By supplementing faulty company data with census and geo-data, our multimodal model improves the classification of recipient addresses.
challenge
Precise address classification is crucial for efficient logistics. The company was faced with the problem of not being able to correctly classify recipient addresses, which required a great deal of manual correction and placed a heavy burden on operational processes. Particularly for international addresses, the variety of languages exacerbated the problem and made manual classification cumbersome and time-consuming. In addition, the historical shipping data had incorrect classifications.
approach
Our approach uses machine learning to automate and optimize address classification. The core strategies are:
- data enrichment: We improve training data quality by integrating additional information from external sources. This compensates for common errors in historical shipping data.
- Multilingual computing: Our system processes data independently of language, which enables rapid expansion into new markets.
- Multi-modal machine learning model: We use not only text data, but also geofeatures and satellite images to improve the performance of our model.
upshot
By integrating our system into address classification, the manual correction effort can be drastically reduced. This simplifies route planning, tariff application and billing by the logistics service provider. Error costs when distinguishing between B2B and B2C recipients were reduced by 46%. The project impressively demonstrated how machine learning can overcome traditional logistical challenges and highlighted the potential of such technologies to transform traditional operational processes in the logistics industry.
