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Learning Supply Chain Management Through Use Of A Business Game

Learning Supply Chain Management Through Use Of A Business Game. Navonil Mustafee Korina Katsaliaki n.mustafee@ swansea.ac.uk | k.katsaliaki@ihu .edu.gr. Facilitating the Analysis of a UK NBS Chain ( Mustafee et al . 2009; Katsaliaki and Brailsford 2007 ).

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Learning Supply Chain Management Through Use Of A Business Game

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  1. Learning Supply Chain Management Through Use Of A Business Game

    Navonil Mustafee KorinaKatsaliaki n.mustafee@swansea.ac.uk | k.katsaliaki@ihu.edu.gr
  2. Facilitating the Analysis of a UK NBS Chain (Mustafee et al. 2009; Katsaliaki and Brailsford 2007) Beer Game (Sterman, 1984; 1989); Mortgage Service Game (Anderson and Morrice, 2000) The Blood Supply Game Mimics supply chain of blood from donors to patients Models material and information flow Perishable product Limited collection/production Sterman, J. (1984), “Instructions for Running the Beer Distribution Game (D-3679), “Sloan School of Management, MIT. Sterman, J. (1989), “Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment,” Management Science, 35, 3, 321–339. Anderson Jr.E.G. and Morrice D.J. A simulation game for teaching service-oriented supply chain management: Does Information Sharing Help Managers with Service Capacity Decisions? Production and Operations Management, 2000, 9 (1): 40-55. Mustafee, N., Taylor, S.J.E., Katsaliaki, K. and Brailsford, S. (2009). Facilitating the Analysis of a UK NBS Chain Using the HLA. SIMULATION: Transactions of the Society of Modelling and Simulation International.Vol 85(2):113-128. Katsaliaki, K., and S. C. Brailsford. 2007. Using Simulation to Improve the U.K. Blood Supply Chain. Journal of the Operational Research Society, 58(2):219-227.
  3. Outdate Katsaliaki, K., and S. C. Brailsford. 2007. Using Simulation to Improve the U.K. Blood Supply Chain. Journal of the Operational Research Society, 58(2):219-227. (adapted from) Mustafee, N., Taylor, S.J.E., Katsaliaki, K. and Brailsford, S. (2009). Facilitating the Analysis of a UK NBS Chain Using the HLA. SIMULATION: Transactions of the Society of Modelling and Simulation International. Vol 85(2):113-128.
  4. The Supplier:The donors who provide the raw material (the unprocessed blood); The Manufacturer:The National Blood Service (NBS) centre which tests, processes and transforms blood into blood products ready for use; The Distributor:The NBS also plays the role of the distributor who has the responsibility to store the product and to transport it to the receiver when an order is placed; Wholesalers:The hospital blood banks which place orders with the NBS, receive products from the NBS, and handle the blood stocks issued to them; Retailers:The doctors at each hospital who place orders for blood products to hospital blood banks (wholesalers) to satisfy the needs of the patients. End Users:The patients (end-user) in need for transfusion.
  5. The pedagogic purpose of the Blood Supply Game is fourfold: To improve understanding of certain aspects of supply chains, such as variant supply and demand and distribution options; To evaluate the overall impact of these principles which is different from the sum of the impact of each one of them; Place the user in the position of managers who have to make real decisions; Familiarisation with a graphical, business-oriented model. The aim of the player is to make such decisions that maximise the ‘profit’ of the NBS; this is related with satisfying as many hospital patients and satisfying as many hospital orders as possible and careful control of NBS stock.
  6. 1 3 3 4 2 2 3 3 3 3 2 4
  7. Weekdays Time Day Max game play: 28 days Click to increment days
  8. STDEV= sqr(905)2: random number from the Normal distribution which is defined by the mean value of 905. Doctors’ orders will vary from week to week. STDEV= sqr(coll)1: random number from the Normal distribution which is defined by the mean value of the player’s choice. PTI weekly collections will vary from week to week. STDEV= sqr(508)3: random number from the Normal distribution which is defined by the mean value of 508. PTI weekly orders will vary from week to week.
  9. Scenario 1 – Assign Stock on Demand
  10. Scenario 1 – Assign Stock on Demand NBS profit calculation (scenario 1)
  11. Scenario 2 – Assign Stock on Demand with Adjusted Collections
  12. Scenario 2 – Assign Stock on Demand with Adjusted Collections NBS profit calculation (scenario 2)
  13. Scenario 3 – Assign Stock Collectively with Adjusted Collections
  14. Scenario 3 – Assign Stock Collectively with Adjusted Collections NBS profit calculation (scenario 3)
  15. Future Work Multi-player version More strategies One day tutorial session
  16. Acknowledgements Mr. Sebastian Kitching Students of EBRM00 HEA (Travel Grant)
  17. Questions
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