Demand forecasting in pharmaceutical supply chains: A case study

TLDR

Demand forecasting plays a critical role in logistics and supply chain management. The paper discusses state‑of‑the‑art methods and key challenges in demand forecasting for the pharmaceutical industry. The authors describe an integrated procedure for in‑market product demand forecasting and purchase order generation, and experimentally evaluate alternative forecasting scenarios—including SMA, multiple linear regression, and symbolic regression with genetic programming—to assess their practical implications. The case study demonstrates the application of the integrated procedure in an emerging market, showing how alternative forecasting scenarios affect demand predictions and purchase order generation.

Abstract

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 implicationsare discussed.