scholarly journals A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 852 ◽  
Author(s):  
Khizar Abbas ◽  
Muhammad Afaq ◽  
Talha Ahmed Khan ◽  
Wang-Cheol Song
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Pharmaceutical Industry ◽  
Recommendation System ◽  
Chain Process ◽  
Drug Supply ◽  
Counterfeit Drugs ◽  
Chain Management ◽  
Drug Supply Chain

From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.

scholarly journals Block Chain Based Supply Chain Management for Counterfeit Drugs in Pharmaceutical Industry

2021 ◽  
pp. 100-108
Author(s):  
Lokesh M R ◽  
Suhail Ahmed M N ◽  
Saifulla Khan
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Pharmaceutical Industry ◽  
Chain Process ◽  
Drug Supply ◽  
Counterfeit Drugs ◽  
Chain Management ◽  
Supply Chain Process ◽  
Block Chain ◽  
Drug Supply Chain

This paper provides a detailed overview of the block chain technology and how it can be used to build a foolproof system in eliminating counterfeit products in pharmaceutical industries. From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analysed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The block chain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a block chain based drug supply chain management. Our proposed system block chain-based drug supply chain management for consumers. In this drug supply chain management the system is deployed using Hyper ledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. Finally, we also perform several tests to check the efficiency and usability of our proposed system.

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

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