Are we ready to embrace the potential of machine learning and create a more efficient and sustainable future, asks Dave Hughes, Director UKI, PTC.

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Many UK companies in the manufacturing industry, both large and small, are currently sitting on a goldmine of data. This data, if used correctly, can be turned into insights that can drive innovation and business value. With modern solutions such as AI and machine learning, companies can make real use of their data - something that is valuable in many ways, not least in terms of sustainability.

Internet of Things (IoT) data - data from sensors, devices and machines - is constantly gaining new insights in real-time. This allows companies not only to react to what is happening and has happened, but also to anticipate and proactively act on future potential events and factors. IoT data can be likened to being constantly prepared for all aspects that may have an impact on the business. The availability of real-time data allows you to act quickly, adapt to changes and even stay ahead of the competition.

Challenges in realising the potential of data

However, making full use of data does not come without challenges. Many companies struggle with data that is scattered across different systems, in different formats, and sometimes even of poor quality. In addition, internal IT infrastructure often needs to be modernized to handle today's rapid data traffic.

Overcoming these challenges requires a thoughtful strategy and effective implementation of modern technology. Here, AI - artificial intelligence - is a very useful tool. But AI can also be very complex, which is why I recommend starting with machine learning, which is a subset of AI. Put simply, an intelligent computer uses AI to think like a human and act on its own, while machine learning only performs the tasks for which the machines in question are trained.

Sustainability and profitability go hand in hand

In many ways, machine learning capabilities in design and manufacturing have changed the way companies look at sustainability initiatives. Sustainability initiatives have long been associated with more or less necessary costs driven by regulators. This has meant that many companies have postponed their investments in the area and chosen to wait and see. Today, we know that sustainability and profitability go hand in hand and the question is no longer whether it is worth adopting sustainable design and manufacturing practices, but how best to do so.

The goal should be to design and manufacture the best products and distribute them efficiently while optimising the consumption of materials, water, gas and electricity across all production sites. The data that underpins this is everywhere - in systems, meters and sensors. With advanced analytics based on machine learning, companies can identify bottlenecks where energy and materials are consumed inefficiently and act accordingly. There are many examples of how even small adjustments can lead to major cost savings and environmental benefits. While sustainability efforts may require new investments, the direct cost savings achieved by reducing waste and improving energy efficiency can be much greater.

Small steps instead of giant leaps

But how do you navigate through this complex terrain? The key is to take one step at a time. By starting small, but staying focused on an overall vision, companies can scale up their initiatives without compromising their safety and security. When the first successes become visible, it becomes easier to make the necessary investments for the next step.

Machine learning is a concrete solution for companies that want to turn their data into a competitive advantage. Instead of being paralysed by the uncertainty of AI, they can focus on the more concrete applications of machine learning.

With the right strategy, tools and vision, these companies can become one of the winners in the future business landscape.

Dave Hughes is Director UKI at PTC