Predictive maintenance leverages advanced data analysis and machine learning to monitor equipment health, enabling early detection of emerging faults before they lead to costly downtime or catastrophic failures.
The world faces significant public health challenges, from the rise of antibiotic resistant infections and emerging viral diseases to the ongoing burden of chronic conditions like cancer, diabetes and cardiovascular disease. Addressing these issues requires faster, more efficient and more reliable drug discovery pipelines.
As the global economy increasingly adopts green energy as an alternative to non-renewable resources, the demand for better production technologies steadily rises. To minimize the downtime cost of critical production devices in the field, a major manufacturer has hired Koehn AI to develop a predictive maintenance system.
Our team empowered the client's Supply Chain Management (SCM) division to significantly enhance their demand forecasting capabilities across all products, setting a foundation for data-driven, future-proof planning.
