Evaluation and Classification Risks of Implementing Blockchain in the Drug Supply Chain with a New Hybrid Sorting Method

Abstract

In blockchain technology, all registered information, from the place of production of the product to its point of sale, is recorded as permanent and unchangeable, and no intermediary has the ability to change the data of other members and even the data registered by them without public consensus. In this way, users can trust the accuracy of the data. Blockchain systems have a wide range of applications in the medical and health sectors, from creating an integrated system for recording and tracking patients’ medical records to creating transparency in the drug supply chain and medical supplies. However, implementing blockchain technology in the supply chain has limitations and sometimes has risks. In this study, BWM methods and VIKORSort have been used to classify the risks of implementing blockchain in the drug supply chain. The results show that cyberattacks, double spending, and immutability are very dangerous risks for implementation of blockchain technology in the drug supply chain. Therefore, the risks of blockchain technology implementation in the drug supply chain have been classified based on a literature review and opinions of the experts. The risks of blockchain technology implementation in the supply chain were determined from the literature review.

Çevik Proje Yönetiminde Scrum Takımlarının Başarı Sınıflandırmasına Yönelik Bir ÇKKV Modeli: AHS Bütünleşik TOPSIS-Sort-B

“…Sabbagh ve ark. [33], ilaç tedarik zincirindeki blok zinciri (blockchain) uygulama risklerinin sınıflandırmasında kullanmışlardır. Mahmud ve ark.…”

Blockchain-Based Dynamic Consent and its Applications for Patient-Centric Research and Health Information Sharing: Protocol for an Integrative Review

“…In 2019, Treiblmaier [26] noted that the quality of blockchain research was lagging, and he offered guidelines for designing and publishing case studies to improve evidence quality. Years later, authors still emphasize that blockchain research must have more scientific rigor for the work to be respected [49][50][51]. When addressing blockchain-based dynamic consent literature quality, we will offer deeper insight into gaps, weaknesses, and ways to address these.…”

ARASsort: A new sorting based multiple attribute decision‐making algorithm

“…VIKORsort algorithm was developed and applied by Demir et al (2018). Also, Ocampo and Yamagishi (2021) applied VIKORsort algorithm in sorting touristic sites for perceived COVID‐19 exposure; Sabbagh et al (2021) hybridized the Best Worst Method and VIKORsort for classifying the risks of implementing blockchain in a drug supply chain; Polat et al (2021) analysed raw cow milk classification problem with VIKORsort. Interested readers can find the steps of VIKORsort algorithm and its details in mentioned papers.…”

“…All methods have their own assumption, characteristics, paradigm, and perspective. Outranking methods such as PROMSORT (Araz & Ozkarahan, 2005) and Flowsort (Nemery & Lamboray, 2008) which are based on PROMETHEE, require that the decision‐maker defines preference functions for each attribute and also indifference and preference thresholds. Distance‐based approaches which are extensions of compensatory methods such as TOPSISsort (de Lima Silva et al, 2020; De Lima Silva & de Almeida Filho, 2020; Ocampo et al, 2021; Sabokbar et al, 2016; Yamagishi & Ocampo, 2022) and VIKORsort (Demir et al, 2018; Ocampo & Yamagishi, 2021; Polat et al, 2021; Sabbagh et al, 2021) need to determine positive and negative ideal solutions and measure the distance between each alternative and these ideals. Pairwise comparison‐based approaches relying on subjective judgments of the decision‐makers, need to build a hierarchy showing the relations between attributes and/or alternatives and too many survey questions should be answered by the experts, such as AHPsort (Ishizaka et al, 2012; Toledo et al, 2019), AHPsort II (Labella et al, 2020; Miccoli & Ishizaka, 2017; Xie et al, 2019), some fuzzy AHP sorting methods (Du et al, 2021; Ishizaka et al, 2020; Krejci & Ishizaka, 2018; Xu et al, 2019), and group AHPsort (Assumma et al, 2021; Labella et al, 2021; Lopez & Ishizaka, 2017). …”

“…• Distance-based approaches which are extensions of compensatory methods such as TOPSISsort Sabokbar et al, 2016;Yamagishi & Ocampo, 2022) and VIKORsort (Demir et al, 2018;Polat et al, 2021;Sabbagh et al, 2021) need to determine positive and negative ideal solutions and measure the distance between each alternative and these ideals.…”