@Article{crph1202,
AUTHOR = {Kalisetty, Srinivas and Pandugula, Chandrashekar and Mallesham, Goli},
TITLE = {Leveraging Artificial Intelligence to Enhance Supply Chain Resilience: A Study of Predictive Analytics and Risk Mitigation Strategies},
JOURNAL = {Current Research in Public Health},
VOLUME = {3},
YEAR = {2023},
NUMBER = {1},
PAGES = {29-45},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1202},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2023.1202},
ABSTRACT = {The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.},
}

%0 Journal Article
%A Kalisetty, Srinivas
%A Pandugula, Chandrashekar
%A Mallesham, Goli
%D 2023
%J Current Research in Public Health

%@ 2831-5162
%V 3
%N 1
%P 29-45

%T Leveraging Artificial Intelligence to Enhance Supply Chain Resilience: A Study of Predictive Analytics and Risk Mitigation Strategies
%M doi:10.31586/jaibd.2023.1202
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1202

TY  - JOUR
AU  - Kalisetty, Srinivas
AU  - Pandugula, Chandrashekar
AU  - Mallesham, Goli
TI  - Leveraging Artificial Intelligence to Enhance Supply Chain Resilience: A Study of Predictive Analytics and Risk Mitigation Strategies
T2  - Current Research in Public Health
PY  - 2023
VL  - 3
IS  - 1
SN  - 2831-5162
SP  - 29
EP  - 45
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1202
AB  - The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.
DO  - Leveraging Artificial Intelligence to Enhance Supply Chain Resilience: A Study of Predictive Analytics and Risk Mitigation Strategies
TI  - 10.31586/jaibd.2023.1202
ER  -