Bringing real-time visibility to entire production, asset performance, inventory, workforce utilization, supply chain, etc. starts by collecting accurate shop floor data.
Data is gold. Yet, for many manufacturing professionals working at the Edge of advanced, multi-step production-its fundamental value remains buried deep.
With more and more data comes the need for storage and fast access which means that technology like DDR5 has never been more important.
Data comes at us fast and in many forms. These different forms can include structured, semi-structured, and unstructured data and many people do not realize that a data warehouse and a data lake handle the data differently.
Deploying RTUs on industrial assets is an important element of capturing, interpreting and using big data from an industrial process. In this article, Jean Burton, technical sales support manager at Ovarro looks at how RTUs play a key role in accessing big data.
By 2025, manufacturers and suppliers implementing Industry 4.0 tactics are expected to create $3.7 trillion in value, according to research by McKinsey and the World Economic Forum.
Manufacturing is forecast to come roaring back in the second half of 2021, according to many leading indices. This is fantastic news for the US economy but poses significant challenges for the manufacturing sector.
Products are becoming more and more complicated-and manufacturers are having a harder time keeping up. 92% of manufacturers have reported that their products have become more complex over the last half-decade.
When it comes to time-series data, there is no shortage of options. Cloud-based, data-lake, open source, and historians are all readily available solutions for data storage, along with lower prices for sensors, and wired or wireless offerings for data aggregation.
There are several different architectures and protocols that can be used to gather data on the factory floor. The two most common protocols are very different, which makes them worth comparing for their effectiveness in IIoT - OPC UA and MQTT/Sparkplug.
AI-guided selling enables analytics to move from descriptive to predictive and prescriptive analytics. The process uses customer and sales data for a scientific, data-driven approach to understand buyer processes and behaviors.
Data is the new Black Gold but if you are gathering data and not using it effectively then you are paying for data transmission and storage andiIt just becomes white noise and adds no value.
Blockchain has proven an effective salve for the dwindling trust in today's supply chains. It enables a network-wide protocol that helps reduce "trust tax," or the costs of maintaining trust.
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).
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Autonomously driving and self-contained logistics robots are a critical component of "Intralogistics 4.0". They are used for storage as well as removal and dispatch preparation, optimize material flow and relieve employees. Thanks to their performance and modular design, drive systems from FAULHABER meet the high demands of modern intralogistics.