Abstract
The pharmaceutical supply chain is composed of multiple firms interacting to produce and distribute drugs in an uncertain environment. In this work, we develop and validate a multi-agent simulation of the supply chains associated with the pharmaceutical industry. We demonstrate that the operating norms of a particular industry can be accurately represented to create an industry-specific model capable of tracing its evolution. Our model is initialized using 1982 financial data with 30 manufacturers, 60 suppliers, and 60 distributors. Three types of drugs, blockbusters, medium and small, with a 12-year lognormal product life cycle are released by manufacturers. Each quarter the distributors bid for future market share of the released products, and the suppliers bid for acceptable margins. Mergers and acquisitions, based on assets and expected profitability, are allowed at each level. One thousand replications, each lasting the equivalent of 39 years, are used to validate the model.
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References
Arunachalam R and Sadeh NM (2005). The supply chain trading agent competition. Electronic Commerce Research & Applications 4(1): 63–81.
Balci O and Ormsby WF (2007). Conceptual modeling for designing large-scale simulations. Journal of Simulation 1(3): 175–186.
Barbuceanu M, Teigen R and Fox MS (1997). Agent based design and simulation of supply chain systems. Proceedings of the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, IEEE Computer Society Press: Los Alamitos, CA, pp 36–42.
Congressional Budget Office (CBO) (2006). Congress of the United States. Congressional budget office. Research and development in pharmaceutical industry. Washington: US government printing office, http://www.fdareview.org/approval_process.shtml.
Cockburn I and Henderson R (2001). Scale and scope in drug development: Unpacking the advantages of size in pharmaceutical research. Journal of Health Economics 20(6): 1033–1057.
Danzon PM, Nicholson S. and Epstein AJ (2007). Mergers and acquisitions in the pharmaceutical industry. Managerial and Decision Economics Special Issue: Economic and Policy Issues in the Pharmaceutical Industry 28(4–5): 307–328.
Danzon PM, Nicholson S and Pereira NS (2005). Productivity in pharmaceutical-biotechnology R&D: The role of experience and alliances. Journal of Health Economics 24(2): 317–339.
DiMasi JA, Hansen RW and Grabowski HG (2003). The price of innovation: new estimates of drug development costs. Journal of Health Economics 22(2): 151–185.
DiMasi JA, Hansen RW, Grabowski HG and Lasagna L (1991). Cost of innovation in the pharmaceutical industry. Journal of Health Economics 10(2): 107–142.
Grabowski HG, Vernon J and DiMasi JA (2002). Returns on research and development for 1990s new drug introductions. PharmacoEconomics 20(Suppl 3): 11–29.
Grabowski HG and Vernon JM (1994). Returns to R&D on new drug introductions in the 1980s. Journal of Health Economics 13(4): 383–406.
Henderson R and Cockburn I (1996). Scale, scope, and spillovers: Determinants of research productivity in the pharmaceutical industry. RAND Journal of Economics 27(1): 32–59.
Higgins MJ and Rodriguez D (2006). The outsourcing of R&D through acquisitions in the pharmaceutical industry. Journal of Finance & Economics 80(2): 351–383.
Pharmaceutical research and manufacturers of America (2006). Pharmaceutical industry profile, http://www.phrma.org/files/2006%20Industry%20Profile.pdf.
Rossetti CL, Handfield RB and Dooley K (2011). Forces, trends, and decisions in pharmaceutical supply chain management. International Journal of Physician Distribution and Logistics Management 41(6): 607–622.
Sadeh NM et al (2003). TAC’03: A supply chain trading competition. AI Magazine 24(1): 83–91.
Siebers P, Aickelin U, Celia H and Clegg C (2007). A multi-agent simulation of retail management practices. In: Proceedings of the 2007 Summer Computer Simulation Conference, San Diego, California, 16–19 July, Summer Computer Simulation Conference Society for Computer Simulation International: San Diego, CA, pp 959–966.
Solo K and Paich M (2004). A modern simulation approach for pharmaceutical portfolio management. International Conference on Health Sciences Simulation, San Diego, California, USA, http://www.simnexus.com/SimNexus.PharmaPortfolio.pdf.
Swaminathan JM, Smith SF and Sadeh NM (1998). Modeling supply chain dynamics: A multiagent approach. Decision Sciences 29(3): 607–632.
Wellman MP, Greenwald A and Stone P (2007). Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition. MIT Press: Cambridge, MA.
Yonghui F, Piplani R, de Souza R and Jingru W (2000). Multi-agent enabled modeling and simulation towards collaborative inventory management in supply chains. Simulation Conference Proceedings, Winter 2: 1763, 1771, Vol. 2.
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© 2014 Operational Research Society
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Jetly, G., Rossetti, C.L., Handfield, R. (2014). A multi-agent simulation of the pharmaceutical supply chain. In: Taylor, S.J.E. (eds) Agent-Based Modeling and Simulation. The OR Essentials series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137453648_8
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DOI: https://doi.org/10.1057/9781137453648_8
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-49773-7
Online ISBN: 978-1-137-45364-8
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