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.

Generative AI refers to deep learning technology that can automatically produce original text, images, audio, video, and other outputs. With its emerging capabilities, Generative AI can radically change the dynamics of key operational processes in most industries. In this document, we illustrate how it is possible to integrate Generative AI technologies into the Financial Reporting System (FRS) of a corporation. The integration will allow the FRS to deliver on demand concise and lucid insights to its associated users on what is happening in the corporation and different aspects of the corporation performance assessment, such as its liquidity, solvency, profitability, organizational structure, and share buy back decision. The integration will also facilitate the delivery of what-if analyses associated with different strategic and tactical decisions taken by the corporation management, such as capital budgeting and profit distribution decisions. The unique added value of several attributes of these insightful analytics is automating the responses to ongoing requests of the FRS users on the corporation. Generative AI capabilities are rapidly expanding. The integration can be applied not only for the corporate FRS but any FRS at the national or global levels delivered by a central bank or an accounting standards setter. Any of these FRS can be made into a unique “hub” for the integrated Generative AI technologies. An equally innovative possible generalized integration could put any corporate process at the center and its supporting FRS tasks and deliverables in its periphery.

This study aims to achieve a new understanding of how, why, and when consumer behavior is shaped, enacted, and experienced inside and across integrated digital ecosystems related to large-scale trackable goods, all in service of helping marketers optimize their business performance in the new economy. The pioneering understanding begins by exploring what motivates the choices of a homogeneous group of consumers to organize their consumption of national and store brand varieties of consumer package goods in a certain manner. Thereafter, the essay explores how, if at all, the other digital activities of consumers across various product-related digital spaces and on various platforms build expertise and interest in these products such that they exert an effect on the purchase choices for these products. The essay then advances to asking how online information seeking, in various product-related digital spaces, on various platforms, and from various sources, and taking place at various points in the purchase journey affects online-offline dynamics in purchasing these products. Thereafter, the research examines how paid digital communication in various product-related digital spheres and forms, enabled by consumer advertising engagement on various platforms, boosts the offline sales of these products. Finally, by employing a new methodology that combines consumer scanning data, self-reported online activity data, and transaction data collected from an ad-tech partner, the research presents a fresh set of marketing action levers and performance outcomes on chosen products. Along the way, progress is made on four under-investigated topics in the advertising literature – the role of consumer actors and their expertise in the online-offline purchasing dynamics for ads, advertising engagement, consumer digital spaces, and consumer digital activity investment.