Articles

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.

The supply chain ecosystem plays a very important role in the success or failure of organizations, markets, and economies. Supply chain ecosystems are broadly defined as supply chain organizations and their collaborators. Today's combined challenges of pandemic shutdowns, rising internet usage, and skyrocketing climate change concerns demand that the supply chain ecosystem better connect with customers, when and how they want, to provide products and services with high levels of availability and zero defects, yet collaboratively do this to reduce transportation and production risks, often at the same time reducing operational costs and carbon footprints. Addressing these challenges, this work explores the cloud delivery capabilities of cloud-native architectures to enable the big data integrations and analytics that are needed to grow smarter supply chain ecosystems. This work describes what smart supply chain ecosystems are and how they are planning to grow their technology and integration capabilities. Discussing the industry-leading advanced and manufacturing technology producer ecosystems, it is explained how their technology collaboration and investment plans are driven by climate change and job creation goals. With these background models, the work examines the new digital reality of customer-driven experiences and economies that are demanding cloud-native and intelligent technology partnerships to deliver climate objectives, operational responsiveness, and compatibility to avoid trading economies of scale for economies of integration. The final objectives of this paper are to share key ideas about the need to balance the growing customer service direct-to-consumer business models with those for collaborative investment by market and industry. In doing this, it hopes to promote an intelligent supply chain ecosystem foundation for helping its different participating countries survive and thrive in the digital economy.