Enhancing Supply Chain Efficiency: A Holistic Examination of Hybrid Forecasting Models Employing Mode and PERT Technique as Deterministic Factors

Abstract

Inaccurate forecasts can cause severe financial consequences and disrupt supply chain operations for organisations. This study focuses on the pharmaceutical industry, renowned for its complex supply chain and diverse data attributes. It proposes a novel approach to identify the optimal combination of demand forecasting models that enhance accuracy by leveraging deterministic factors using Mode and PERT. By refining model selection in the pharmaceutical industry, this research aims to improve both forecasting precision and supply chain efficiency. A four-level framework based on deterministic factors is proposed to evaluate the extent of hybrid modelling in demand forecasting, empowering practitioners to make informed decisions even in challenging circumstances. The findings offer decision-makers flexibility in selecting suitable forecasting models and assist in tailoring methods to specific conditions. Furthermore, this research highlights the industry's ability to leverage digital technologies and transform existing forecasting methodologies, ensuring uninterrupted business operations during disruptions such as the COVID-19 pandemic.

Publication DOI: https://doi.org/10.1080/13675567.2023.2280094
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: © 2023 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. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Uncontrolled Keywords: Advanced forecasting techniques,Demand forecasting,Forecasting accuracy,Hybrid forecasting,Inventory optimization,Pharmaceutical supply chain,Management of Technology and Innovation,Business and International Management,Management Information Systems,Strategy and Management,Management Science and Operations Research
Publication ISSN: 1469-848X
Last Modified: 07 Oct 2024 07:53
Date Deposited: 10 Nov 2023 09:38
Full Text Link:
Related URLs: https://www.tan ... 67.2023.2280094 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-11-08
Published Online Date: 2023-11-08
Accepted Date: 2023-11-01
Authors: Azmat, Muhammad (ORCID Profile 0000-0002-8894-3737)
Siddiqui, Raheel

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