Smart Industry 2021

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<strong>Smart</strong> Business Predictive Manufacturing Manufacturing in the Cloud Embedded connected product monitoring enables data monitoring and analytics, administration, IoT device provisioning, and network operations through a single embedded board from a cloud platform. source ©: Tritos source ©: Aspen Technology Inc data into tangible business outcomes rapidly, he says. The concept of predictive manufacturing effectively extends an enterprise digital strategy. It should help reduce costs, increase quality and throughput, and prepare the organization to be more agile, says Naren Gopalkrishna, digital product manager at GE Digital. As the organizations mature in their predictive manufacturing journey, several other aspects of optimization are driven forward, such as predicted observations and prescriptive actionable insights. Organizations need to have a certain level of digital transformation maturity to successfully implement 38 IoT is making predictive manufacturing possible. Mats Samuelsson CTO at Triotos The Artificial Intelligence of Things transforms raw data into business outcomes. Bill Scudder General manager for AIoT solutions at AspenTech the predictive manufacturing concept. “The digital strategy should align with the larger manufacturing strategy and it should also consider the business problems that must be addressed,” says Gopalkrishna, adding that the IT and OT teams need to work together. Predicting the Downsides At Cognizant, Mehta’s view is that predictive manufacturing is a strong concept but implementation is often lacking in terms of providing sufficient volume, granularity, quality, and information accuracy. As an example, a temperature measurement at the output of a process can be effectively used to control source ©: Tritos quality in real time, avoiding quality issues by retrospectively analyzing the data. Lack of appropriate measurement (sensory) and/or intake frameworks would lead to the absence of this data, or the inability to use it even if it’s measured. Zebra’s Wheeler says predictive manufacturing may require investment in visibility infrastructure to provide real-time data plant-wide. “Justifying this investment may require some level of vision of the broad uses and value of leveraging this visibility,” he adds. Mats Samuelsson, CTO at Triotos, a company that builds overlay solutions on the Amazon Web Services (AWS) IoT cloud platform, sees the combination of better ways of collecting and processing data from new IoT technologies, plus improvements in machine learning, analytics, and AI, as a game changer. “They will certainly be combined with integration of existing and new control technologies for steady improvements in how manufacturing and production are planned and operated,” he says. “The question is which strategies enterprises will embrace to cost-effectively seize the opportunities, such as predictive manufacturing, that IoT is making possible,” he concludes.
Industrial Solutions Across Edge and Cloud The world is constantly changing, demanding continuous innovation from the cloud to the edge. The rapid growth of artificial intelligence is producing unprecedented amounts of data, leaving some factory systems with longer lifecycles struggling to keep up. You need Xilinx. Today’s <strong>Industry</strong> 4.0 applications demand a new approach to embedded computing. One that seamlessly powers intelligent assets in harsh environments and handles evolving complexity with ease. That’s our Adaptive Platform, and it’s redefining the way the world thinks about accelerated computing at the edge. Market leaders trust us to help them deploy products that anticipate and adapt to future market needs. Xilinx. Building the Adaptable, Intelligent World. www.xilinx.com/applications/industrial.html
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