PMID- 37845382
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231121
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 16
TI  - IoT platforms assessment methodology for COVID-19 vaccine logistics and 
      transportation: a multi-methods decision making model.
PG  - 17575
LID - 10.1038/s41598-023-44966-y [doi]
LID - 17575
AB  - The supply chain management (SCM) of COVID-19 vaccine is the most daunting task 
      for logistics and supply managers due to temperature sensitivity and complex 
      logistics process. Therefore, several technologies have been applied but the 
      complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use 
      case due to its multiple features support like excursion notification, data 
      sharing, connectivity management, secure shipping, real-time tracking and 
      monitoring etc. All these features can only feasible through choosing and 
      deploying the right IoT platform. However, selection of right IoT platform is 
      also a major concern due to lack of experience and technical knowledge of supply 
      chain managers and diversified landscape of IoT platforms. Therefore, we 
      introduce a decision making model for evaluation and decision making of IoT 
      platforms that fits for logistics and transportation (L&T) process of COVID-19 
      vaccine. This study initially identifies the major challenges addressed during 
      the SCM of COVID-19 vaccine and then provides reasonable solution by presenting 
      the assessment model for selection of rational IoT platform. The proposed model 
      applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It 
      also adopts Estimation-Talk-Estimation (ETE) approach for response collection 
      during the survey. As, this is first kind of model so the proposed model is 
      validated and tested by conducting a survey with experts. The results of the 
      proposed decision making model are also verified by Simple Additive Weighting 
      (SAW) technique which indicates higher results accuracy and reliability of the 
      proposed model. Similarly, the proposed model yields the best possible results 
      and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% 
      and 94% respectively. The survey-based testing also suggests that this model can 
      be adopted in practical scenarios to deal with complexities which may arise 
      during the decision making of IoT platform for COVID-19 SCM process.
CI  - (c) 2023. Springer Nature Limited.
FAU - Ali, Yasir
AU  - Ali Y
AD  - Shahzeb Shaheed Government Degree College Razzar, Swabi, Higher Education 
      Department, Peshawar, Khyber Pakhtunkhwa, Pakistan.
FAU - Khan, Habib Ullah
AU  - Khan HU
AD  - Accounting and Information Systems, College of Business and Economics, Qatar 
      University, Doha, Qatar. habib.khan@qu.edu.qa.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231016
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
RN  - 0 (COVID-19 Vaccines)
SB  - IM
MH  - Humans
MH  - COVID-19 Vaccines
MH  - *COVID-19/prevention & control
MH  - *Internet of Things
MH  - Reproducibility of Results
MH  - Decision Making
PMC - PMC10579304
COIS- The authors declare no competing interests.
EDAT- 2023/10/17 00:42
MHDA- 2023/10/23 01:18
PMCR- 2023/10/16
CRDT- 2023/10/16 23:33
PHST- 2023/02/07 00:00 [received]
PHST- 2023/10/13 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/17 00:42 [pubmed]
PHST- 2023/10/16 23:33 [entrez]
PHST- 2023/10/16 00:00 [pmc-release]
AID - 10.1038/s41598-023-44966-y [pii]
AID - 44966 [pii]
AID - 10.1038/s41598-023-44966-y [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 16;13(1):17575. doi: 10.1038/s41598-023-44966-y.