Supply Chain management with Demand Forecasting of Covid-19 Vaccine using Blockchain and Machine Learning
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This document proposes a model using blockchain and machine learning for supply chain management and demand forecasting of COVID-19 vaccines. The objectives are to determine vaccine distribution by location and time, and ensure transparency. Challenges include integrating the technologies, data privacy, and getting competitors to share data. The proposed model focuses on geographical forecasting and not vaccine efficacy. Future work includes implementing the model and comparing machine learning algorithms for forecasting.
Supply Chain management with Demand Forecasting of Covid-19 Vaccine using Blockchain and Machine Learning
- 1. Paper ID: 419 - Supply Chain Management with Demand Forecasting of Covid-19 Vaccine using Blockchain and Machine Learning Md. Mahfujur Rahman,
- 2. CONTENTS ❏ Introduction ❏ Research Objective ❏ Motivation ❏ Model Architecture ❏ Open Research Challenges ❏ Limitation and Future Research Direction ❏ Conclusion
- 3. Introduction ❖ Vaccination of the global population against COVID-19 is one of the challenging tasks in supply chain management. ❖ It’s effectiveness depends on the availability of an operational and transparent distribution chain that can be audited by all related stakeholders ❖ In this paper, the necessity of Blockchain and Machine Learning in supply-chain management with demand forecasting of the COVID-19 vaccine has been presented. ❖ The aim is to understand how the convergence of Blockchain technology and ML monitor the prerequisite of vaccine distribution with demand forecasting
- 4. Motivation ❖ To ensure the seamless COVID-19 vaccine distribution with transparency, data integrity, and end-to-end traceability for reducing risk, assuring the safety, and also immutability
- 5. Research Objective ❖ The main objective of this paper is to propose a Blockchain and Machine Learning based model in supply-chain management with demand forecasting of the COVID-19 vaccine ❖ This work attempts to find solution on the following research questions: RQ1: How many vaccines to ship where and when? RQ2: Which vaccine is to supply where and when? RQ3: How to ensure seamless COVID-19 vaccines distribution through Blockchain-based supply chain management? ❖ Lastly, we have discussed research challenges and also mentioning the limitations with future directions.
- 7. Open Research Challenges ❖ Here we have analyzed the challenges that we may face to implement the proposed model. ❖ Integration of Blockchain and ML in the vaccine supply chain is still in its infancy. ❖ Different batches of the covid vaccine may be produced by different contract manufacturers at different facilities, resulting in variations among them, and there may be further issues around the storage of individual lots of vials from within each batch. ❖ Most of the data is subjected to privacy policies and local regulations such as vaccine development. Therefore, most of the data is not easily accessible.
- 8. Open Research Challenges(Cont.) ❖ The majority of the software applications for the supply chain were intended for use within a specific organization. For the proposed model, we need to keep track of every activity as well as participants of the network manufacturers, distributors, hospitals, pharmacies, etc. ❖ Moreover, some of these participants are competitors of each other that are usually resistant to share data, or they may be incapable to share data easily owing to security, regulatory, and compliance concerns. ❖ Building a unique platform to include all the participants/stakeholders in vaccine distribution and demand forecasting is costly and time- consuming.
- 9. ❖ In this paper, initial demand forecasting performance can be low because of limited data in the Blockchain network. ❖ The proposed model only focuses on the most appropriate vaccine forecasting in terms of geographical areas and storage facilities but not the efficacy of the vaccines. ❖ But, while a significant number of people get vaccines then this data can be used for efficacy prediction for the particular vaccine ❖ Proposed model needs to be implemented with a comparative analysis of machine learning algorithms for demand forecasting. Limitations and Future Research Directions
- 10. ❖ Therefore, future work is to focused on collecting COVID-19 vaccine distribution datasets and implement the proposed model for the verification of the model's effectiveness. Limitations and Future Research Directions
- 11. Conclusion ❖ In this paper, Blockchain-ML based model has been proposed for the transparent tracing of COVID-19 vaccine distribution with demand forecasting. ❖ Here, Blockchain technology is used to verify the vaccine distribution process and chain of custody from manufacturing to end-users. ❖ we have applied the Machine Learning model to forecast the most appropriate COVID-19 vaccine based on the available data.