Sometimes the most difficult thing about AI is simply knowing where to start. Identifying potentially impactful use cases is one of the most cited roadblocks for organizations seeking to leverage AI in their business.
Industry 4.0 has been a hot topic at maxon since the drive specialist introduced configurable drives. Now we're continuing to drive digitization - but not at all costs.
Marking of engineered assemblies, components and materials with identification numbers and other data is an essential element of modern manufacture. Until now, this has involved at least a small interruption in the production process for every unit marked.
The essential thing to remember is that humans will remain central to success, regardless of the level of technology deployment and there is not a silver bullet. So, what is the answer for best leveraging technology innovation?
According to a recent report from Boston Consulting Group (BCG), "Quality 4.0 Takes More Than Technology," nearly two-thirds of manufacturers believe that Quality 4.0 will significantly affect their operation within five years.
Industry 4.0, or the fourth industrial revolution, has called for a merger between automated solutions and smarter, more effective operations through the application of real-time data collection.
One of Industry 4.0's key drivers is data integration. By expanding the scope of data collection and making information readily retrievable, computers on the production floor have evolved to facilitate a higher level of collaboration and innovation.
One of the trends we've identified is the interest by diversified industrial companies to streamline businesses and realign around key markets or customer segments to further drive results. We're seeing an increase in mergers, acquisitions, and divestitures as these companies recalibrate their capabilities.
The IoT ecosystem connects heterogeneous processes, personnel, resources, devices, sensors, robotics, heavy machineries, and business procedures in a cohesive way to attain optimum resource usage and enhance the productivity of business processes.
Although 2011 is considered the unofficial start of the fourth industrial revolution, U.S. manufacturers have reported zero average growth in labor productivity over the past five years - despite continual improvements in equipment, software and management approaches.
Among the wealth of use cases for AI & ML in manufacturing, one rises above the rest in terms of feasibility and impact - predictive maintenance. Predictive maintenance addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems.
Modern manufacturers face key challenges to reaching their Industry 4.0 potential. MarkLogic knows data is your greatest asset to better serve customers, improve products, and navigate those complex environments.
"What makes the digital thread so important is that to leverage all the data, you need to also manage how the data connects to the rest of the operation and how it flows from one process to another."
With NIMS' emphasis on manufacturing skills training, credentialing, and standards, plus Festo's complete Industry 4.0 learning Factories, courseware and eLearning integration, the two organizations are well poised to provide the training programs for Industry 4.0.
Automation Alley's 2019 Technology in Industry Report looks at Industry 4.0 ecosystem, introduces Velocity Index™ to gauge investment-worthiness of key technologies
Industry 4.0: From Vision to Implementation, looks at the state of Industry 4.0 from its eight core technologies: the Industrial Internet of Things, robotics, artificial intelligence, Big Data, cloud computing, cybersecurity, additive manufacturing and advanced materials.
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RapidMiner offers a data science platform that's built to enable people of all skill levels across the enterprise to quickly build and operate AI solutions to drive hard ROI for their organizations. Many data science tools are built to create accurate models, but cannot help manage the models into production, where they can have an impact. The platform covers the full lifecycle of the AI production process from data exploration and data prep to model building, model deployment and model operations. RapidMiner can help with a wealth of manufacturing use cases, like designing smart products, running smart factories, forecasting demand, ensuring quality, reducing production downtime, and managing supply chain risk.