Logistics: How the Industry Can Withstand Growing Pressures with Kafka

Customers want to track their packages in real-time. This isn’t new, but the intensity is changing. Previously, hourly updates were sufficient. Now, companies must be able to transparently show where packages are and when they will arrive at their recipients – accurate to the quarter hour.

For international shipments, customs authorities want to be informed about shipments even earlier. It’s about more than just electronic pre-notification. Customs will want to receive even more data in the future.

Additionally, the business with large retailers is changing. For cost reasons, these companies want to handle large segments of logistics themselves. This means: only relying on established services for the last mile. This makes an already difficult-to-plan business even harder to anticipate. After all, any traffic jam or rail damage can throw companies off course.

However, it would be incorrect to speak of new challenges. Analytically speaking, it’s more of an intensification. Or, more dramatically, an escalation. The problem is as follows:

Even though delivery services have made efforts in IT, the growth of data structures has not kept pace with business volume. This creates a complex task.

How can the central data highways be restructured during ongoing operations – and without disrupting daily business?

The expected data volumes will grow massively. Some logistics companies expect more than a hundredfold increase. How could systems handle this load?

Additionally, data needs to be available faster – ideally in real-time, to the second.

With Apache Kafka, these challenges can actually be solved. As an expert and trainer, I have the following expectations:

Not only will the amount of data increase significantly. The same applies to the number of external systems involved. On their data highways, companies must not only transport more data load. Interfaces – analogous to on and off ramps – must also be quickly integrated.

Although there is more data traffic, it must flow even more smoothly than before. Only then will information be available in real-time.

Data protection remains important in this context. Many participants may only receive and view very specific data. Information must be individually packaged before sending. And this must happen quickly enough to fulfill the real-time promise.

Not only does management want to receive analyses, but logistics centers do as well. However, they need it even faster. They need to know what package load they must manage in the coming minutes/hours/days.

In many corporations, Apache Kafka serves as the central platform for data exchange. Some companies even refer to Kafka as a "package data operating system."

Compared to previous systems, Kafka can scale to handle significantly higher data volumes. Even when data volumes multiply, Kafka won’t become a bottleneck in the system.

With Kafka, companies can route data to many different systems in near real-time. This allows various teams to independently access the same data. The motto: centralize data – democratize data access. Of course, this is only within the company with predefined permissions.

Kafka enables more agile methods when it comes to data access. For some customers, the time from access request to implementation is reduced from 4-5 weeks to 1-2 days.

Kafka is surprisingly robust. After the initial implementation, Kafka often "just" runs – on its own. Only with very large data volumes are additional measures necessary.

Kafka alone is not enough – it’s just the center. A formulation I prefer: the data hub. Once Kafka is installed and people can operate this tool, there are numerous complementary solutions in the ecosystem.

Through them, not only will private households find package shipping less stressful and more convenient. So will the logistics companies themselves.

Soon, this blog will be expanded with a case interview from the industry.