Automatic Stock Prediction with AI to Optimize Retail Expenditures as an Innovation in Business
Abstract
This research paper explores the transformative potential of integrating Artificial Intelligence (AI) and Blockchain technologies in business innovation (BI-AIBT), with a focus on the retail sector. By investigating the impact of BI-AIBT on operational challenges, this research highlights how AI-driven automated stock prediction, exemplified by the Smart Inventory AI (SI-RAI) solution, can address critical issues in inventory management, supply chain inefficiencies, and interdepartmental collaboration. Through qualitative research involving a literature study, this paper reveals the urgent need for an efficient solution to optimize spending, improve customer satisfaction, and enable data-driven decision-making. The findings underscore the great expectations for SI-RAI, signifying its potential to revolutionize retail operations by offering predictive insights, fostering interdepartmental collaboration, and ultimately driving efficiency, financial growth, and market competitiveness.
References
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308(1–2), 7–39. https://doi.org/10.1007/s10479-020-03620-w
Chen, Y., & Biswas, M. I. (2021). Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19. Sustainability, 13(22), 12656. https://doi.org/10.3390/su132212656
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Pradeep, A. K., Appel, A., & Sthanunathan, S. (2019). AI for marketing and product innovation: Powerful new tools for predicting trends, connecting with customers, and closing sales. Wiley.
Reier Forradellas, R. F., & Garay Gallastegui, L. M. (2021). Digital Transformation and Artificial Intelligence Applied to Business: Legal Regulations, Economic Impact and Perspective. Laws, 10(3), 70. https://doi.org/10.3390/laws10030070
Simchi-Levi, D., & Wu, M. X. (2018). Powering retailers’ digitization through analytics and automation. International Journal of Production Research, 56(1–2), 809–816. https://doi.org/10.1080/00207543.2017.1404161
Wang, Z., Li, M., Lu, J., & Cheng, X. (2022). Business Innovation based on artificial intelligence and Blockchain technology. Information Processing & Management, 59(1), 102759. https://doi.org/10.1016/j.ipm.2021.102759
Weber, F. D., & Schütte, R. (2019). State-of-the-art and adoption of artificial intelligence in retailing. Digital Policy, Regulation and Governance, 21(3), 264–279. https://doi.org/10.1108/DPRG-09-2018-0050
