Inefficiencies. Faulty products. Costly maintenance. These are just some of the issues the manufacturing industry grapples with daily. Amid these challenges, AI technology emerges as a transformative solution for manufacturers.

Although AI and machine learning have been around for many years, primarily adopted by global giants like Apple, the recent buzz around AI has reignited conversations among manufacturers of all sizes.

According to a recent survey, 57% of manufacturing companies have begun experimenting with AI technology to identify how best it can be applied to their operational strategies. It’s still early days for most, so in this post, we look at 3 ways AI can help manufacturers be more efficient and productive.

1) Production operations improvement

Maximising efficiency is key in manufacturing. Any time wasted is money lost. AI can become crucial and add value by spotting and rectifying inefficiencies in the manufacturing process early and minimising wasted time within operations.

For an AI system to be able to do this, start by collecting relevant data within your manufacturing processes. This data collection should be timely, safe, and in a format that is easily digestible and readable.

Feed all this quality data into a centralised location for the systems to identify inefficiencies, bottlenecks and patterns in real time that would otherwise go unnoticed.

Manufacturing lines have thousands of endpoint sensors, equipment, and devices that generate tons of data in different formats. However, without a cohesive structure, this data loses its meaning. Imagine how many dashboards you’d have to look at to get a clear picture of the production process. That’s why AI is essential to quickly scan and filter essential data to analyse and make it meaningful.

For example, if there’s a four-stage process to develop a kettle, the system can analyse each stage down to the granular level of detail and flag if any process is taking longer than expected.

2) Predictive maintenance

Picture this: an AI system extracts data from hundreds of high-tech machinery, predicting potential failures or breakdowns. As components approach the end of life, the system automatically initiates a procurement process to replace the part before it’s broken down. This isn’t a fantasy tale; it’s a reality with today’s AI advancements.

AI is particularly helpful in predicting such failures so manufacturers can be prepared to upgrade or replace components in advance without affecting the production process and minimising downtime.

What’s more, it can go a step further by predicting the required downtime, allowing businesses to plan decisions and logistics around these anticipated breaks in production.

Previously, such predictive measures were often a tedious and manual process. But now, with IoT devices and AI systems, you can monitor the quality, efficiency and output of machines throughout the day. This proactive approach saves resources, cuts waste, and keeps customers happy.

3) Generative AI to maximise everyday productivity

Now, Generative AI tools alongside regular office applications have become accessible to everyone to help them be more productive and maximise daily work capabilities. For example, engineers can ingest huge volumes of data into an AI system when solving complex production issues and rapidly find information that significantly reduces resolution time.

Tools like Microsoft Copilot also extend the benefits of AI to those in manufacturing who understand everyday production challenges but may not be data experts.

For example, a plant worker might struggle to analyse information from a dashboard, but now, with Copilot, they can simply ‘talk’ to the system and get the information they want without figuring out complex charts. This empowers users across various roles, making advanced data-driven insights more accessible.

4 things to watch out for before an AI project

It’s one thing to understand the potential value of AI in manufacturing; it’s another to actually implement it. Here are 4 crucial considerations for manufacturers before investing in AI.

• Enablement

Without the support and training of people who aim to use AI, the adoption of the technology will fail. It’s important to take people on the journey with assurance, security and good information to help create a culture of success and adoption.

People will make or break the AI implementation in every scenario. The technology comes second, the people come first, and it is so important to ensure that people are listened to for understanding the problem and supported to adopt and succeed in the technology roadmap for AI.

• Good data

Do you have high-quality data to train the AI model? If your data is scattered in siloes or stored in a format unreadable for an AI system, it’s of no use. So, first and foremost, make sure your data is as clean as possible without duplicates, inaccuracies and errors. Centralise this in a cloud platform to enable long-term, scalable use of AI applications.

• What value are you trying to derive?

Clarify the business outcome or value you aim to derive from your AI investment. While this new tech is exciting, it may not fit everyone’s budget. Often, it is challenging to get the leadership on board and secure funding for a costly AI project so clearly articulate the cost to value. Understand how your desired outcome will streamline processes, give a competitive edge, and deliver tangible value.

• Minimise technical debt

New technology will inevitably introduce some technical debt. AI is still not mature, and the solutions you create still need fine-tuning as we understand more about it. While we cannot eliminate it altogether, we can minimise it. Ensure that AI systems have a clear business case and aren’t merely vanity projects adding to technical debt.

There’s also the issue of technical debt stemming from AI-generated code. Generative AI has opened the doors to citizen developers and even experts to create code faster than ever, but the output is still not perfect and will need human oversight. Balancing AI assistance with human expertise is the way forward to reduce technical debt while enhancing productivity.

Next steps

AI is fairly new, and getting your leadership team on board can be challenging. 40% of manufacturers cite coming up with a business case as a challenge for AI adoption. We can help here.

With our two routes, firstly a Copilot Readiness Assessment and secondly an AI & ML Assessment. We’ll check your readiness for AI, review ‘where you are’ and ‘where you want to be’ and present a proposal on how best to reach your AI goals, all backed by a business case. Sign up for an assessment to get started.

Remember, AI is here to stay; it will happen one way or another, and it’s important that people and processes are correctly improved to help handle this change in the months and years to come.