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Manufacturing

AI for Metals Manufacturing Operations

AI adoption in manufacturing is accelerating. But the use cases that actually move the needle are far more specific than most “Industry 4.0” content lets on.

For metals manufacturers: service centres, stockholders, fabricators, and structural steel processors. The problems worth solving are concrete. Cutting scrap on long products, bar, and structural sections. Managing stock across dozens of grades, dimensions, and forms without losing track of what’s in the yard. Keeping mill certificate records traceable and audit-ready. Hitting delivery commitments when the schedule changes at noon and three jobs need replanning before the shift ends.

AI solves these problems without ripping out your ERP or running a six-month implementation project. Tools like Cutting Plans, MillCert Reader, and Metals Manager work on top of what you already use: your existing ERP, CSV exports, spreadsheets, email order intake. They add intelligence where you’re currently burning hours or material. You can be live in a day from a CSV upload. You start with one product family or one process. You scale when you’ve seen it work.

Posts here cover practical AI use cases for metals operations: cutting optimisation, certificate automation, real-time inventory, and what an AI-assisted planning workflow actually looks like on the shop floor.

No buzzwords. No theory. Just what works for metal.

Mastering AI in manufacturing: the three levels of competency

Manufacturers have been facing continual pressure to improve their technology base, reduce costs, and improve quality since the Industrial Revolution. Manufacturers are used to change but not every manufacturer can or will embrace it at the same rate. Also, no manufacturer jumps straight to being an expert at the new thing they're needing to adopt. The same goes for Artificial Intelligence (AI) as an emerging change in manufacturing.

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Industry IoT, smart factories and AI in manufacturing

The world of manufacturing is on the brink of another revolution due to the Internet of Things (IoT) and Artificial Intelligence (AI) applications. Aside from clear use cases like robotics and automation, big data applications are coming into play, thanks to industrial time series data collected by data historians. Thriving on all this data, AI systems can be built to send early warnings, optimise processes, predict maintenance and enforce quality control. By collecting the right data, manufacturers can get really creative with their AI solutions, and it can set them apart from the competition.

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A partnership of Machine Learning and AI with healthcare professionals

Healthcare has always been a data-rich area, but with new technologies for processing and structuring, and new ways of collecting data, such as using sensors, like many other industries, the available data is growing exponentially. Artificial Intelligence (AI) makes it possible to analyse all this data in real-time by combing Machine Learning (ML) and Natural Language Processing (NLP), in order to gain valuable insights.

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Why do you need business intelligence?

How quickly could you answer the question, "How's your business doing?" if it was asked right now? How detailed would your answer be, and how confident would you be in your answer? If you were utilising business intelligence, your answer would be fast, comprehensive and accurate.

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7 tips for building a data culture that will strengthen your business

Data analytics has taken off but not everyone is on the same page. While some companies are already making waves with data science, others are still struggling with the basics. Curating a healthy data culture is ever more important now to prevent the gap from growing between those who are embracing analytics and those who are lagging behind.

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How to get AI to work for your business and enhance operations

Enterprise cognitive computing is the application of AI to enhance business operations. It has a wide range of applications including call handling, fraud detection and maintenance scheduling. ECC systems automate repetitive tasks and improve efficiency through fast search and information processing.

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Building a solid foundation in data science

Steph Locke on building a solid foundation in data science We spoke to Steph Locke about how much experience is needed to build a solid foundation in data science and how to future-proof your tech skills.

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