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Cloud Technology

Cloud Infrastructure for Metals Manufacturing

Your shop floor generates data every day. Inspection results, production runs, mill certificate scans, inventory movements. Most of it sits in spreadsheets, in email threads, or in a server room nobody updates.

Cloud platforms, primarily Microsoft Azure and Amazon Web Services (AWS), change what you can do with that data. They are where the processing happens when GoSmarter reads your mill certificates, where GoSmarter stores your data securely, and where your team can access live production reports from any device.

For metals manufacturers, cloud technology is not about replacing your ERP. It is about connecting the systems you already have. Your ERP does not talk to your quality system. Your quality system does not talk to your inventory tool. Cloud-based middleware and application programming interfaces (APIs) can close those gaps. No custom development project costing six months and a small fortune.

Posts in this section cover:

  • Microsoft Azure and AWS fundamentals for manufacturers
  • Cloud storage and security for mill certificates and production records
  • Connecting ERP systems to cloud-based AI tools
  • Cost management: so you are not paying for compute you are not using

Cloud infrastructure is not glamorous. But it is what makes everything else work.

How to score your first AI quick wins: Intelligent Insights

There’s no doubt that going ahead with Artificial Intelligence (AI) can be risky. We’ve seen numerous AI fails from major companies including IBM, Amazon and Microsoft which landed them in hot water, something big companies can often bounce back from, but could be more of a problem for the smaller players. The trick to getting started with AI is to start small, which is where our quick win AI projects come into play.

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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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How to score your first AI quick wins: Knowledge Worker Productivity

When you choose to introduce Artificial Intelligence (AI) in your organisation or department there can be a lot of resistance and uncertainty, which is why it is important to start small and win fast. By taking on smaller fail-proof projects, you can build up confidence among your team as they begin to see the value of the projects and stop fearing failure and resisting changes. In this project we discuss how to boost knowledge worker productivity, something employees will be able to track themselves and see the true value of. Building momentum in this way will pave the way for greater successes down the line.

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Removing AI bias for better decision making

It is difficult to deny that humans make biased decisions. Unconsciously we all make choices that are based on prejudices and flawed associations. This bias that we introduce to our business decisions can trickle through entire organisations, from recruitment to market segmentation. AI, with its lack of consciousness, human experience and gut feelings, has the potential to remove bias from businesses, and yet all too often AI is found to exhibit the same biases - link no longer works that we do.

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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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Garbage In, Garbage Out – The pitfalls of bad data

What is it? In advance of our upcoming Data Science Bootcamp, we are pleased to announce an open evening to explore the importance of data quality. Talent Garden faculty member, Steph Locke – data scientist and Microsoft AI MVP talks Garbage In, Garbage Out – The pitfalls of bad data.

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