Artificial Intelligence
AI for Metals Manufacturing
AI isn’t one thing. It’s a shorthand for a dozen different technologies: machine learning, computer vision, natural language processing, and optimisation algorithms. Each solves a different class of problem.
For metals manufacturers, the most valuable applications are narrow and practical. AI that reads mill certificates in seconds and extracts every heat number, grade, and mechanical property without anyone typing a thing. AI that calculates optimal cut sequences for bar, rebar, and structural sections, cutting scrap by up to 50%. AI that monitors stock levels in real time and flags reorder points before you run short.
None of this requires a data science team or a rip-and-replace ERP project. The best metals AI sits on top of what you already have: your spreadsheets, your email inbox, your existing ERP. It adds intelligence where it counts. You pilot on one product family. You go live in a day. You scale when it works.
Posts here cover practical implementations for metals manufacturers: certificate automation, cutting optimisation, inventory intelligence, and what to expect when you bring AI into a shop that’s been running on Excel and tribal knowledge.
Start with the problem you want to solve. Let the maths do the rest.
Investing in tools scales staff productivity
- Steph Locke
- Archive , Blog
- Jul 23, 2021
- Updated
Businesses with the highest productivity experience more stable and consistent revenue growth, as well as being able to adapt more quickly. With less waste and more staff power focussed on growth, manufacturers with an investment in tooling are more operationally resilient.
Chatbots explained
- Steph Locke
- Archive , Learning
- Jul 21, 2021
- Updated
Chatbots are computer programmes that provide a (primarily) text-based interface to help people access information or perform tasks. Chatbot conversations can help you improve customer service whilst reducing demand on staff. They can process text using natural language processing and build a conversational workflow to support a intuitive interaction with people.
How to move your operations to the cloud - what you need to know
- Steph Locke
- Archive , Blog
- Jul 12, 2021
- Updated
You’ve probably heard of the cloud but what is it and how can it help your business? Learn how moving to the cloud can help you innovate and stay ahead of the curve when it comes to tech.
Utilizing AI with Steph Locke
- Will Harris
- News
- Jul 7, 2021
- Updated
Head over to Utilizing AI to listen to Steph on all things AI, it’s a very worthy listen.
Tools matter for Developer Velocity
- Steph Locke
- Archive , Blog
- Jul 7, 2021
- Updated
Businesses with the highest developer velocity experience the highest revenue growth, according to new research by Microsoft and McKinsey. Read on to understand how tools impact developer velocity and what the business case is for investing in them.
Developer Velocity
- Steph Locke
- Archive , Learning
- Jul 5, 2021
- Updated
Businesses with the highest developer velocity experience the highest revenue growth, according to new research by Microsoft and McKinsey. Read on to understand the factors impacting developer velocity and how you can start building a strategy to improve this vital area of technology in your business.
Selected for Procter & Gamble Supplier Academy 2021
- Ruth Kearney
- News
- Jun 28, 2021
- Updated
The Procter & Gamble Supplier Academy is a partnership between P&G and WEConnect International, providing training for majority women-owned businesses.
Funding for AI use cases and much more - AIPlan4EU
- Ruth Kearney
- Archive
- Jun 24, 2021
- Updated
At Nightingale HQ we recently submitted our AI use-cases to the AIPlan4EU to potentially develop them further. Find out how you can get yours funded and all about the new European AI on Demand Platform.
Manufacturers: Are you digitising or digitalising?
- Steph Locke
- Archive
- Jun 22, 2021
- Updated
To digitise your business means to perform traditionally analogue processes or actions digitally. Rather than a few changes here and there, digitalisation involves a change in how to the process is performed to better leverage technology.
MLOps is like process engineering for Data Science
- Solange Borrego
- Archive
- Jun 21, 2021
- Updated
The goal of MLOps is to streamline the development, deployment, and operation of machine learning models, by supporting their building, testing, releasing, monitoring, performance tracking, reusing, maintenance and governance, joining the efforts of Data Science and IT teams under a shared focus.
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