Adoption of Artificial Intelligence in Supply Chain Risk Management

Paul, Souma Kanti; Riaz, Sadia; Das, Suchismita

doi:10.4018/jgim.307569

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Over time, models based on artificial intelligence applications, such as artificial neural networks and deep learning, have been highlighted in various scientific studies and applications in different sectors (Chang, Wang, & Chuang, 2022;Du & Shu, 2022;Feng & Chen, 2022;Hossain et al, 2022;Hou et al, 2022;Li, Shang et al, 2022;Paul, Riaz, & Das, 2022;Qiu, 2022;Rashidin et al, 2022;Shrivastav, 2022;Sun et al, 2022;Varsha et al, 2021;Wu, Qiao et al, 2022;Wu, Zhu et al, 2022;Xu, Xiang, & He, 2022;Yang & Wu, 2022;Zhao, 2022). However, the financial sector -FinTech, has shown strong growth in using this method to provide financial solutions.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Figueiredo,

Camargo,

Ferreira

et al. 2023

Journal of Organizational and End User Computing

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Based on the mixed model unified technology acceptance and utilization theory (UTAUT) and spinner innovation model (SPINNER), a theoretical model is suggested to explain the determinant of behavioral intention to predict innovation in the context of a financial sector firm. A questionnaire was developed to collect primary data, which was subsequently processed through the artificial intelligence technique (deep learning). The constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, public knowledge, private knowledge, and innovation) supported the model, including mediating hypotheses. It was observed that the mixed methodological approach (SEM and ANN) can help to find the linear and non-linear relationships better, being that the error of the predicted model is 0.104, that is, 10.4% relatively low, which evidences that ANN can be used to predict the dependent variable innovation safely.

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Section: Discussionmentioning

confidence: 99%

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Figueiredo,

Camargo,

Ferreira

et al. 2023

Journal of Organizational and End User Computing

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“…The global trends in AI adoption in supply chain management indicate a growing interest in leveraging AI technologies to address challenges and improve transparency and traceability, particularly in the food supply chains (Dora et al, 2021). The adoption of AI in supply chain management is still in its early stages, with promising opportunities to elevate supply chain performance (Paul et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

AI in supply chain optimization: A comparative review of USA and African Trends

Akoh Atadoga,

Ogugua Chimezie Obi,

Femi Osasona

et al. 2024

Int. J. Sci. Res. Arch.

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The integration of Artificial Intelligence (AI) in supply chain management has emerged as a critical driver of efficiency and competitiveness in global markets. This paper provides a comparative review of AI trends in supply chain optimization between the United States and African regions, shedding light on the unique challenges and opportunities faced by each. In the United States, AI adoption in supply chain optimization has been robust, with a focus on predictive analytics, machine learning, and advanced automation technologies. American companies leverage AI to enhance demand forecasting, optimize inventory management, and streamline logistics processes. The integration of AI-driven solutions has allowed U.S. businesses to achieve higher accuracy in demand predictions, reduce lead times, and minimize operational costs. Furthermore, the use of AI algorithms in route optimization has significantly improved delivery efficiency, leading to enhanced customer satisfaction. Contrastingly, African countries are experiencing a more gradual but steadily increasing adoption of AI in supply chain optimization. Limited access to advanced technology infrastructure, coupled with resource constraints, has posed challenges for African businesses. However, innovative approaches are being explored, such as the use of mobile technologies and cloud-based solutions to overcome infrastructure limitations. AI applications in African supply chains focus on improving visibility, minimizing waste, and ensuring timely delivery. The continent's diverse supply chain landscape, encompassing agriculture, mining, and manufacturing, presents a unique set of challenges that AI aims to address. Both the United States and African nations recognize the potential of AI to transform supply chain management. While the U.S. is at the forefront of AI implementation, Africa is forging ahead with tailored solutions that align with its specific context. Collaboration and knowledge exchange between these regions could pave the way for a globalized approach to AI in supply chain optimization. This review underscores the importance of understanding regional nuances in adopting AI technologies, fostering collaboration for mutual benefit, and advancing the global evolution of AI-driven supply chain management.

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“…The nascent technology may not quickly bring business the best return on investment (ROI), as the governance mechanism, regulation and industry standards are not readily available (Angelis and da Silva, 2019; Janssen et al , 2020; Muldoon, 2021). To successfully adopt emerging technology in business sectors, businesses must consider various factors such as corporate norms and culture, regulation and legislation, governance, market and technical factors (Chau et al , 2021; De Moya et al , 2021; Janssen et al , 2020; Mondal and Chakrabarti, 2021; Paul et al , 2022; Xing et al , 2022). As blockchains have received much attention, many enterprises and organizations desire to develop business solutions through blockchain-based applications to gain competitive advantages in the market (Chong et al , 2019; Hu et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

Understanding governance and control challenges of blockchain technology in healthcare and energy sectors: a historical perspective

Zhang

Shetty

et al. 2023

JMH

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Purpose Despite rapid growth in blockchains, there was limited discussion about non-technical and technical factors on blockchain governance in the extant literature. This study aims to contribute new knowledge to the literature on potential factors affecting the adoption, governance and scale-up of blockchain technologies in the health-care and energy sectors, presented in a holistic framework. Design/methodology/approach This study adopts the qualitative case study research methodology to research blockchain governance in practice. The authors contacted a national blockchain consortium to conduct their research on the governance issue of blockchain. Two leading case organizations, one from the health-care industry and another from the energy industry, were deliberately selected for their study for their active role and reputation in the consortium and practical experience in blockchain governance. Findings The developed framework helps identify potential research gaps or concerns on adopting a blockchain as well as assessing blockchain implementation and governance in other industries. Depending on the circumstances, some of the factors can be either drivers or obstacles to further blockchain development. The different forces may also be more or less evident over time as blockchains develop. The two real-world case studies contribute to the information technology governance literature on blockchain governance. Originality/value The results of this case studies will be beneficial for developing theories and empirical models to determine antecedents for achieving consensus and trust in blockchain and testing the relationship between these factors and blockchain governance at different levels. As a result, theories related to the governance of blockchain technologies could be further developed.

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Adoption of Artificial Intelligence in Supply Chain Risk Management

Cited by 9 publications

References 30 publications

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Predicting the Intention to Adopt Innovation in Supply Chain Finance

AI in supply chain optimization: A comparative review of USA and African Trends

Understanding governance and control challenges of blockchain technology in healthcare and energy sectors: a historical perspective

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