Cognitive manufacturing completely utilizes data residing across equipment, systems, and processes to extract actionable insight across the entire value chain through various processes ranging from design to manufacturing to support activities.
Fremont, CA: Industry 4.0 has finally arrived as factories and machinery become smarter and more equipped with emerging technology such as IoT, AI, and Cognitive Automation. Industry 4.0, a term invented in Germany to describe the computerization of production, has now become a worldwide phenomenon. China and Japan have also coined their own campaigns, appropriately dubbed Made in China 2025 and the Industrial Value Chain Initiative, stressing the use of data and automation for business benefits. As business data volumes increase, conventional computing will give way to emerging technology to tackle the massive influx of data and the complexities of analytics, paving the way for Cognitive manufacturing, also known as the Industrial Internet of Things, Factory Digitization, or Industry 4.0 revolution.
Making way for Cognitive Manufacturing
Cognitive manufacturing completely utilizes data residing across equipment, systems, and processes to extract actionable insight across the entire value chain through various processes ranging from design to manufacturing to support activities. Built on the foundations of IoT and using analytics combined with cognitive technology, Industry 4.0 or cognitive manufacturing drives key productivity changes in the manufacturing environment's reliability, quality, and performance. So, how does cognitive manufacturing impact the manufacturing industry? It accomplishes this in three distinct ways, as detailed below:
1. By deploying smart analytics, connected sensors, and cognitive skills, intelligent assets and equipment can interact, sense, and self-diagnose problems, reducing excessive downtime and optimizing performance levels.
2. Cognitive processes and operational performance are improved by evaluating a wide range of knowledge from processes, workflows, context, and the environment to drive quality, thus improving operations and decision-making.
3. Smarter resources and resource management by integrating data from multiple sources such as use, people, and location parameters and deploying cognitive insights to maximize and leverage resources.