There was a time when the average manufacturing facility was a kind of black box: you could measure inputs and outputs, and roughly gauge other metrics, but precision was a pipe dream. That's changed drastically in recent decades, with the advent of the IIoT
In order to continue to stay relevant and competitive, manufacturing must embrace technologies like Big Data, AI, AR, and more to help improve processes, increase productivity, and make informed, data-driven decisions. But are manufacturing companies doing so?
Context in batch manufacturing provides the "where" and "when" for a given recipe. Analytics can then be run within the context of a particular piece of equipment or unit and across all levels of a batch, providing the ability to perform batch-to-batch comparisons.
Data analytics is a powerful tool for manufacturers, which involves applying mathematical techniques to extract valuable insights from data. This can help to improve systems, understand trends, and increase efficiency across various industries.
Manufacturers are increasingly collecting data and it has become correspondingly more important to collect, save and distribute all stored data for further use. Access to this information lays the foundation for faster decisions, increased productivity and reduced costs.
When it comes to MedTech, data is the gamechanger. Whether it is patient outcomes or manufacturing excellence, data holds the key to a new era in manufacturing - and with it, the Cloud. It is the future of MedTech.
By going digital and adopting a data-first strategy, manufacturing leaders can transform their operations and better prepare themselves for the future.
With a platform-agnostic configuration solution, your data can float across systems and be used in any consuming application. And because you are not tied to any one enterprise system, you can create a single source of truth across the organization.
With synthetic data providing such a useful alternative to generating real-world data, it might not seem surprising that a study by Gartner estimates that by 2024, 60 per cent of all data used in AI developments will be synthetic.
Insights are more than data, it's about improving how your teams and production lines work together. Insights are crucial as they let cross-functional teams see the same data and understand the real-time state of the entire operation. Learn more in this Q&A.
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.
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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.