Demand Forecasting in Pharmaceutical Supply Chains: A Case Study
Procedia Computer Science 2019
Gaļina Merkurjeva, Aija Valberga, Alexander Smirnov

Demand forecasting plays a critical role in logistics and supply chain management. In the paper, 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 implications are discussed.


Keywords
Demand forecasting; Phamathetical supply chain; Logistics; Multiple linear regression; Symbolic regression
DOI
10.1016/j.procs.2019.01.100
Hyperlink
https://www.sciencedirect.com/science/article/pii/S1877050919301061?via%3Dihub

Merkurjeva, G., Valberga, A., Smirnov, A. Demand Forecasting in Pharmaceutical Supply Chains: A Case Study. Procedia Computer Science, 2019, Vol. 149, pp.3-10. ISSN 1877-0509. Available from: doi:10.1016/j.procs.2019.01.100

Publication language
English (en)
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