Getting a robust, firing-on-all-cylinders data operation is a far less labor-intensive process than many seem to suspect, and the dividends it yields invariably outpace whatever effort is expended on getting it off the ground.
Data is the critical driver of change, but even the highest quality data can't drive change in a vacuum. Simply gathering and accessing data is not enough-manufacturers must leverage it in context to make decisions that enhance value in times of digital transformation.
Data Guys Versus Production Pros - Who is the Best Person to Train AI Programs for Defect Detection?
Artificial intelligence has the potential to deliver game-changing results for quality inspection and defect detection. However, until now the process of training the model has proven problematic.
Due to the fact that manufacturing and IT systems have historically developed in parallel, they cannot communicate by default, which forces companies to struggle with compatibility issues when performing digital transformation.
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.
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