Precise Forecasting, Efficient Supply Chains
The machine learning models developed through Cloud Interactive not only need to integrate with customers' existing systems but also accurately predict mineral elements, making this approach an industry pioneer. Furthermore, the application of machine learning can be extended to benefit other aspects of manufacturing, such as forecasting material demand and improving Maintenance, Repair, and Operations (MRO), bringing significant innovation to the manufacturing industry.
Customer Profile
FLSmidth, headquartered in Copenhagen, Denmark, is a global engineering company that sets benchmarks in the mining and cement industries. Cloud Interactive developed customized machine learning software for FLSmidth, replacing the standardized multivariate regression analysis model previously used in PX-Cal. This new software offers more accurate predictions for raw material demand, maintenance forecasts, and supply chain optimization.
Challenges
The use of machine learning and deep learning has become crucial for predictive modeling in various technological and engineering fields. In response to this trend and customer demand, Cloud Interactive integrated industry data to build machine learning models and enhance existing calibration software, addressing FLSmidth's challenges. By employing advanced algorithms to predict elements in mineral processing, we have optimized the complex and precise cement beneficiation process.
Solutions
Cloud Interactive data science team organized vast amounts of industry data, handling everything from data collection and data import to analysis and forecasting. By combining various machine learning models, we have developed a more accurate and scalable beneficiation system for FLSmidth, enhancing the existing multivariate regression analysis model they currently use.
