Supply Chain Visibility: The Role of Real-Time Data in Logistics

Modern IT infrastructures, such as stream processing and novel technologies like the Internet of Things (IoT) and artificial intelligence (AI), have made it possible for logistics companies to process and analyze real-time data in previously unimaginable ways. By leveraging these technologies, logistics companies can gain real-time visibility and a competitive edge by quickly and accurately responding to demand changes or supply chain disruptions. With real-time freight analytics, logistics providers can optimize routes, manage their fleets, forecast demand, and make informed decisions that improve efficiency and reduce costs.

In this article, we will explore how modern IT infrastructures and novel technologies enable real-time data processing in the logistics sector and how companies can benefit from these advancements.

Supply Chain Visibility and The Ever-Growing Amount of Data Points in the Logistics Sector

Supply chain visibility is essential for logistics companies, as it allows them to make informed decisions about their business operations and anticipate supply chain disruptions. In today’s data-driven world, an ever-growing number of data points can be used to improve supply chain visibility. From real-time tracking of goods, sensor data from transportation assets, and online customer interactions, logistics companies have access to an unprecedented amount of information about the movement of goods. However, with this abundance of data comes the challenge of effectively collecting, storing, processing, and analyzing it.

Modern Big Data Infrastructures and Novel Technologies

Modern IT infrastructures and technologies, such as IoT, machine learning, and big data analytics, can help logistics companies manage and make sense of this data, ultimately leading to improved supply chain visibility and efficiency.

The Internet of Things and Telematics: Revolutionizing Transportation Management Systems with Complete Visibility

With GPS trace devices, sensors, and other IoT devices collecting data on transportation assets, logistics companies can now track the location and condition of their vehicles in real-time. This real-time visibility allows logistics professionals to get the real-time location of every shipment, provide status updates to customers, provide predictive fleet maintenance, reduce fuel consumption, and improve overall efficiency. In addition, telematics systems provide valuable data on driver behavior, helping logistics companies identify and address any potential safety issues.

Stream Processing: Powering Real-Time Data Processing in Logistics

Stream processing is a modern IT infrastructure that plays a vital role in the logistics industry’s ability to process and analyze real-time information. Stream processing allows companies to continuously collect, process, and analyze data as it is generated, rather than waiting for all the data to be collected before processing it (an approach based on batch processing). This enables logistics professionals to make timely, informed decisions based on the most up-to-date information.

Machine Learning: AI-Powered Real-Time Data Analysis in the Supply Chain

In the logistics industry, machine learning models based on real-time information can be applied in various ways, such as predicting demand, detecting anomalies, analyzing natural language, implementing computer vision, or optimizing pricing strategies.

For example, a machine learning model could be trained to identify patterns in data on transportation routes, traffic patterns, location of gas stations, and weather conditions and then use that information to suggest the most efficient routes and schedules or next cost-efficient fuel stop for delivery vehicles as they drive. Insights from how each truck moves can be used to provide accurate ETA (estimated time of arrival) for every shipment and proactively notify customers if any delays occur. Alternatively, it could detect anomalies in truck sensor data, alerting maintenance teams to potential issues before they become problems.

Real-Time Data in Action: Case Studies From the Logistics Industry

Real-time data is revolutionizing the logistics industry, enabling companies to track shipments, optimize routes, and forecast demand like never before. Here are a few examples of how real-time visibility of supply chain data is being put to work in the logistics world:

Real-Time Tracking System for Shipments

With a real-time tracking system, shipments can be monitored every step of the way. Sensors or other tracking devices transmit data on the location and status of shipments, allowing customers to see exactly where their packages are and when they’ll arrive. It’s a win-win for logistics companies and their customers - businesses can optimize routes and reduce fuel consumption while customers enjoy peace of mind and improved satisfaction.

Related case study: Implementing AI model to optimize routes and timelines of deliveries
A company from the logistics sector approached us to create a custom AI model that optimizes routes and the scheduling of deliveries.
Our challenge? The key challenge here was to prepare a dedicated AI-based system designed for carriers to optimize delivery time depending on the destination address. Thanks to the model we managed to reduce failed and late delivery rates by 30%. Read our detailed case study of this project.

Inventory Management Process

Gone are the days of overstocked warehouses and empty shelves. With real-time consumer demand and sales data, logistics professionals can adjust their inventory levels to meet changing demand and avoid waste. Smart inventory management is all about staying nimble and responsive in today’s fast-paced world.

Real-Time Freight Analytics

Real-time freight and lane analytics involves real-time data to enable automated, proactive decision-making in logistics and supply chain operations. By analyzing order data on shipments or data on transportation routes, traffic patterns, and other factors, automated systems can make data-driven decisions that improve efficiency and reduce costs. For example, real-time data can trigger alerts for logistics professionals when unexpected delays or disruptions occur, allowing them to make informed decisions about how to respond. Automated systems can also use real-time visibility to consolidate shipments, find available carriers, adjust schedules, reroute trucks, and make other proactive adjustments to optimize the movement of goods along the supply chains.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriers
One of our clients was seeking to improve the global supply chain optimization product
Our challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery. Read more about this case study.

Predictive Maintenance and Transportation Management System

Predictive maintenance is a game-changer for logistics companies. By analyzing real-time data from sensors on transportation assets, businesses can predict when maintenance is required and schedule it in advance, reducing the risk of equipment failures and unplanned downtime.

Dynamic Pricing Solutions in Logistics and Supply Chain Operations

In today’s competitive market, dynamic pricing is a must for logistics and supply chain operations. With real-time visibility on consumer demand, market conditions, the current shape of the internal logistics network, and other factors, businesses can adjust their prices in real-time to maximize profits and stay ahead of the competition.

Related case study: Delivering a dedicated IT system to manage and sell freight deals and plan transportation
A major logistics company approached us to create a dedicated IT system to handle their core business process - managing and selling logistics deals.
Our challenge? The key challenge in the logistics sector is cutting the time of concluding deals to an absolute minimum. The tool has to be very responsive and help in the smart matching of carriers and freight, fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier. Read more about this case study.

Digital Twin of a Supply Chain Process

Digital twins are virtual representations of physical objects or systems that can be used to simulate and analyze their behavior. In the logistics industry, digital twins can be used to improve the efficiency of supply chain operations by providing real-time data on the movement of goods and the performance of transportation resources. By creating digital twins of delivery vehicles, for example, companies can track their location and condition in real time, optimize routes, and predict maintenance needs. Digital twins can also be used to simulate the behavior of entire supply chains, allowing organizations to analyze and optimize the flow of goods from end to end. More information about parcel delivery optimization you can find here.

Navigating the Complexities of Real-Time Data Processing in Logistics

Real-time streaming data has the power to transform logistics operations, but navigating the complexities of real-time data processing can be challenging. Here are a few tips for success:

Set clear business objectives: Before you even begin investing in real-time data solutions and architectures, you must clearly understand what you hope to achieve. Identify your business objectives and use real-time data to help you achieve them.

Invest in the proper infrastructure: Modern IT architectures like stream processing are essential for real-time data processing in logistics. Ensure you have the right tools and technologies to support real-time data collection and analysis.

Build a strong foundation: Before effectively approaching real-time analytics, you need a strong foundation of clean, accurate data. Make sure your data is well-organized and properly structured to support real-time analysis.

Train your staff: Real-time data processing requires a different mindset and set of skills than traditional data processing. Ensure your staff is trained to work with real-time data and can effectively use the tools and technologies at their disposal. If stream processing is not your internal know-how, consider hiring external experts for consulting.

Maximizing the Benefits of Real-Time Data in Logistics

Are you looking to improve your logistics operations with real-time data? You’re not alone! Real-time analytics can transform how companies track shipments, optimize routes and forecast demand. And with modern IT infrastructures like stream processing and technologies like edge devices and machine learning, it’s easier than ever to gain 360-degree visibility into supply chain management and analyze real-time data. But let’s be honest - it can still be intricate.

That’s where the experts at nexocode come in. We can help you navigate the complexities of real-time data processing and make the most of this powerful technology. Contact us today to learn more about how we can help you take your logistics operations to the next level.