ChatGPT – An all-purpose tool for logistics?

ChatGPT has been causing quite a stir since its release last year. The artificial intelligence from the company OpenAI can simulate conversations in real time. In doing so, the application draws on a huge wealth of knowledge that it has tapped into through machine learning and natural language processing.

Due to the wide range of possible applications, the question arises as to whether the chatbot prototype can also be used in the world of logistics and transportation in the future. In this InfoSnack, SpediFux presents some possible applications:

• Inventory and maintenance management: By integrating appropriate sensor technology, automatic reports on stock levels can be generated and reorders suggested. The application can also forecast the expected wear and tear of devices and vehicles. This allows companies to plan maintenance periods proactively.
• Language translation: Shipping documents, instructions and customer inquiries can be translated automatically for shipments to and deliveries from abroad.
• Programming: AI's understanding of language also extends to numerous programming languages. This means that companies can create software solutions cost-effectively and independently and are not reliant on external service providers.
• Route optimization: ChatGPT could be used to generate optimal transport routes that take into account factors such as traffic, weather and time slots for delivery.

These are just some of the possible uses of ChatGTP in logistics. It should be noted that the chatbot is not yet fully operational and all responses must be carefully checked for errors. It remains to be seen how the application will develop and how it will actually be used in the future. In addition, the competition is working flat out on alternative chatbot solutions.

But one thing is certain: the prototype is already proving its worth in many areas. For example, the company Heureka recently announced the use of the software to automate customer search queries for KPIs and thus reduce programming effort. Further development remains exciting!