects approximately 950,000 units of whole blood. 60,000 units of plasma via plasmapheresis 35,000 units of platelets via plateletpheresis. Donations are collected from unpaid volunteers in Canada. Units are provided to qualified users free of charge. Annual cost ~ $920M $CAN (~€670M depending on rate)

4 4 Some Observations At a macro level, CBS looks like a large distributed primary manufacturer: Harvest raw materials from the environment Batch manufacturing processes Extensive quality control system Geographically dispersed distribution network Like all manufacturers, CBS faces a number of challenges: Mature industry – growth is difficult and expensive Stochastic demand – safety stock required to overcome Stochastic supply – even more safety stock required Perishable stock – too much safety stock is bad Network coordination – where to collect, manufacture, distribute

5 5 Halifax St. John’s Moncton Ottawa Toronto / Mississauga London SudburyWinnipeg Regina Saskatoon Calgary Vancouver Edmonton Windsor Kelowna Victoria Prince George The CBS Network Charlottetown Saint John Brandon Hamilton Thunder Bay Sydney

6 6 Logistics Notes Large consumers tend to be teaching hospitals, typically located in urban centres Production sites are predominately located near to large consumers However less than 2% of eligible Canadians donate Donation rates are substantially lower in large urban centres than elsewhere in the country Logistics is an important concern for CBS Saberton, Paez, Newbold & Heddle (2009), IJHG 8:56

7 7 Facilities Redevelopment in Atlantic Canada* CBS collects from a small number of fixed sites & a larger number of mobile clinics throughout four provinces Products are processed at 3 fixed sites: Halifax, Saint John, and Saint John’s Product is shipped from local collection sites (mostly mobile) to local production centres and distributed locally (mostly) Most donor testing, however, takes place in Halifax, with some tests (WNV) taking place in Toronto Halifax Moncton Saint John *a similar project is happening in Ontario

8 8 Facilities Redevelopment Project in Atlantic Canada Current plans call for a re-alignment of facilities In Atlantic Canada, donations will be collected and sent to local centres Samples will be shipped to Toronto for testing Raw materials will be shipped (mostly) to Halifax for production Finished products will be distributed in the Maritimes from Halifax either directly or via distribution hubs (stock holding units) Appears to be a straightforward distribution problem… Halifax St. John’s Moncton Saint John

9 9 However, there’s a wrinkle…

10 10 NB Hospitals

11 11 Current Logistics Arrangement Current transport is strictly ground based Overnight ground courier for regular (low urgency) shipments Bus courier for ad-hoc (medium to high urgency) shipments Taxi and RCMP relay for emergency (high urgency) shipments

12 12 How’s the weather? It seems like a cliché, but the weather is a concern Flights in and out of Halifax are subject to adverse weather conditions (i.e. fog) The road linking NB to NS is also subject to weather About once a year there is a snowfall that delays traffic usually for less than 6 hours In 2008 it was shut for 24 hrs when a truck jack-knifed during an unexpected snow storm (see above) The Canadian Press

13 13 Dartmouth to Saint John: Single Road Issues Cobequid Pass Tantramar Marsh

14 14 Study Approach Statistical comparison of existing & proposed network to NE & NW Transport time Delivery lag Delivery consistency Network reliability Simulation model of Dartmouth-Saint John link Inventory analysis Service level Contingency planning Evaluate Impact

15 15 Physical Test To be run on over four seasons (calendar 2011) Testing regular deliveries via –Ground from Saint John (“current” network) –Air from Dartmouth/Halifax (“to be” network) using regular orders for platelets to four regional centres in NE and NW of province Will use a comparison of means and variance for: –Delivery lag –Variance of delivery lag –Delivery tardiness from target time of arrival –Time of arrival at hospital/lab Expect about 1100 data points

16 16 Simulation Assumptions 1.Demand from all NB facilities included 2.All demand (urgent & non- urgent) is serviced out of the Saint John SHU 3.Only the Dartmouth/Saint John transport link is modelled 4.Demand ABO/Rh is proportional to Canadian population 5.Supply ABO/Rh is proportional to historical collections 6.Substitution by ABO/Rh (red cells) or ABO/CMV (platelets) is permitted 7.At the end of each day any platelet demand may be satisfied by any available supply

17 17 Demand Modelling Volume specific to day of weekArrival specific to hour of day Red Cell Demand 09/10 Platelet Volume 09/10 by Day of Week RBC Demand Arrival 09/10 by Hour

18 18 Supply Modelling – “Current” Platelets Target inventory 13-26 units Daily demand averages 13 units Target collections are 13*(7/6) i.e. prorated for 6 day collection cycle RBC Target inventory 670-690 units Daily demand averages 67 units Target collections are 13*(7/5) i.e. prorated for 5 day collection cycle RBC End Label Distribution 09/10

19 19 Supply Modelling – “To Be” We assume a daily order placed & filled from Dartmouth RBC Target Inventory Platelet Target Inventory ABOAABBO CMV-+-+-+-+ Max Inventory84 2284 Min Inventory84 2284 ABO/RhA-A+AB-AB+B-B+O-O+ Annual Volume146056013124929894425238248 Daily Volume415. Max Inventory361391792463204 (9 Days Supply) Min Inventory321241682156181 (8 Days Supply)

20 20 Supply Modelling - Weather The reliability of the transport link between Halifax/Dartmouth and Saint John is crucial Historical data is available for a commercial service delivering test samples from Saint John to Halifax Summer transit time is about 4.14 hours; winter transit time is about 4.29 hours 98.1% of transit times ≤ 5.5 hours

21 21 Supply Modelling - Weather Wanted to include weather impact on transit Matched delivery times against snowfall (in cm) for weather stations along route between Halifax and Saint John for period 01 Jan 09 - 31 Mar 10 MLR regression on transit time versus snowfall Results were very weak We use empirical transit time data to determine supply arrival at Saint John, supplemented with road closure data from provincial departments of transport The regression equation is Time in Transit = 4.08 + 0.0302 HfxSnow - 0.0340 MncSnow + 0.0037 StJSnow - 0.0143 Month + 0.0389 DofW Predictor Coef SE Coef T P Constant 4.0806 0.1594 25.60 0.000 HfxSnow 0.03023 0.01236 2.45 0.015 MncSnow -0.03399 0.01803 -1.89 0.061 StJSnow 0.00372 0.01439 0.26 0.796 Month -0.014288 0.008699 -1.64 0.102 DofW 0.03887 0.02897 1.34 0.181 S = 0.523705 R-Sq = 4.8% R-Sq(adj) = 2.8%

22 22 Run Time Parameters: Warm Up Inventory level versus run time - RBCInventory level versus run time - Platelets

23 23 Run Parameters: Replication Length* for Batch Means * A 12-month batch length is used for production runs

24 24 Validation: Monthly Volume of Issues (95% Confidence Intervals)

25 25 Validation: Age of Product on Disposition (95% Confidence Intervals)

26 26 Experimental Results: Outdates

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