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. 2023 Dec 15;13(1):22287.
doi: 10.1038/s41598-023-49606-z.

Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach

Wai Peng Wong  1 , Pui San Saw  2 , Suriyan Jomthanachai  3 , Leong Seng Wang  2 , Huey Fang Ong  4 , Chee Peng Lim  5
Affiliations

Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach

Wai Peng Wong et al. Sci Rep. .

Abstract

One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Pharmaceutical supply chain and the main processes, modified from Saha et al.11 and Musamih et al. (2021).
Figure 2
Figure 2
The conceptual methodology of a digitalized supply chain using a supply risk management approach.
Figure 3
Figure 3
The membership functions of O-factor, (a) = input and (b) = output.
Figure 4
Figure 4
The membership functions of S-factor, (a) = input and (b) = output.
Figure 5
Figure 5
The membership functions of D-factor, (a) = input and (b) = output.
Figure 6
Figure 6
An example of defuzzification methods of SOM, MOM, and LOM.
Figure 7
Figure 7
The supply chain structure of the case study.
Figure 8
Figure 8
The cross-efficiency of manufacturer node.
Figure 9
Figure 9
The cross-efficiency of distributor node.
Figure 10
Figure 10
The cross-efficiency of pharmacy node.
Figure 11
Figure 11
Cluster of Pharma 4.0 main technologies related to the failure mode and its cross-efficiency score using the hierarchical cluster analysis method.
Figure 12
Figure 12
Framework to incorporate digital technologies in the pharmaceutical supply chain.
Figure 13
Figure 13
Integration of digital technologies in the pharmaceutical supply chain.
See this image and copyright information in PMC

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