A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Raheel Siddiqui, Muhammad Azmat*, Shehzad Ahmed, Sebastian Kummer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation’s supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt’ s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.
Original languageEnglish
JournalSupply Chain Forum: An International Journal
Early online date5 Sept 2021
Publication statusE-pub ahead of print - 5 Sept 2021

Bibliographical note

© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


  • Forecast
  • combined forecast
  • hybrid forecast
  • supply chain efficiency
  • demand forecasting
  • forecasting technique for integrated systems
  • pharmaceutical industry


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