From optimizing supply chains to setting prices, the world’s major car producers require accurate data to make good decisions. That’s why they employ analysts, statisticians, and other specialists in every business unit.
These analysts and statisticians can only use their data, however, if it’s been properly integrated in a common system. If their data is out of date or incomplete, their predictions are unreliable. When it comes to negotiating contracts, optimizing supply chains, or identifying manufacturing defects, the slightest errors can significantly disrupt operations.
First, we built a data foundation using a combination of big data technologies and open-source software. Many of the customer’s datasets were previously incompatible, which made analysis impossible. On top of the data foundation, our team deployed machine learning capabilities like clustering algorithms, ensemble methods, and other predictive techniques for real-time analysis of car, driver, market, and supplier data.
In addition, with all of its data in one place, the car company can experiment with new use cases for predictive analytics, such as marketing or operations.