Technologies like Apache Kafka and Apache Flink are at the heart of this transformation. They allow logistics companies to capture, process, and act on streaming data as it’s generated—from vehicle sensors and telematics systems to inventory platforms and customer applications. This enables new use cases in predictive maintenance, live fleet tracking, customer service automation, and much more.

A growing number of companies across the supply chain are embracing this model. Whether it’s real-time shipment tracking, automated compliance reporting, or AI-driven optimization, the ability to stream, process, and route data instantly is proving vital.

One standout example is Penske Logistics—a transportation leader using Confluent’s data streaming platform (DSP) to transform how it operates and delivers value to customers.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases.

Why Real-Time Data Matters in Logistics and Transportation

Transportation and logistics operate on tight margins and stricter timelines than almost any other sector. Delays ripple through supply chains, disrupting manufacturing schedules, customer deliveries, and retail inventories. Traditional data integration methods—batch ETL, manual syncing, and siloed systems—simply can’t meet the demands of today’s global logistics networks.

Data streaming enables organizations in the logistics and transportation industry to ingest and process information in real-time while the data is valuable and critical. Vehicle diagnostics, route updates, inventory changes, and customer interactions can all be captured and acted upon in real time. This leads to faster decisions, more responsive services, and smarter operations.

Real-time data also lays the foundation for advanced use cases in automation and AI, where outcomes depend on immediate context and up-to-date information. And for logistics providers, it unlocks a powerful competitive edge.

How Data Streaming with Apache Kafka and Flink Transforms the Supply Chain

Apache Kafka serves as the backbone for real-time messaging—connecting thousands of data producers and consumers across enterprise systems. Apache Flink adds stateful stream processing to the mix, enabling continuous pattern recognition, enrichment, and complex business logic in real time.

In the logistics industry, this event-driven architecture supports use cases such as:

• Continuous monitoring of vehicle health and sensor data
• Proactive maintenance scheduling
• Real-time fleet tracking and route optimization
• Integration of telematics, ERP, WMS, and customer systems
• Instant alerts for service delays or disruptions
• Predictive analytics for capacity and demand forecasting

This isn’t just theory. Leading logistics organizations are deploying these capabilities at scale.

Data Streaming Success Stories Across the Logistics and Transportation Industry

Many transportation and logistics firms are already using Kafka-based architectures to modernize their operations. A few examples:

• LKW Walter relies on data streaming to optimize its full truck load (FTL) freight exchanges and enable digital freight matching.
• Uber Freight leverages real-time telematics, pricing models, and dynamic load assignment across its digital logistics platform.
• Instacart uses event-driven systems to coordinate live order delivery, matching customer demand with available delivery slots.
• Maersk incorporates streaming data from containers and ports to enhance shipping visibility and supply chain planning.

These examples show the diversity of value that real-time data brings—across first mile, middle mile, and last mile operations.

An increasing number of companies are using data streaming as the event-driven control tower for their supply chains. It’s not only about real-time insights—it’s also about ensuring consistent data across real-time messaging, HTTP APIs, and batch systems. Learn more in this article: A Real-Time Supply Chain Control Tower powered by Kafka.

Penske Logistics: A Leader in Transportation, Fleet Services, and Supply Chain Innovation

Penske Transportation Solutions is one of North America’s most recognizable logistics brands. It provides commercial truck leasing, rental, and fleet maintenance services, operating a fleet of over 400,000 vehicles. Its logistics arm offers freight management, supply chain optimization, and warehousing for enterprise customers.

But Penske is more than a fleet and logistics company. It’s a data-driven operation where technology plays a central role in service delivery. From vehicle telematics to customer support, Penske is leveraging data streaming and AI to meet growing demands for reliability, transparency, and speed.

Penske’s Data Streaming Success Story

Penske explored its data streaming journey at the Confluent Data in Motion Tour. Sarvant Singh, Vice President of Data and Emerging Solutions at Penske, explains the company’s motivation clearly: “We’re an information-intense business. A lot of information is getting exchanged between our customers, associates, and partners. In our business, vehicle uptime and supply chain visibility are critical.”

This focus on uptime is what drove Penske to adopt a real-time data streaming platform, powered by Confluent. Today, Penske ingests and processes around 190 million IoT messages every day from its vehicles.

Each truck contains hundreds of sensors (and thousands of sub-sensors) that monitor everything from engine performance to braking systems. With this volume of data, traditional architectures fell short. Penske turned to Confluent Cloud to leverage Apache Kafka at scale as a fully-managed, elastic SaaS to eliminate the operational burden and unlocking true real-time capabilities.

By streaming sensor data through Confluent and into a proactive diagnostics engine, Penske can now predict when a vehicle may fail—before the problem arises. Maintenance can be scheduled in advance, roadside breakdowns avoided, and customer deliveries kept on track.

This approach has already prevented over 90,000 potential roadside incidents. The business impact is enormous, saving time, money, and reputation.

Other real-time use cases include:

• Diagnosing issues instantly to dispatch roadside assistance faster
• Triggering preventive maintenance alerts to avoid unscheduled downtime
• Automating compliance for IFTA reporting using telematics data
• Streamlining repair workflows through integration with electronic DVIRs (Driver Vehicle Inspection Reports)

Why Confluent for Apache Kafka?

Managing Kafka in-house was never the goal for Penske. After initially working with a different provider, they transitioned to Confluent Cloud to avoid the complexity and cost of maintaining open-source Kafka themselves.

“We’re not going to put mission-critical applications on an open source tech,” Singh noted. “Enterprise-grade applications require enterprise level support—and Confluent’s business value has been clear.”

Key reasons for choosing Confluent include:

• The ability to scale rapidly without manual rebalancing
• Enterprise tooling, including stream governance and connectors
• Seamless integration with AI and analytics engines
• Reduced time to market and improved uptime

Data Streaming and AI in Action at Penske

Penske’s investment in AI began in 2015, long before it became a mainstream trend. Early use cases included Erica, a virtual assistant that helps customers manage vehicle reservations. Today, AI is being used to reduce repair times, predict failures, and improve customer service experiences.

By combining real-time data with machine learning, Penske can offer more reliable services and automate decisions that previously required human intervention. AI-enabled diagnostics, proactive maintenance, and conversational assistants are already delivering measurable benefits.

The company is also exploring the role of generative AI. Singh highlighted the potential of technologies like ChatGPT for enterprise applications—but also stressed the importance of controls: “Configuration for risk tolerance is going to be the key. Traceability, explainability, and anomaly detection must be built in.”

Fleet Intelligence in Action: Measurable Business Value Through Data Streaming

For a company operating hundreds of thousands of vehicles, the stakes are high. Penske’s real-time architecture has improved uptime, accelerated response times, and empowered technicians and drivers with better tools.

The business outcomes are clear:

• Fewer breakdowns and delays
• Faster resolution of vehicle issues
• Streamlined operations and reporting
• Better customer and driver experience
• Scalable infrastructure for new services, including electric vehicle fleets

With 165,000 vehicles already connected to Confluent and more being added as EV adoption grows, Penske is just getting started.

The Road Ahead: Agentic AI and the Next Evolution of Event-Driven Architecture Powered By Apache Kafka

The future of logistics will be defined by intelligent, real-time systems that coordinate not just vehicles, but entire networks. As Penske scales its edge computing and expands its use of remote sensing and autonomous technologies, the role of data streaming will only increase.

Agentic AI—systems that act autonomously based on real-time context—will require seamless integration of telematics, edge analytics, and cloud intelligence. This demands a resilient, flexible event-driven foundation. I explored the general idea in a dedicated article: How Apache Kafka and Flink Power Event-Driven Agentic AI in Real Time.

Penske’s journey shows that real-time data streaming is not only possible—it’s practical, scalable, and deeply transformative. The combination of a data streaming platform, sensor analytics, and AI allows the company to turn every vehicle into a smart, connected node in a global supply chain.

For logistics providers seeking to modernize, the path is clear. It starts with streaming data—and the possibilities grow from there. Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases.

The post How Penske Logistics Transforms Fleet Intelligence with Data Streaming and AI appeared first on Kai Waehner.

Real-Time Locating / Tracking System (RTLS) in Supply Chain and Logistics

RTLS is a key part of many use cases across verticals. Many manufacturing processes and supply chains rely on good real-time information of assets and people. But also, other innovative scenarios could not exist without RTLS. For instance, think about ride-sharing, car-sharing, or food delivery.

An RTLS enables identifying and tracking the location of objects or people in real-time. Some examples:

• Tracking automobiles through an assembly line
• Locating pallets of merchandise in a warehouse
• Finding medical equipment in a hospital
• Track tools, machines, people (if legal) in a construction area

An RTLS has three key goals:

• Improve safety
• Control security
• Optimize processes and productivity

Wireless RTLS tags are attached to objects or worn by people, and in most RTLS, fixed reference points receive wireless signals from tags to determine their location. However, more and more use cases require outdoors tracking, too. In many cases, a postmodern RTLS combines indoors and outdoors location tracking.

Challenges of Today’s Location and Tracking Systems

RTLS exist for a long time, already. Plenty of products are available on the market. While they differ in their characteristics and features, most traditional RTLS have at least some of the following technical challenges:

• Monolithic
• Proprietary
• Limited Scalability
• No Hardware Flexibility
• Single Purpose Solution
• Limited Integration Capabilities
• Limited Tracking Technologies

Many vendors invest in their RTLS system. Similarly to CRM, ERP, and MES systems, many of the next generation RTLS systems are based on Kafka to solve these challenges. So feel free to check the above characteristics with your favorite vendor and how they plan to solve (or have already solved) them.

Many enterprises prefer building their own custom postmodern RTLS. This approach allows an open, flexible solution. Custom RTLS are typically built to include innovative and differentiating features that add business value and optimize the business processes.

A Postmodern RTLS for Multi-Purpose Use Cases and Architectures

From my conversations with customers across industries, I learned that use cases and requirements for RTLS changed in the last years. In addition to solving the above technical challenges, Two key differences establish a postmodern view on how to define an RTLS:

1. RTLS is not just about location anymore. Applications leverage enhanced metadata such as speed, direction, or spatial orientation. Data integration and correlation is key for adding business value and improving processes.
2. The combination of indoors and outdoors via hybrid architectures enables multi-purpose RTLS.

Some examples for indoors location tracking: Asset tracking monitoring, non-linear production line, geofencing for safety (cobots) and distance enforcement (e.g., Covid 19). Outdoors track&trace enables regional or global logistics, routing, and end-to-end monitoring (e.g., construction areas).

A key requirement of modern RTLS is the ability to integrate with different technologies. This includes Location Tracking Technologies such as Radiofrequency (RF), Infrared (IR), RFID, Beacon, Wi-Fi, Bluetooth, UWB, GPS, GSM, 5G, etc. But that’s not all. The RTLS also needs to integrate with the rest of the enterprise reliably in real-time at scale. This includes MES, ERP, APS, CRM, data lakes, and many other applications.

Use Cases for a Postmodern RTLS

Many use cases exist to leverage a postmodern RTLS to improve processes or build innovative new applications that were not possible beforehand. Some examples:

• Locate and manage assets within a facility, such as finding a misplaced tool cart in a warehouse or medical equipment
• Notification of new locations, such as an alert if a tool cart improperly has left the facility
• Combine identity of multiple items placed in a single location, such as on a pallet
• Locate customers, for example, in a restaurant, for delivery of food or service
• Maintain proper staffing levels of operational areas, such as ensuring guards are in the proper locations in a correctional facility
• Quickly and automatically account for all staff after or during an emergency evacuation
• Automatically track and time stamp the progress of people or assets through a process, such as following a patient’s emergency room wait time, time spent in the operating room, and total time until discharge
• Clinical-grade locating to support acute care capacity management
• Replay past events to understand the mass movements of workflows
• Plan future location requirements
• Auditing for compliance cases
• Etc.

Two important notes here:

1. Many use cases exist for a long time already. But once again: Check out the challenges discussed above. The requirements change regarding scale, flexibility, and other characteristics.
2. As you can see, most of these use cases do not just require location tracking but also data correlation in real-time. That’s where the optimization or added business value comes from.

Vehicle Tracking System in other Industries

Transportation and logistics are the obvious industries for real-time tracking systems. But industries not traditionally known to use vehicle tracking systems have started to use it in creative ways to improve their processes or businesses. Here are a few examples:

• The hospitality industry has caught on to this technology to improve customer service. For example, a luxury hotel in Singapore has installed vehicle tracking systems in their limousines to ensure they can welcome their VIPs when they reach the hotel.
• Vehicle tracking systems used in food delivery vans may alert if the temperature of the refrigerated compartment moves outside of the range of safe food storage temperatures.
• Car rental companies are also using it to monitor their rental fleets.

The following sections explore an example using the scenario around transportation and logistics with truck delivery. Let’s look at how Apache Kafka and Event Streaming can help implement a postmodern RTLS.

Kafka-native Real-Time Locating / Tracking System (RTLS)

The following picture shows a multi-purpose Kafka-native RTLS for transportation and logistics:

The example shows three use cases of how produced events (“P”) are consumed and processed:

• (“C1”) Real-time alerting on a single event: Monitor assets and people and send an alert to a controller, mobile app, or any other interface if an issue happens.
• (“C2”) Continuous real-time aggregation of multiple events: Correlation data while it is in motion. Calculate average, enforce business rules, apply an analytic model for predictions on new events, or any other business logic.
• (“C3”) Batch analytics on all historical events: Take all historical data to find insights, e.g., for analyzing issues of the past, planning future location requirements, or training analytic models.

The Kafka-native RTLS can run in the data center, cloud, or closer to the edge, e.g., in a factory close to the shop floor and production lines.

Hybrid Kafka Architecture for Transportation and Logistics for RTLS and Track&Trace

One of the benefits of Apache Kafka is the freedom to deploy the infrastructure as needed. On the one end, Kafka can be deployed as a single broker in a vehicle (like a truck or train). On the other end, a global Kafka infrastructure can spread multiple cloud providers, regions, countries, or even continents and integrate with tens or hundreds of factories or other edge locations. The reality is often somewhere in the middle. Most enterprises start small and roll it out across locations and countries over time.

The following shows a pretty powerful hybrid architecture for a Kafka-native RTLS:

In the above scenario, the hybrid architecture includes:

• A 5G infrastructure with public telco and private 5G Campus networks
• Confluent Cloud as fully-managed event streaming platform in the cloud
• Confluent Platform deployed at the edge in the 5G Campus leveraging AWS Wavelength
• Real-time integration with assets and people at the edge and in the cloud
• Real-time integration with enterprise applications such as APS, CRM, or ERP systems
• Data correlation of edge and cloud data (replicated bi-directionally in real-time with tools such as Confluent’s Cluster Linking or Apache Kafka’s MirrorMaker 2)

This is obviously just one sample architecture. Again, you are totally free to design your own architecture with the components and technologies you need for your use cases.

An RTLS system is heavily connected to the whole Supply Chain Management (SCM) process. As Kafka plays a key role in many supply chains, it is also a perfect fit for building real-time asset tracking.

Let’s now move over to two public use cases for location-based transportation and logistics with Kafka-native technologies.

Example: Bosch – Location-based Construction Site Management

The global supplier Bosch has a track&trace application leveraging Apache Kafka and Confluent Cloud: Construction site management analyzing sensors, machines, and workers.

Use cases include collaborative planning, inventory and asset management, and track, manage, and locate tools and equipment anytime and anywhere:

The example is close to the hybrid architecture I showed in the last section. The solution spans multiple construction areas in various regions and integrates with the event streaming platform running in the cloud.

Let’s now take a look at another advanced use case for a real-time location service.

Location-Analytics and Geofencing with Kafka and ksqlDB

A geofence is a virtual perimeter for a real-world geographic area and is used for location-analytics in real-time. A geo-fence could be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as school zones or neighborhood boundaries).

The use of a geofence is called geofencing. One example of usage involves a location-aware device of a location-based service (LBS) user entering or exiting a geo-fence. This activity could trigger an alert to the device’s user and message to the geo-fence operator. Or, in the case of a factory, it could enforce distancing during Covid 19 times.

Guido Schmutz from Trivadis has done great work on this topic: “Location Analytics and Real-time Geofencing using Apache Kafka and KSQL“. It is actually quite simple to implement with KSQL:

These ksqlDB queries create continuous stream processing that analyses and correlates sensor data in motion in real-time. As ksqlDB is a Kafka-native technology, it is possible to process millions of events per second in a reliable, scalable, and secure way.

Example: Lyft – Real-Time Map-Matching to Provide Accurate Locations

The ride-sharing giant Lyft shared a great example for location analytics in real-time. Lyft implemented map-matching to track customers based on the GPS information of the mobile app.

Lyft has “two main use cases for map-matching:

1. At the end of a ride, to compute the distance traveled by a driver to calculate the fare.
2. In real-time, to provide accurate locations to the ETA team and make dispatch decisions as well as to display the drivers’ cars on the rider app.“

As the signal is often weak, Lyft enhanced and correlated the data with other data sets to get more accurate information. For instance, Lyft also uses location data from public free Wi-Fi hotspots close to the customer.

This is a great outdoors example of a modern, scalable RTLS. And once again, this example shows that the real added value of real-time data is the data correlation. It does not help if you only use real-time messaging and process the data in batch mode in a data lake.

Open, Scalable, Multi-Purpose, Real-Time RTLS based on Kafka is the New Black

Real-Time Locating System (RTLS) enables identifying and tracking the location of objects or people in real-time. This is not a new problem. But the requirements changed…

A postmodern RTLS provides an open architecture and high scalability. For this reason, more and more RTLS implementations rely on Apache Kafka as an open, scalable, and reliable event streaming platform.

Last but not least, if you wonder what the term “real-time” actually means in “RTLS” (no matter if Kafka-based or not), check out the article “Apache Kafka is NOT Hard Real-Time BUT Used Everywhere in Automotive and Industrial IoT” to understand what real-time really means.

What are your experiences with RTLS architectures and applications? Did you already use Apache Kafka? Which approach works best for you? What is your strategy? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

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