A unique deep learning solution for the manufacturing industry can help US firms eradicate defective parts from their production operations.

Founded in Cape Town in 2014, DataProphet is a leader in artificial intelligence (AI) that enables manufacturers to step towards autonomous manufacturing. Their AI-as-a-service, DataProphet PRESCRIBE, proactively prescribes changes to plant control plans to continuously optimize production without the expert human analysis that is typically required. DataProphet has been recognised by numerous forums including the World Economic Forum as a Technology Pioneer in 2019, and CBInsights as 1 of the Top 100 AI Startups globally.

While every factory process is unique, a number of best practices will generally apply when implementing Artificial Intelligence (AI) for process control, according to DataProphet, a global leader in AI for manufacturing.

The consistency of a factory’s data is crucial to unlocking the long-term productivity benefits of Artificial Intelligence (AI), according to DataProphet, a global leader in AI for manufacturing. 

Within the industrial world, leaders are defining their journey to autonomous manufacturing. Manufacturers are focusing on digitization strategies that will help to drive efficiency across their plants. As data is increasingly aggregated across manufacturing lines, the reality of autonomous lines is likely to be reached in the next five years.

One of the biggest questions posed by manufacturers looking to transform their operations with artificial intelligence is: How do we know if we've got the data for this? It is a really good question. Machine learning is largely dependent on the ingestion of data - and not just any data. This article looks at the complexity that surrounds large industrial operations and the type of data manufacturing plants are probably already collecting. We also take a look at supporting the systems and infrastructure required. 

Machine Learning Solution Implementation Aims to Reduce Scrap and Improve Quality in Mature Data Environment 

Computer vision systems are installed in manufacturing plants to assist with a wide variety of industrial quality assurance and control processes, and to a lesser extent in applications of process control.

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn complex patterns from a sample of data. 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. When considering AI, it’s important to understand what the data requirements are at the outset. 

American manufacturers have been urged to unlock the value in their production data and learn from the historical knowledge embedded in their organizations.