Tracking ore using physical trackers involves attaching a physical device to the ore to collect data about its location and movement. This data is then used to track the ore’s progress through the supply chain.
On the other hand, tracking ore using machine learning involves using algorithms and statistical models to analyze data about the ore’s location, movement, and other relevant factors. This data is then used to predict the ore’s future behavior and to make informed decisions about its management and transportation.
The main difference between the two methods is that physical tracking relies on directly measuring the ore’s physical location, while machine learning relies on analyzing data and making predictions based on patterns and trends. Physical tracking is typically more accurate in the short term, but can be less effective in predicting future behavior. Machine learning, on the other hand, is less accurate in the short term, but can provide more insight into long-term trends and patterns.
