Background and initial situation

A large retail company operating in several countries was facing challenges in the area of logistics and supply chain management. Employees needed regular updates on the status of shipments, stock availability and real-time supply chain information. The manual processing of these requests led to delays and increased workload.

Aims of the project

The main objective of the project was to improve the efficiency of logistics and supply chain management by automating recurring requests and providing real-time information. Specific objectives included:

  • Automation of shipment status and stock availability requests.
  • Reduce processing time for logistics requests.
  • Improving transparency and traceability along the supply chain.
  • Relieving logistics employees of administrative tasks.

Solution implementation

To achieve these goals, the AI was integrated into the company’s existing logistics and supply chain management systems. The implementation process followed a clearly structured plan:

a) Needs analysis and planning:
  • Identification of the most frequent requests: By analyzing historical logistics data, the most frequent requests were identified, e.g. shipment tracking, stock queries and delivery times.
  • Determination of integration points: The relevant systems, including the warehouse management system (WMS), the enterprise resource planning (ERP) system (e.g. SAP) and the transportation management system (TMS), were determined.
b) Development and integration:
  • Creation of call workflows: Development of specific workflows for processing inquiries regarding shipment status, stock levels and delivery times.
  • Technical integration: The AI was seamlessly integrated into the WMS, ERP and TMS.
c) Training and knowledge database:
  • Data import and structuring: Historical logistics data and frequently asked questions were used to expand the AI’s knowledge base.
  • Regular updates: Planning regular updates to the knowledge base to ensure the relevance and accuracy of the information provided.

Application example and process scenario

Scenario: Shipment tracking request

  • Step 1: A logistics employee requests information on the current status of a shipment via the internal chat system.
    – Employee: “AI, please give me the current status of consignment number ABC123.”
  • Step 2: The AI processes the request and retrieves the current shipment status from the transport management system (TMS).
    – AI: “One moment please, I’m checking the status of consignment ABC123.”
  • Step 3: The AI provides the requested information.
    – AI: “The shipment ABC123 was sent on 02.10.2023 and is currently in transit. The expected delivery date is 05.10.2023. The current location is the Frankfurt distribution center. Would you like more details?”
  • Step 4: The employee states what exactly they need.
    – Employee: “Yes, please send me the last status update and the carrier’s contact information.”
  • Step 5: The AI provides the requested details and contact details of the carrier.
    – AI: “The last status update of shipment ABC123 was on 03.10.2023 at 14:00 in Frankfurt. The carrier is DHL, and the contact information is +49 123 456 789. Is there anything else I can do?”
  • Step 6: The employee thanks you for your help.
    – Employee: “No, that’s all. Thank you very much!”
  • Step 7: The AI documents the request and the information provided in the transport management system (TMS).
    – AI: “You’re welcome! Your request has been documented in the system. Please get in touch if you need further assistance.”

Results and benefits

Following the implementation of AI, there were significant improvements in logistics and supply chain management:

  • Reduced response times: The average processing time for logistics requests decreased by 70%.
  • Improved transparency: Real-time supply chain information was available at all times, increasing transparency and traceability.
  • Increased efficiency: Logistics staff were able to focus on more strategic tasks as repetitive requests were handled by AI.
  • Increased accuracy: The information passed on to employees was always up-to-date and accurate.

Conclusion

The integration of AI into the company’s logistics and supply chain management processes led to a significant increase in efficiency and satisfaction for both employees and customers. By automating repetitive tasks and providing up-to-date information, valuable time was freed up and the quality of logistics work was significantly improved. Future enhancements could include the implementation of additional analytics to optimize the supply chain and the integration of further communication channels to cover an even broader range of logistics requests.