Leverage the Edge - the Benefits of Processing Data at the Edge, Not the Cloud

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 Current State of Supply Chain Data Quality

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.

How Does the New Generation Memory - DDR5 - Change Things?

With more and more data comes the need for storage and fast access which means that technology like DDR5 has never been more important.

Accelerating Digital Transformation to Support Post-Pandemic Growth

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.

5 Ways MDM Can Improve Manufacturing

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.

The Dynamic Duo of the IoT - Combining Cloud and Edge Processing Gives the Best of Both Worlds

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.

How Does IoT in Manufacturing Drive Employee Productivity

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.

Scoring Big on Insight From Effective Data Cleansing and Contextualization

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.

Five Steps to More Effective Operational Data Collection

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.

A Sustainability Process That Drives Business Value

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.

Data Integration is a Key Driver for Industry 4.0 in Polymer Manufacturing

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.

Could Predictive Analytics Help Tesla Drop the Cost of the Battery Pack to $100 per kWh?

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.

Transforming Manufacturing Takes Good Data Management

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.

How to Make Sense of Machine Data in Smart Factory 101

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?"

Data Requirements for Artificial Intelligence in Manufacturing

In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together.

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Opportunity Tonasket:  You Only Get to do this Life Once, So Let's Make it Count.

Opportunity Tonasket: You Only Get to do this Life Once, So Let's Make it Count.

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