Leveraging IoT and Data Analytics in Logistics: Optimized Routing, Safety, and Resource Planning
  IJCTT-book-cover
 		          
 
© 2024 by IJCTT Journal
Volume-72 Issue-7
Year of Publication : 2024
Authors : Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan
DOI :  10.14445/22312803/IJCTT-V72I7P113

How to Cite?

Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan, "Leveraging IoT and Data Analytics in Logistics: Optimized Routing, Safety, and Resource Planning," International Journal of Computer Trends and Technology, vol. 72, no. 7, pp.101-107, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I7P113

Abstract
This article delves into the many advantages of using data analytics and the Internet of Things (IoT) in logistics, with an emphasis on improved safety, optimized routing, and resource allocation. In optimized routing, IoT sensors embedded in vehicles, warehouses, and infrastructure collect real-time data on traffic conditions, weather patterns, and vehicle performance. Coupled with sophisticated data analytics algorithms, this information enables logistics companies to adjust routes dynamically, minimizing delays, fuel consumption, and environmental impact while maximizing efficiency and customer satisfaction. Safety enhancement is another critical aspect empowered by IoT and data analytics. By equipping vehicles with sensors that monitor driver behavior, road conditions, and surrounding environments, logistics companies can proactively identify and mitigate potential safety risks. Real-time analytics further enable predictive maintenance, ensuring vehicles are in optimal condition and reducing the likelihood of accidents. Resource planning is streamlined through IoT-enabled inventory management systems and predictive analytics. By continuously monitoring inventory levels, demand patterns, and supply chain dynamics, logistics companies can optimize warehousing space, reduce stockouts, and minimize carrying costs. Predictive analytics algorithms forecast future demand, enabling proactive decision-making and strategic resource allocation. Furthermore, the paper discusses the challenges and considerations associated with implementing IoT and data analytics solutions in logistics, including data privacy concerns, cybersecurity risks, and infrastructure requirements. This paper explores the symbiotic relationship between IoT and data analytics in logistics, focusing on their applications in three critical areas: optimized routing, safety enhancement, and resource planning. Safety enhancement is another crucial aspect where IoT and data analytics play a pivotal role. By deploying sensors and monitoring devices throughout the logistics network, organizations can gather comprehensive data on vehicle health, driver behavior, and environmental conditions. Analyzing this data in real-time enables proactive identification of safety hazards, facilitating timely interventions to prevent accidents and ensure compliance with regulatory standards. Resource planning in logistics involves the efficient allocation of assets such as vehicles, personnel, and storage facilities. IoT-enabled tracking devices provide granular visibility into the movement and utilization of these resources, while advanced analytics algorithms offer predictive insights into demand patterns and operational trends. By optimizing resource allocation based on data-driven forecasts, organizations can minimize costs, reduce wastage, and enhance overall operational agility.

Keywords
Internet of Things(IoT), Data analytics, Logistics, Optimized routing, Safety enhancement, Resource planning.

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