The goal was to create a centralized solution that would encompass all processes from delivery, RTTV, booking, and yard management, and allow for centralized operation.

It was important to integrate with SAP EWM and ERP, so an SAP-based approach was initially considered. However, to ensure the desired level of customization and flexibility, we ultimately decided to implement in the cloud.

Together we were able to create a fully integrated process flow between SAP ERP, EWM, and myleo/dsc. The cloud approach also enabled numerous automations, such as partial and full notification via a bot, or more efficient gate utilization through dynamic time window calculation based on notified carriers and materials. All information could be centrally consolidated in one solution, eliminating the need for document drives. By reintegrating with SAP EWM, delivery documents can still be created for physical warehouse processes. Overall, logistics processes have already become 15 percent more efficient.

The customer already had several yard and transportation management systems in place, but due to the high volume of deliveries and pick-ups, they were unable to significantly improve productivity or reduce waiting times. The integration of AI was to address this issue.

With the help of AI and AI-based OCR cameras, all steps on the factory premises could be transparently mapped, enabling comprehensive monitoring. The AI-based gate process including time slot booking and pre-registration reduced registration times from 30 minutes to 3 minutes, and logistics personnel only need to intervene in exceptional cases. In addition, AI is now used to retrieve vehicles to the loading bays, allowing them to be fully automated and sequenced.

BMW MC wanted to implement a cloud-based, real-time supply control system to better monitor criticality and range issues. Full integration with SAP ERP, tight carrier integration, and continuous ETA calculation were essential. In addition, weather data and traffic information needed to be incorporated to calculate delays through root cause analysis.

With the newfound visibility and integration, missing parts and insufficient availability can now be proactively identified and quick alternatives sought. Wait times were reduced and gate capacity could be managed more effectively. In addition, manual follow-ups and inquiries were eliminated and all delivery processes were integrated into a central tracking platform.

The automotive industry’s highly sensitive and expensive products rely on transparent, predictable logistics. Achieving this can be challenging, especially with rail transportation. The client’s wish was to create a seamless and transparent delivery process specifically for electric batteries to European manufacturing plants.

While much information was already available through IoT sensors installed in rail cars, without data linkage, it did not add value to the logistics process. To connect asset and transportation data, we developed a solution that aggregates and compares multiple sources on a single platform. This solution enables anomaly detection, precise routing of railcars at receiving stations (automotive plants), and creates a new level of visibility.

The customer already had several yard and transportation management systems in place, but due to the high volume of deliveries and pick-ups, they were unable to significantly improve productivity or reduce waiting times. The integration of AI was to address this issue.

The ability to integrate is fundamental to a successful transformation, especially in the context of disparate cloud applications and data sources such as telematics and forwarding systems. Despite the innovations, existing functions and processes must continue to work seamlessly. Seamless integration with SAP, integrated carriers (API, EDI, etc.), and high coverage of tracking rate, expected milestones, and quality of arrival notifications in Track & Trace are critical in this case. Much of this goes beyond an IT project; it also involves people who are constantly optimizing and preparing for the next transformation.