IoE-Based Predictive Oxygen Inventory Management
4th International Conference on Communication Systems, Computing and IT Applications, CSCITA 2023
; : 219-224, 2023.
Article
in English
| Scopus | ID: covidwho-2322768
ABSTRACT
The COVID-19 pandemic highlighted a major flaw in the current medical oxygen supply chain and inventory management system. This shortcoming caused the deaths of several patients which could have been avoided by accurate prediction of the oxygen demand and the distribution of oxygen cylinders. To avoid such calamities in the future, this paper proposes an Internet of Everything (IoE) based solution which forecasts the demand for oxygen with 80-85% accuracy. The predicted variable of expected patients enables the system to calculate the requirement of oxygen up to the next 30 days from the initiation of data collection. The system is scalable and if implemented on a city or district level, will help in the fair distribution of medical oxygen resources and will save human lives during extreme load on the supply chain. © 2023 IEEE.
COVID-19; Hypoxia; Internet of Everything; Inventory Management; Recurrent Neural Network (RNN); Inventory control; Supply chains; 'current; Accurate prediction; Data collection; Fair distribution; Human lives; Inventory management systems; Recurrent neural network; Supply chain management system; Recurrent neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
4th International Conference on Communication Systems, Computing and IT Applications, CSCITA 2023
Year:
2023
Document Type:
Article
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