scholarly journals Application of Block Chain & Artificial Intelligence in E-commerce supply chain to reduce product counterfeiting & increase transparency

2021 ◽  
Vol 57 (9) ◽  
pp. 6244-6250
Author(s):  
LAKSHIT JAIN, KIRAN KARANDE
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Programming Language ◽  
Data Analytics ◽  
Research Paper ◽  
Product Counterfeiting ◽  
Research Findings ◽  
Block Chain ◽  
Wide Exposure

Product counterfeiting is a major issue in today’s world. These days product counterfeiting leads to around 1.05 lac crore rupees in India per year. As per a report by business standard, if even 50% of product counterfeiting is stopped with authentication, monitored properly and governed, it can save 50,000 crore rupees per year in India itself, then think about whole world. This research paper is based on the use of block chain along with artificial intelligence to curb the problem of product counterfeiting and maintain the transparency. This research paper also demonstrates the use of blockchain using R studio as a platform. R is a programming language mostly for data analytics purposes. So, the implementation of blockchain in R can give a wide exposure to the data to be analyzed and suitable insights and forecasts can also be made. The research findings give a holistic picture of all the variables that affect the supply chain of E-commerce industry. This study helps the supplier side in curbing their loses and also the consumer side in receiving the genuine product that build trust among both the stakeholders

The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Harsh M. Shah ◽  
Bhaskar B. Gardas ◽  
Vaibhav S. Narwane ◽  
Hitansh S. Mehta
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions

PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

scholarly journals Application of Information and Technology in Supply Chain Management: Case Study of Artificial Intelligence – A Mini Review

2020 ◽  
Vol 5 (12) ◽  
pp. 19-23
Author(s):  
Hieu Bui Trong ◽  
Uyen Bui Thi Kim
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management ◽  
The Past ◽  
Widespread Acceptance ◽  
Limited Application ◽  
Block Chain ◽  
Management Case Study

It is wide known that the world has been moving towards a digital future over the years, and Industry 4.0 technologies are considered to be the way of the future. One of the most prominent of these technologies (including Block Chain, Internet of Things, Cloud Computing, Big Data, etc.) is Artificial Intelligence (AI), was introduced to develop and create “thinking machines” that are capable of mimicking, learning, and replacing human intelligence. However, its widespread acceptance as a decision-aid tool, AI has seen limited application in supply chain management (SCM). The purpose of this work is to identify the contributions of AI to SCM through a brief review of the existing literature. Besides, this paper reviews the past record of success in AI applications to SCM and identifies the most subfields of SCM in which to apply AI.

Impact of Supply Chain Digitalisation on a Connected Global Market

2020 ◽  
pp. 186-204
Author(s):  
Farooq Habib ◽  
Murtaza Farooq Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Final Section ◽  
Digital Technologies ◽  
Global Market ◽  
The Third ◽  
The Impact

This chapter focuses on the impact of supply chain digitalisation on a connected global market. The first section focuses on the dynamic consumer requirements and preferences. The second section appraised the segmentation and mapping of digital technologies. The third section examines the contemporary application of digital technologies including: big data, blockchains, artificial intelligence, machine learning, and data analytics. The final section analysises the rules and regulations the form the basis of a contemporary framework for the governance of digital technologies.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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