Cognitive manufacturing uses cognitive computing, the Industrial IoT, and advanced analytics to optimize manufacturing processes in ways that were not previously possible. It helps organizations improve fundamental business metrics such as productivity, product reliability, quality, safety and yield, while reducing downtime and lowering costs.
What It Is and Why It Matters
Cognitive technologies look deeply into a manufacturing process and business environment to derive information that has tangible value for a manufacturer.
Cognitive manufacturing leverages cognitive computing, the Industrial IoT, and advanced analytics to digitize, understand and optimize manufacturing processes in ways that were not previously possible.
Cognitive manufacturing is powerful because it combines sensor-based information with machine learning and other artificial intelligence capabilities to find patterns in structured and unstructured data from plant, enterprise and industry systems. It pulls relevant information together in real time and applies analytics to yield unprecedented levels of understanding and insights about the manufacturing process. It automates responses based on its findings and delivers actionable information as well as continuously updated knowledge to decision makers in the manufacturing setting.
These sophisticated capabilities are possible today because technologies facilitating the IoT and data analytics engines are mature and can be implemented at scale, thanks to pervasive connectivity and reduced costs for chipsets, sensors, cloud computing, and storage. The capabilities are also necessary if companies expect to take advantage of the skyrocketing volumes of data their IoT applications are generating, from the data collected by sensors to unstructured data contained in text files, correspondence, videos, audio and other sources.
Cognitive technologies can find meaning in this data in ways that, until now, only the human brain could comprehend. This level of understanding will be considered essential for success in the modern manufacturing era as heightened competitiveness and cost sensitivities demand new levels of agility, responsiveness and innovation from manufacturers.
Key Issues Cognitive Technologies Address for Manufacturers
Manufacturers can use cognitive technologies to solve fundamental business challenges, find new value in their manufacturing data, improve quality and enhance knowledge management in their organizations.
Solving business challenges: Cognitive manufacturing helps organizations improve fundamental business metrics, such as productivity, product reliability, quality, safety and yield, while reducing downtime and lowering costs. Applications can be easy to use and generate
immediate benefits. For example, a field technician sent to repair a machine can simply submit symptoms into a cognitive engine that will then analyze the symptoms and suggest a set of repair options ranked in order of probability of success. The approach improves the first time fix rate, which improves productivity and reduces costs.
Creating new value from manufacturing data: Cognitive technologies look deeply into a manufacturing process and business environment to derive information that has tangible value for a manufacturer. The process considers new data sources as well as unstructured data and applies advanced analytical models to find significant relationships that weren’t revealed in the data before. By using cognitive technologies, predictive maintenance activities—previously reliant on historical data—can be enriched with information found in technician logs, maintenance records, emails and other sources. Product inspections can be performed by cognitive visual inspection systems that learn from
pictures of manufactured products to identify defects and determine if the defects are tied to quality issues. Companies that use these types of techniques can improve plant operations and performance and reduce costs as well.
Improving product quality: Successful manufacturers are always striving to build the best-quality products. In a recent survey of electronics manufacturers, for example, IBM found that two-thirds (66%) of company executives consider minimizing defects and achieving a higher rate of accuracy in production to be key performance indicators for their facilities. Cognitive manufacturing enables companies to put a laser-like focus on quality throughout the life cycle of a product’s development—from design through manufacturing and even after distribution when companies must ensure product quality through warranty and support programs. The approach improves yield, reduces overall warranty costs, and helps ensure customer satisfaction for the lifetime of a product.
Enhancing knowledge management: Cognitive manufacturing is all about exploiting data from diverse sources—not only equipment sensors, but also logs, manuals, employee biometric monitors or the environment. The approach incorporates these types of sources and data into the analytical process to create a knowledgeable system that is continuously learning. It is able to make insightful operational recommendations based on a comprehensive understanding of manufacturing conditions.
Four Compelling Applications for Cognitive Manufacturing
- Asset Performance Management – Improving reliability and performance of equipment and assets through better visibility, predictability and operations
- Process and Quality Improvement – Optimizing yield and productivity of manufacturing operations, from design through warranty support
- Resource Optimization—Improving safety of workers and optimizing energy efficiency and facility productivity while reducing costs
- Supply Chain Optimization—Improving visibility and insights to build a dynamic supply chain that accelerates innovation
Cognitive manufacturing enables companies to put a laser-like focus on quality throughout the life cycle of a product’s development – from design through production and warranty support.
This article is an extract from a report published by IBM titled Cognitive Manufacturing. Download the full report.