Manufacturing is undergoing a dramatic transformation as companies instrument their factory equipment with network-connected sensors and employ sophisticated data analytics to better understand and optimize production. The techniques offer new opportunities to improve key business performance metrics, derive new value from manufacturing data, improve product quality, and enhance the way information and knowledge are managed in an organization.
Manufacturers have always been keen to improve the business of production, but as companies employ these new data-driven techniques they are faced with a new challenge: how to find and respond to actionable insights in their manufacturing information. Conventional computing can’t scale with the
large influx of data or the complexity of the analytics, which limits the data’s applicability and benefits.
Cognitive manufacturing—also known as smart opportunities to harness manufacturing data so companies can realize their modernization objectives. Cognitive manufacturing uses cognitive computing techniques along with Industrial IoT (IIoT) applications and advanced analytics to evaluate data in real time from multiple systems, equipment, and processes. It then automates or guides manufacturing accordingly. Companies that use cognitive manufacturing
technologies can discover previously unknown issues or problems in their manufacturing processes to improve the life cycle of a product, from design through manufacturing to after-market support.
This paper explains the importance of cognitive manufacturing while highlighting four fundamental manufacturing applications: asset performance management, process and quality improvement, resource optimization, and supply chain optimization. The paper also describes a technical architecture for cognitive manufacturing and suggests steps companies can take to begin developing strategies for implementation.