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A hierarchical multi-criteria model for analyzing the barriers to Pharma 4.0 implementation in developing countries

Authors :
Akib Zaman
Ismat Jerin
Puja Ghosh
Anika Akther
Salma Sultana Shrity
Ferdous Sarwar
Source :
Healthcare Analytics, Vol 5, Iss , Pp 100334- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Pharmaceutical industries in most developing countries with limited resources are expected to encounter several barriers while incorporating Industry 4.0 to transform into Pharma 4.0. With limited resources, a developing country must prioritize the barriers consider their impacts, and make a resource utilization plan accordingly. In this study, We employed a hierarchical multiple criteria decision analysis (MCDM) technique to identify potential barriers to Pharma 4.0 in developing countries and examine their effects to generate a prioritization inventory. Firstly, we extracted the likely barriers using a systematic literature study and used an expert opinion-based Delphi Method to choose the most pertinent barriers. Subsequently, we analyzed the correlation and influence of the selected barriers on each other by formulating a hierarchical multi-criteria model integrating Interpretive Structural Modelling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). As an outcome, we found three distinct categories of the selected 12 barriers: Prominent (4 of 12), Influencing (5 of 12), and Resulting (3 of 12). The results of this study are intended to assist the government in developing a solid adoption strategy for Pharma 4.0 and supply chain strategists in ensuring optimum resource utilization by resolving the examined barriers during the deployment of Pharma 4.0. The study is the first of its kind to discover barriers to Pharma 4.0 adoption and create hierarchical correlations within the context of the pharmaceutical sector from the perspective of a developing country.

Details

Language :
English
ISSN :
27724425
Volume :
5
Issue :
100334-
Database :
Directory of Open Access Journals
Journal :
Healthcare Analytics
Publication Type :
Academic Journal
Accession number :
edsdoj.5b0c26dd6d1e47a5be060deba42e99f7
Document Type :
article
Full Text :
https://doi.org/10.1016/j.health.2024.100334