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Demystifying artificial intelligence in manufacturing

Artificial intelligence is transforming the manufacturing sector by using data at scale to optimise operations and make lights-out factories a reality.

Demystifying Artificial Intelligence in manufacturing

While you may associate Artificial Intelligence (AI) with its depiction in Sci-fi movies (Terminator, iRobot), this is actually a highly advanced type of AI called Artificial General Intelligence (AGI), which we are still far from achieving. Instead, there are many forms of AI in our lives today which focus on solving specific problems, an area known as Narrow AI (NAI). For example, you may have heard of Google’s AlphaZero AI that beat the world’s best Chess-playing computer program after learning the game in under 4 hours. This is a very narrow application with a defined set of rules, rather than one that will “replace humans”.  

And there are many applications of AI in manufacturing already that could help improve efficiency, increase revenues, or mitigate risks. In this article, we aim to break down what Artificial Intelligence is, the need for it in manufacturing, and the applications that already exist.  

The need for artificial intelligence in manufacturing

Today’s manufacturers face many challenges, including the need to address labour shortages and navigate an increasingly complex regulatory landscape. Additionally, minimising waste remains a top priority. And smaller manufacturers must balance the need to protect their supply chain against finding new revenue opportunities.

At the same time, modern manufacturing systems and processes are generating vast amounts of data. And, with machine-to-machine (M2M) communications systems and the Internet of Things (IoT) driving digital transformation across the sector, humans can no longer comprehend the sheer size and complexity of these data sets.

Interpreting this data is where Artificial intelligence (AI) can help. AI is one of the defining characteristics of the fourth industrial revolution, or Industry 4.0. It can collect data from sensors, CNC machines, and other systems and operations. Then, it feeds this data through algorithms that transform it into actionable insights and automated workflows. Ultimately, AI aims to automate repeatable and routine operations, including everyday decision-making.

Moreover, we may need AI sooner than we think, with more than 90% of manufacturers already investing in digital solutions to boost efficiency in an ever more dynamic market. However, while AI is one of the newest and most promising technologies in the space, it has only seen widespread adoption in 9% of factories, according to a recent study by PwC.

What is artificial intelligence in manufacturing?

Artificial intelligence remains a poorly understood term. It is routinely confused with machine learning (ML), business intelligence (BI), and various other related areas. Actually, AI is a branch of computer science where programs mimic human capabilities at machine speed and scale to solve problems creatively.

For example, machine learning is a core subset of AI. Machines learn from past actions by analysing data for recurrent characteristics and trends. Meanwhile, Business intelligence provides predictive analytics to help business leaders make informed decisions based on past facts. However, AI goes a step further by enabling prescriptive analytics to answer “what would happen?” if a particular action were taken.

As mentioned initially, many forms and applications of AI already exist in the manufacturing space. Some of the most promising forms include predictive maintenance, demand forecasting, and process optimisation. These applications focus on solving specific problems and fall into the Narrow AI category. Meanwhile, Artificial General Intelligence, in which intelligent systems can handle a wide range of tasks in different domains, is still in its infancy. However, there is little doubt it will one day enable the automation of entire production lines.

Applications of AI for small manufacturers

AI is now well-established as a pillar of Industry 4.0. However, while innovation is predominantly considered for large-scale manufacturers, the implications for smaller operations are just as profound. Since one of the overarching goals of AI is to automate repeatable and routine operations, including everyday decision-making, it can help small manufacturers scale with demand and achieve far greater efficiency.

Here are some of the most promising applications of AI in manufacturing SMEs:

  • Predictive maintenance – unscheduled downtime at any point in a production line can result in widespread disruption. Therefore, manufacturers must take a proactive approach towards preventing it. Predictive maintenance systems use AI to establish a baseline for normal operations and take corrective action to address any discrepancies in real-time. For example, if a CNC machine is operating above normal temperature, pressure, or humidity parameters, the predictive maintenance system will step in to reduce the load or take other actions.
  • Business intelligence – manufacturers collect vast amounts of data from machines and software-based systems. The challenge lies in converting this data into actionable insights that decision-makers can understand and act upon. An AI-enabled BI solution goes even further by automating routine decision-making. For example, an AI-powered system can assist with demand and price forecasting and inventory management by forecasting the demand for raw materials based on past and existing throughput.
  • Research & development – manufacturing cycles are often longer than desired due to challenges in quality control, failure prediction, and prototyping. AI can assist with R&D by helping teams evaluate the viability of products in development. For example, digital twins are virtual representations of physical things. By applying AI models to these digital twins, R&D teams can carry out advanced tests without putting real-world assets and employees at risk.
  • Edge analytics and IoT – many manufacturing systems are highly sensitive to latency. This means that any delays can have significant impact. So, you need to process data at its source and take action in real-time to minimise issues. In many situations, these failsafe measures cannot rely on the relatively slow response times of cloud computing and human interaction. By automating decision-making at the source, AI is effective in reducing the time it takes to garner insights from data and take appropriate action. The rollout of 5G mobile networks will be vital in achieving this goal.
  • Process optimisation – as pressure grows to implement more sustainable production models, manufacturers have a duty to implement more efficient delivery systems. AI can proactively eliminate bottlenecks by analysing routine processes and making any necessary tweaks in real-time while providing a steady stream of actionable insights.

AI in manufacturing is a rapidly advancing field. And, while it may be some years before artificial general intelligence is broadly adopted, it is expected to make lights-out manufacturing the new normal. Already, manufacturers large and small, are tapping into the power of AI and related technologies. For example, French food manufacturer Danone Group uses ML to improve demand forecasting. Meanwhile, German automotive giant BMW uses image recognition to automate quality control checks.

How to get started with AI in manufacturing

There is no doubt that AI is critical to the future of mass production. With narrow AI applications now widely available, it is time for manufacturers to consider implementing AI on their shop floors.

The first stage is implementing the systems necessary to capture valuable data from all possible sources. After all, data is the fuel that makes AI both necessary and useful. The next stage is establishing your key performance indicators (KPIs) and implementing business intelligence to track these metrics and derive insight from the data collected. AI is simply the next stage in that evolutionary process, in which prescriptive analytics can help address some of the most pressing challenges and questions facing manufacturers today.

Fitfactory enables manufacturers to Streamline, Connect, Analyse, Level-up and Extend (SCALE) through digital transformation. We provide integrated technology products to capture data and analyse it in real-time to create your smart factory. Our business intelligence solution creates actionable insights and provides the foundation for adopting AI in manufacturing. Get in touch today to find out more.

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At Fitfactory, we aim to bridge the gap between industry and technology to make digitalisation achievable for all. This is the third part of our demystify series as we break down complex topics to demonstrate how they can add value to your business. Over the next few weeks, we’ll be covering Big Data, Digital Twins and more.

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