Ascendion designed an IOT solution to capture plant and machine data from factories, transform the data, and store it in Microsoft Azure Data Lake (ADL). Our engineers defined the data architecture, the setup, and analytical services for real-time sensor data ingestion, processing, anomaly detection, regression prediction, and visualization.
Sub-assemblies in a manufacturing unit are crucial to the entire process. These are beneficial in cost and quality control as they help break down the manufacturing process into simple, efficient tasks.
Ascendion experts had to engineer accurate failure predictions to maintain an optimal inventory and ensure minimal downtime during production.
We used Ascendion AVA, an integrated, intelligent engineering platform, to seamlessly load data from 80,000 sensors in one factory into the ADL. The solution helped transform data, covering over 25 use cases for the data science and analytics team to consume.
Ascendion developed the AI/ML platform infrastructure and constructed an implementation roadmap for the client. The data architecture and management strategies used resulted in an increased lifespan of the equipment by 6 to 8 months and many other successful results:
The production helped to predict downtime or failures and manufacturing process issues while also providing proactive maintenance leading to higher quality, faster time-to-market, and optimized costs.
Tech Stack: Azure Data Lake, Azure VM, Azure IoT hub, Aspen, SAP, SQL managed DB
Case studies
Case studies
Case studies
Case studies
Case studies
Case studies