Digital transformation and the adoption of cognitive manufacturing
An emerging group of manufacturers is integrating analytics, Industrial Internet of Things (IIoT) and cognitive computing with production to increase flexibility, speed and quality. Adopters of cognitive manufacturing are both moving aggressively to implement new technologies, and importantly, using a strategy to guide their efforts.
Many organizations are experiencing a cognitive transformation now. According to research from the IBM Institute of Business Value, between 2017 and 2020, electronics companies, as an example, see themselves transitioning from “establishing a technical foundation” to “enabling insight and optimization capabilities.”
Hannover Messe had everything – supplies, parts, tools, machinery, software, communications, financing, services, and all the professional experience and expertise an organization would need to create the ideal manufacturing environment to rapidly transform concept into product. While the main focus of the event was “things,” the digitization of manufacturing via IoT capabilities offered a new way to explore Hannover Messe. The inherent value of “things” is greatly increased via the data they generate.
From an IBM perspective, data, and the management and analysis thereof, is a primary means to realize this value. Watson is already changing the world. This year alone at least 1 billion people will be touched in some way by Watson. Some through cognitive robotics. Some through cognitive ball bearings. And some through cognitive elevators. Real-time analytics. Predictive analytics. A total transformation of the way products are operated in real-world environments.
Building new partnerships to help clients unlock the value
Last week, IBM announced the strategic collaboration with ABB which brings together capabilities to unlock new value for customers in utilities, industry, transport and infrastructure using artificial intelligence. The new suite of breakthrough solutions aims to help organizations tackle some of their biggest challenges – improving quality control, reducing downtime and increasing speed and yield of industrial processes – in a completely new way.
Reducing defects by bringing cognition to the factory floor
In a second announcement, IBM launched a new solution which brings intelligent eyes and cognition to the factory floor. The IBM Cognitive Visual Inspection solution uses an ultra-high definition (UHD) camera and cognitive capabilities from IBM Watson. The solution captures images of products as they move through production and assembly, and together with human inspectors, can detect defects in products, including scratches or pinhole-size punctures. Early testers of the new solution have already experienced a reduction in inspection time, and a 7 – 10% decrease in manufacturing defects.
Fusing innovative design and operational data with Digital Twin
But, before a product is produced in the factory, it may begin as a digital twin with both physical attributes and IoT functionality developed and tested using product lifecycle management and application lifecycle management solutions. Together, these digital thread capabilities help individuals or teams understand the impact of engineering decisions on a product’s performance, from concept through design and into production. For example, an engineering team might use a Digital Twin to visualize the performance of equipment in operation, and use this feedback to improve its design. For an operations team, a Digital Twin advice would allow technicians to see inside a device in order to identify potential problems. Maintenance could then be scheduled to correct issues before they become a problem.
Monitoring overall equipment effectiveness
Assets are increasingly connected to computer networks for remote administration purposes and monitoring. Driven by the increasing sophistication of operating assets that include hardware and software, operative equipment – either on a plant floor, a power plant or integrated into the infrastructure – is increasingly dependent on IT for their operation and maintenance. During production, monitoring overall equipment effectiveness (OEE) can help to predict impending production equipment degradation or failure, as well as provide diagnostics and recommendations to accelerate repair while simultaneously minimizing impact on production schedules. Analysis of various types or classes of manufacturing equipment can identify factors – positive and negative – that influence equipment performance and recommend optimized maintenance schedules to more efficiently allocate skills and resources with the goals of improving equipment reliability and availability.
Providing diagnostics and recommendations
IBM’s strengths in the area of predictive and prescriptive maintenance are complemented by significant investments in the Watson IoT Platform, years of research and development in data management and analytics, and application of these analytic and IoT solutions to meet a broader set to manufacturing needs such as quality analytics, cognitive visual inspection, overall equipment effectiveness (OEE), and warranty analytics.
Mitigating risk of downtime using asset health checks
Asset health and maintenance is critical to the smooth operation of shop floors. Keeping equipment well-maintained, while minimizing downtime can have a huge impact on profitability, production, and lean operations. Capabilities such as asset health insights and enterprise asset management enable operators to obtain advanced warning of equipment failure and automatically initiate work orders that describe the problem to help maintenance personnel effect timely repairs.
Improving shop floor traceability with cognitive analytics
The world is powered by an extraordinary array of equipment and machinery that produce the goods we use. Any shutdowns, failures and maintenance, planned or unplanned, will lead to billions of dollars in losses and affect upstream and downstream supply chains. It is here that CATS, together with Watson IoT Predictive and Cognitive Analytics, can transform equipment availability. Learn how to improve shop floor traceability with CATS.
Interacting with equipment with adaptive robotics and Watson IoT
In an Industry 4.0 setting, directly integrating edge devices – such as machines and KUKA robots – in the cloud using Watson IoT Platform enables manufacturers to develop personalized products and services, improve operations, reduce costs and avoid the risk of downtime.
Bringing knowledge to every employee, even at the edge
Of course, the knowledge and expertise of engineers, operators and technicians can be used to quickly onboard new staff, as well as improve the effectiveness of ongoing maintenance. The ability to capture structured and unstructured information, and turn it into valuable insight to be used by anyone, from anywhere is exactly what Woodside Energy achieved using IBM Watson’s cognitive capabilities. Watson helped the organization save 30 years of irreplaceable Woodside employee knowledge and $ 10M USD.
Driving transformation through human-centric design thinking
The next big thing in manufacturing will be all about digital transformation and the adoption of cognitive manufacturing.For organizations looking at business model changes, a human-centric approach can aid the development of new business models or processes within organizations, ecosystems and supply chains. Using IBM Design Thinking principles is one way to getting teams on the right path quickly.
How to benefit from the era of cognitive manufacturing?
Cognitive Manufacturing will digitize and optimize previously inaccessible areas of manufacturing processes and help manufacturers minimize downtime, optimize asset and equipment performance, improve quality and yield from design to support as well as reduce costs by driving efficiency in process, labor and energy.
Please join us on 12th May, 2017 to learn how you can utilize IoT and cognitive insight to optimize resources and achieve better visibility, predictability, and operations. Register to attend the webinar.
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