One of the biggest challenges that enterprises face in their digitalization efforts is having too many complex data silos and applications that don't follow a common architecture.
The edge is an essential layer of the manufacturing technology stack. Machines on the factory floor collect vast amounts of raw data from various sources using numerous protocols, which all needs to be processed quickly to gain actionable insight.
The implementation of strategic data quality capabilities can make or break a business. Many businesses suffer the consequences of risks and excess costs without ever understanding the root cause to be poor data quality or integration.
With more and more data comes the need for storage and fast access which means that technology like DDR5 has never been more important.
We have reached a tipping point to reengineer our end-to-end supply chains. Resilience across the entire value chain is critical. You must have the systems in place and ensure there is no over-dependence on any one partner, country, or region.
The often-discussed goal is to have a "single version of the truth" across the organization. MDM is not unique to the manufacturing sector, but it's rapidly gaining ground in that industry.
A challenge lies in that fact that every single smart device connected to the IoT generates huge amounts of data. All of this information must be processed and analyzed to successfully take advantage of the opportunities presented by Industry 4.0.
More devices mean more data and more information. But there is a catch! Data in itself is not helpful until used in the right context. In order to gain that context, you must ask the right questions from the data.
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.
In the increasingly high-tech, high-touch realm of manufacturing, actionable data is an ever-growing factor in business and technical decision making. It's tempting to think that data collection can be a fully automated process, especially in the age of IoT and AI.
Iota has developed a framework called the Sustainability Process Blueprint that allows businesses to easily monitor, measure, and curate data pertaining to their sustainability performance.
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.
The battery pack cost of $100/kWh is a primary target for Tesla. To achieve this, the company must solve one of the world's most demanding technology challenges - and it is how to increase the volumetric energy density of battery cells while slashing production costs.
By mirroring physical assets as digital systems, manufacturers create products better and cheaper than ever before and, by bridging the physical and digital worlds, solve physical issues faster.
The idea of machine to machine communication has raised the initial excitement and high hopes but now owners of mid-size and small factories ask: "What's next? How do I start adopting these technologies in my factory?"
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