Manufacturers need to maintain highly demanding standards of operational precision, efficiency, safety and quality. They need AI that can navigate complex global supply chains and leverage vast IoT data to continuously improve performance. But current machine learning systems fail to separate signal from noise in manufacturing time-series data.
causaLens uses Causal AI to find the real drivers of supply chain dynamics and the manufacturing value chain, enabling leading manufacturers to solve their biggest challenges.
Operators often still rely on their experience, intuition and judgement. However a multitude of signals need to be monitored all at the same time, resulting in urgent activities getting prioritized even though they don’t necessarily add value. Manufacturers are turning to Machine Learning to help ease this burden, but current AI techniques often fall short:
Efficient predictive maintenance can only be achieved with root cause analysis. Existing correlations-based predictive maintenance models often fail to distinguish between causal relationships and spurious correlations. At the same time they fail to adapt to changing conditions in the environment. causaLens autonomously discovers causal relationships in real-time enabling businesses to optimize the maintenance of their systems
Directly tune your production level to the most profitable one. causaLens autonomously predicts demand for products in real-time and takes into account the cost and profitability of the production lines in order to optimize the production level. It also allows you to seamlessly add and analyze macroeconomic data in your models to enhance your infrastructure investment planning.
Improve yields and customer satisfaction by detecting faulty products faster. causaLens helps you autonomously understand the areas that are at higher risk in real-time. It also empowers users to automatically assess the impact of potential interventions in the production line without the need to resort to costly trial & error.