Leveraging Cutting-Edge Technologies and Innovative Strategies to Optimize the IoT and AI Integration in Supply Chain Management

I. Introduction

Conventional supply chain models are insufficient to satisfy the requirements of contemporary consumers and their growing quantities. This model presents a methodology for incorporating Internet of Things (IoT) and Artificial Intelligence (AI) (Pandey, D. et al., 2021) technology into supply chain management. The objective is to establish a flexible and responsive system (Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J., 2022). The proposed new model aims to improve the efficiency, transparency, and environmental friendliness of the supply chain. In order to accomplish this, a diverse array of Internet of Things (IoT) sensors (Kumar Pandey, B. et al., 2021) is strategically placed across the supply chain network. These sensors include temperature sensors, GPS trackers, RFID tags, and other similar devices, which are utilized to gather up-to-the-minute data (Kousiouris, G. et al., 2019). Edge computing devices are strategically deployed at critical locations along the supply chain to locally analyse data, resulting in decreased latency and improved real-time decision-making capability (Oh, A. S., 2019). AI-powered big data analytics are employed to handle and examine the extensive volume of data produced by IoT (Pandey, J. K. et al., 2022) (B. Muniandi et al.,2019) sensors, including historical data, real-time data, and external data sources. The updated model integrates machine learning algorithms (Meslie, Y. et al, 2021) to enhance predictive demand forecasts, optimize routes, ensure quality control, and minimize risks. The concept guarantees scalability and minimal latency by utilizing a hybrid cloud and edge computing structure to process data both on-site and in the cloud. The proposed methodology for integrating IoT (Pramanik, S. et al., 2023) and AI into the supply chain is a comprehensive approach to revolutionizing supply chain management (Lee, I., & Lee, K., 2015). Organizations can develop more adaptable, productive, and environmentally friendly supply chains that meet the requirements of the contemporary business environment by adopting advanced technology (Pandey, D., & Pandey, B. K., 2022) and promoting an innovative culture. The amalgamation of Internet of Things (IoT) and Machine Learning (ML) technologies presents a vast opportunity to transform supply chain management by enhancing productivity, diminishing expenses, and facilitating superior decision-making. The proposed methodology places significant emphasis on the collecting of real-time data (Iyyanar, P. et al., 2023) by deploying a variety of Internet of Things (IoT) sensors, in addition to the current content. The placement of temperature sensors, GPS trackers, RFID tags, and other similar devices across the supply chain network allows for the collection of real-time data, which improves the adaptability and responsiveness of the model. The integration of edge computing devices at crucial locations in the supply chain (Malhotra, P. et al., 2021) not only guarantees local data analysis but also leads to a decrease in latency, hence improving the system's capacity to make instantaneous decisions (Wang, J. et al., 2021). The application of artificial intelligence (AI) (Pandey, B. K., & Pandey, D., 2023) enabled big data analytics continues to be an essential element in managing the vast amount of data produced by Internet of Things (IoT) sensors. This includes data from the past, present, and external sources. The new model incorporates machine learning techniques (Anand, R. et al., 2023), which improve its abilities in predictive demand forecasting, route optimization, quality control, and risk reduction. The adoption of a hybrid cloud and edge computing architecture ensures the ability to scale and reduces latency, achieving a harmonious equilibrium between on-site and cloud-based data processing (Sarkis, J. et al., 2011).