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

Abstract

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.

Publication DOI: https://doi.org/10.1080/16258312.2021.1967081
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
College of Engineering & Physical Sciences
Additional Information: © 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.
Uncontrolled Keywords: Forecast,combined forecast,hybrid forecast,supply chain efficiency,demand forecasting,forecasting technique for integrated systems,pharmaceutical industry
Full Text Link:
Related URLs: https://www.tan ... 12.2021.1967081 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-09-05
Published Online Date: 2021-09-05
Accepted Date: 2021-08-08
Authors: Siddiqui, Raheel
Azmat, Muhammad (ORCID Profile 0000-0002-8894-3737)
Ahmed, Shehzad
Kummer, Sebastian

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