As an industrial manufacturing engineer writing and speaking about the supply chain for many years, as well as being in the field helping companies for most all of my career, I wanted to pass on some thoughts and information for those of you that are considering this type of deployment option - especially if your mission-critical business depends upon a working cloud.
The complexity of mineral processing and manufacturing plants - how Artificial Intelligence can help
Industry 4.0 encapsulates not only manufacturing but also mineral processing plants and the differences between these should be considered carefully in the deployment of 4.0 solutions such as Artificial Intelligence (AI).
Even when you can connect directly to whatever it is that you are measuring and design the system yourself, you might encounter issues. Inconsistencies can arise from power failures, wifi outages, faulty or damaged equipment.
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.
Commentary from Ramona Schindler, General Manager Machine Tool Systems, Siemens Switzerland. Most shops, even the smallest ones, have obviously been gathering data and logging it in a production-schedule format of some kind for many years.
"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."
Data connectivity and collection has evolved past the workarounds and silos that long-plagued industrial ecosystems. New technology is transforming data accessibility-without interrupting your vital processes or requiring new equipment.
Many companies are striving to create real business value from their various streams of information, a serious data-driven approach. Measurement, monitoring and access to real-time data are extremely important to reaping the benefits of Industry 4.0
By tapping into existing maintenance or equipment logs, a manufacturer can apply machine learning to predict which connected devices or machines will be in need of servicing or forecast required inventory levels across warehouse locations...
Big data and automation represent two very important tools that should help us create a brand-new type of manufacturing that doesn't require as much constant human attention and effort. It should, at the same time, vastly improve the safety and accuracy ...
In a recent PWC survey on Industry 4.0,which involved over 2,000 participants from nine major industrial sectors, data analytics emerged as one of the biggest challenges in the transformation from analog to digital operations on the production floor and beyond.
The consolidated view of the data dramatically simplifies comparisons across factories. With that it is much easier to identify inefficiencies (e.g. product not passing QA, volume of left over scrap) and differences in output.
In the current climate of industrial trucking products, truck manufacturing companies are striving to improve their products in more efficient ways than ever.
John Hitch for Industry Week: IBM's Watson IoT sees all and might even know it all. In a new simulation, you can feel what it likes to have it working for you.
What happens when a command from the controller arrives at the robot arm a tenth of a second late? Or what happens if sensor data never actually reaches the controller?
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