Читать книгу Artificial Intelligent Techniques for Wireless Communication and Networking - Группа авторов - Страница 70

Machine learning uses massive computer power to classify feature vectors that people never see and develop to become smarter and more accurate in real time with new data. Machine learning and self-learning are very common concepts in industries such as automated pattern recognition, e-discovery and sensor data processing. While machine learning in the logistics sector has been particularly sluggish, other insightful companies have systems for self-learning.

Machine learning uses data from various systems and data sets. The system brings together all data in the logistics context inside the carrier network. The strength of machine learning is the integration of information through different systems and data sets. In order to improve the accuracy of shippers’ forecasts of demand, predictive patterns in supply chains, seasonal calendars and daily tracks in lines, we are able to integrate all the information we have inside our carrier network with external sources of data, such as GPS, historical price rates and FMCSA.

When they get more data over time, self-learning logistics systems enhance their algorithms. The device operates by identifying data patterns, analyzing them, and issuing specific reports or behavior. Handwritten text is decoded by common use cases for machine learning and logistics. These self-learning logistics are also commonly used by the post office, as are major shipping companies such as UPS and FedEx.

We use educational approaches in the logistics industry to make faster and better decisions, helping suppliers to boost cost saving, classification, routing and tracking processes for the carriers. Machine learning will assist you to solve an issue that you don’t realize has thousands of disparate data points gathered and evaluated. Analytics focused on master learning and self-development will recognize complex attributes such as the environment or traffic over time in order to detect patterns that people may not see.

Intelligent warehouses are a newer advancement of self-learning systems. These systems detect trends and events repeatedly, analyze data over time, connect data to entities, such as deliveries and clients, and initiate pre-pack instructions. Another popular example is AI and robotics which check inventory levels to rearrange and restore as needed. Self-learning over time helps the machine to refine its algorithms for even more detailed responses.