Demand forecasting in pharmaceutical supply chains: A case study

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@Article{MERKURYEVA:2019:procs,

• author = "Galina Merkuryeva and Aija Valberga and Alexander Smirnov",
• title = "Demand forecasting in pharmaceutical supply chains: A case study",
• journal = "Procedia Computer Science",
• year = "2019",
• volume = "149",
• pages = "3--10",
• note = "ICTE in Transportation and Logistics 2018 (ICTE 2018)",
• keywords = "genetic algorithms, genetic programming, Demand forecasting, Phamathetical supply chain, Logistics, Multiple linear regression, Symbolic regression",
• ISSN = "1877-0509",
• URL = "http://www.sciencedirect.com/science/article/pii/S1877050919301061",
• DOI = "doi:10.1016/j.procs.2019.01.100",
• size = "8 pages",
• abstract = "Demand forecasting plays a critical role in logistics and supply chain management. State-of-art methods and key challenges in demand forecasting for the pharmaceutical industry are discussed. An integrated procedure for in-market product demand forecasting and purchase order generation in the pharmaceutical supply chain is described. A case study for supply of pharmaceutical products from a wholesaler to a distribution company located in an emerging market is presented. Alternative forecasting scenarios for thebaseline demand calculations using the SMA model, multiple linear regressions and symbolic regression with genetic programming are experimentally investigated, and their practical implicationsare discussed",

}

Genetic Programming entries for Galina Merkurjeva Aija Valberga Alexander V Smirnov

Citations