As the digitization and logistics landscape constantly matures, so is the demand for quicker, more efficient, and more transparent shipment tracking.
Due to this rising demand, artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as game-changing technologies, improving how businesses handle vehicle tracking.
By combining AI with the Internet of Things (IoT), we notice the emergence of a new level of data-driven intelligence transforming the logistics game from fleet management to real-time tracking.
This blog post highlights what AIoT is in logistics, what shipment tracking is, and how AIoT can be used in shipment tracking.
Shipment tracking is a practice that allows you to track a package’s journey from the sender to the receiver. It uses barcodes and GPS to deliver real-time updates regarding the package’s location, status, and assessed delivery date.
This is crucial in logistics as it improves efficiency, customer satisfaction, and transparency. It lets businesses monitor their goods in transit, manage inventory correctly, and quickly address delivery issues.
In the logistics landscape, real-time shipment tracking has become the need of the hour. It bridges transparency between businesses and clients, providing timely updates about a package’s journey. This enhances the customer experience and meets their changing expectations in an evolving digital world.
For businesses, real-time operations tracking enhances inventory management and allows quick identification of delivery issues. This boosted efficiency can significantly improve their bottom line.
Implementing AIoT-assisted vehicle tracking systems and other advanced technologies in this logistics and supply chain promises even more hassle-free operations. The combination of AI and IoT improves the accuracy of vehicle tracking systems. It delivers predictive insights, which can be leveraged to predict and mitigate potential disruptions in the delivery process.
In logistics, AIoT (artificial intelligence + internet of things) helps with demand predictions, alter orders, and optimize routes for products in transit. With AIoT, shipment tracking is enhanced, delivering real-time updates, indicating potential delays, delivering customer alerts, and optimizing delivery routes. As such, AIoT is creating shipment tracking more efficiently and user-friendly.
Illustration: Imagine a truck fleet equipped with AIoT devices. These devices not only track the location of each truck in real time but also analyze the data they gather to predict potential delays, optimize delivery routes, and control inventories effectively. This makes shipment tracking more efficient and enhances the overall logistics operation.
Let’s have a quick overview of how AIoT is being used in logistics and its transformative effects on shipment tracking.
Integrating IoT devices can help immediately process and interpret vast amounts of data. Furthermore, it delivers real-time updates about a shipment’s location and status.
For instance, GPS data, traffic information, telematics, and weather conditions are analyzed immediately to provide real-time updates about a shipment’s location, status, and estimated arrival time.
This helps improve planning and decision-making for the logistics provider and the customer.
Illustration: AI-powered real-time vehicle tracking lets you receive updates on your phone as the package travels from the warehouse to your doorstep. You can monitor the package’s journey via each phase, from being loaded onto the delivery vehicle to reaching your doorstep, ensuring reliability and satisfaction.
Fleet management coordinates an enterprise’s vehicles, drivers, and products and is essential to the logistics industry. AIoT systems help optimize fleet operations, ensure driver safety, schedule preventive maintenance, and reduce operational costs by integrating data from vehicle sensors, GPS, and external sources like weather or traffic reports.
Illustration: Imagine a fleet of delivery trucks managed by AIoT-based fleet GPS tracking systems, where each vehicle’s route is optimized for the quickest delivery and minimal fuel consumption.
The system also monitors each vehicle’s condition, scheduling maintenance when needed to prevent breakdowns and ensure efficient operations.
Predictive analytics is a significant aspect of AIoT. It incorporates machine learning algorithms with historical and real-time data to forecast future outcomes. In logistics, predictive analytics can predict possible delays due to weather or traffic conditions, enabling businesses to adjust their routes or schedules accordingly.
Illustration: A logistics company employs an AIoT system to analyze data from past shipments, current weather, and traffic reports. This system can anticipate a potential delivery delay due to a hurricane brewing along the planned route and adjusts the delivery schedule proactively, enhancing customer satisfaction.
AIoT technology can study location, destination, traffic conditions, roadwork, and weather to decide the most efficient delivery routes. This greatly reduces delivery times and fuel consumption while also improving customer satisfaction.
Illustration: Suppose a delivery driver employs an AIoT-powered map that provides the fastest route based on real-time data. It indicates rerouting when a traffic jam occurs, ensuring the driver takes the most efficient route to the delivery location.
AIoT enhances cargo optimization by intelligently managing the loading and unloading process. This reduces handling time, increases storage efficiency, and optimizes the cargo for unloading at the destination.
Illustration: An AIoT system manages cargo loading in a warehouse, considering the destination, priority, and weight of each package to optimize space usage. It creates a loading plan that reduces handling time during unloading and decreases the chance of damage to the goods.
AIoT allows logistics companies to provide timely and automated customer alerts about shipment status. If the system predicts a delay, the customer is immediately informed, improving transparency and customer satisfaction.
Illustration: When you order a product online, AIoT-driven customer alerts keep you updated about the estimated delivery date. If the system predicts a delay due to bad weather, you’re informed instantly, ensuring transparency and improving your experience.
Fraud detection is another area where AIoT can make a significant difference. By analyzing patterns and irregularities in the data, AIoT can identify potential fraudulent activities and alert the concerned personnel to take immediate action.
Illustration: A shipping company’s AIoT system monitors all shipment tracking data. Suppose a package is rerouted unexpectedly or a delivery report is submitted when GPS data shows the package hasn’t been delivered. In that case, the system detects these inconsistencies and alerts the security team, potentially preventing fraudulent activity.
AIoT-powered chatbots can provide instant, accurate responses to customer queries about shipment status 24/7. This enhances customer service and frees human customer service agents to handle more complex questions.
Illustration: A customer concerned about a delayed shipment uses an AIoT-powered chatbot. The chatbot instantly provides information about the delay, explains the reason, and gives the new estimated delivery time, easing the customer’s worries.
AIoT can enhance demand forecasting by analyzing historical shipment data and market trends. This allows logistics companies to manage their inventory effectively, reducing storage costs and ensuring they meet customer demand.
Illustration: A logistics company uses an AIoT system to analyze data from past years and current market trends. The system predicts a surge in demand for their services during the upcoming holiday season, enabling the company to plan and ensure they can meet the increased demand.
With AIoT, the documentation process can be automated, reducing the chance of human error and ensuring compliance with shipping regulations. This streamlines the process, saves valuable time, and can prevent costly compliance issues.
Illustration: A shipping company uses AIoT to automate its documentation process. The system accurately fills out shipping labels, customs forms, and other required documents, speeding up the process and preventing mistakes that could lead to regulatory issues or delays.
A quick comparison between traditional shipment tracking vs AI assisted shipment tracking
To learn more about how AIoT technology can optimize your logistics operations, we encourage you to get in touch with Fretron. Discover the evolving potential of their AIoT-powered platform and unlock new possibilities for your overall logistics and supply chain management process.
Shipment tracking tracks a package’s journey from the sender to the receiver, delivering real-time updates about its location, status, and calculated delivery date.
AIoT enhances transportation tracking by offering real-time updates, predicting possible delays, optimizing delivery routes, automating customer service, catching fraud, improving cargo handling, forecasting demand, and automating documentation.
Shipment tracking enhances efficiency, customer satisfaction, and transparency in the logistics ecosystem. It helps businesses manage goods in transit, control inventory effectively, and fast address delivery issues.
AIoT optimizes fleet management by examining data from GPS systems, weather forecasts, and real-time traffic updates to calculate the most efficient routes. This reduces delivery times, fuel consumption, and overall costs.
Predictive shipment tracking analytics lets AIoT anticipate potential delays due to weather conditions and traffic congestion. Businesses can proactively address issues and notify customers about any changes in delivery timelines.
AIoT offers robust solutions for cargo optimization by analyzing data to define the best loading and unloading methods, enhancing efficiency and reducing transport costs.
AIoT-powered chatbots and virtual assistants deliver instant and correct responses about shipment status, enhancing the customer service experience by reducing wait times and letting teams focus on more complex issues.