CONTEXT: TRADITION MEETS INNOVATION

Ernst Kähler GmbH, known as Sahne Kähler, has been supplying restaurants, canteens, schools, and hospitals in Hamburg with fresh dairy products, fruit, vegetables, and other fresh produce for almost 100 years.

Sebastian Kähler, Managing Director in the 4th generation, pursues a clear strategy: respect proven processes where they work, and selectively modernize where it brings real added value.

"We are a fourth-generation family business, but we are not afraid of new technologies as long as they make life easier for our employees, not more complicated." Sebastian Kähler, Managing Director

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CHALLENGE: MANUAL DEPOSIT AND RETURNS HANDLING

As with most wholesalers, the documentation of goods delivery was done via physical delivery notes. Drivers manually record the returnable goods exchange, deposit, and returns in handwriting on the delivery notes. This is a proven and audit-proof procedure that works in the hectic driving environment.

“Drivers are some of our most important employees, and we do a lot to retain them long-term. Most drivers don't like apps, which are often not very practical in a hectic environment.”

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However, for the administration, this process meant a high manual effort. Around 500 delivery notes had to be viewed, evaluated, and manually transferred to SAP. In addition to the high time workflow, the process was error-prone and unpopular among colleagues in the administration.

SOLUTION: LOGISTICA OS's AI AGENTS

Logistica OS's AI solution now digitizes the entire process of empty containers and reclamations handling. In the first step, all delivery notes are scanned without pre-sorting. The AI automatically recognizes which documents are relevant for deposit and returns accounting and filters out irrelevant documents such as invoices or packing lists.

The Logistica AI agent then reads all relevant data fields, including tour number, date, customer, as well as the type and number of exchanged loading equipment. The system also recognizes handwritten entries, “thinks along” in special cases, and improves with each run, which distinguishes it from classic machine document processing methods.

The system marks missing or uncertain information, allowing employees to specifically check and supplement it. Thus, instead of manually checking every delivery note, employees can focus on a few exceptions.

Through close collaboration with the Logistica product team, the platform was further developed so that returns, i.e., incorrectly delivered or defective goods, are also recognized by the AI and corresponding processes are initiated. This not only leads to further time savings in administration but also increases service quality because inquiries are processed more quickly.

RESULT: FAST IMPACT AND HIGH ACCEPTANCE

After just a few weeks, the time required for the manual review of the delivery notes was reduced by at least 50%. The system achieves a data reading accuracy of 80-90% by the AI, which has led to fewer incorrect bookings and corrections in post-processing.

Due to its simple operation and ability to learn, the solution is particularly popular with operational employees in the head office.

“The time saving is at least 50%, and I use the tool daily. I was particularly surprised by how quickly the implementation went and how well the system learns. I would have expected a much longer process.” (Timo, Administration)

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A previously unpopular, error-prone process now runs digitally and automatically, without changing existing procedures in the driving operation.

CONCLUSION

Sahne Kähler shows that even traditional, family-run companies can use AI pragmatically and effectively, even without tedious implementation. Through the close collaboration of both teams, the solution went live just 14 days after the contract was signed and quickly generated measurable added value.

"Logistica's AI was super easy to implement. From the decision to introduce the system until go-live was 14 days. I would recommend Logistica's AI to anyone who wants to streamline and improve the deposit and returns accounting process.”

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Since the AI continuously learns from exceptions and constantly improves its accuracy, an early start pays off particularly well.

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