Background

Machinery is a critical asset for manufacturing companies, and keeping it functional is vital to doing business. Equipment downtime accounts for billions of dollars lost globally every year. Repairs are often based on time and usage, which is limited in accurately assessing equipment health. This leads to unexpected failures and perfectly good equipment being replaced—resulting in unproductive workers and unhappy customers.

Opportunity

Using IoT sensor data, manufacturing companies can apply a more sophisticated, risk-based methodology to their maintenance scheduling. Artificial intelligence (AI) and machine learning (ML) models can analyze equipment sensor data to identify maintenance needs from things like temperature, vibration, oil, and sound. Diagnosing machinery issues before a breakdown reduces downtown, increases operational efficiency, and generates bigger cost savings.

Impact

Implementing a fast, accurate, reproducible, and cost-effective predictive maintenance framework is the power needed to run efficiently and hold a competitive edge in the industry. See how Anaconda Enterprise can improve data science workflows in manufacturing by booking a demo.

Through the power of Anaconda Enterprise, National Grid has been able to streamline data science workflows and build reproducible maintenance models to reduce costs and improve safety and reliability.