Manufacturing and AI: the path to efficient and safe operations

Martin Kahl
6 November, 23

Today’s manufacturing landscape is sophisticated, intricate, and fiercely competitive. It involves countless processes, technologies, and skilled professionals collaborating, sometimes even across borders. With unplanned downtime costing the industry $50 billion annually, manufacturers continually race to adopt the latest tech to enhance efficiency and remain at the forefront of progress.

The manufacturing sector is no stranger to such rapid innovation. Having experienced the Fourth Industrial Revolution (4IR) first-hand, it’s no surprise that state-of-the-art technologies, from assembly line robots to automated supply chain and warehouse management systems, are already in use to help boost operational activity.

So, how can artificial intelligence (AI) help the manufacturing industry create more efficient and safe operations and, as a result, boost their business results?

Automation and Digital Twins

Industrial Internet of Things (IIoT) applications have proliferated across various industries, from automotive and energy to aerospace. The data collected from equipment, sensors, cameras, and other devices creates enormous benefits in streamlining manufacturing processes and workflows.

One of the best strategies for using this data is to deploy it through a Digital Twin, a software model that reflects an accurate virtual replica of a physical asset in real-time. This model considers everything from individual machine performance metrics and energy outputs to weather conditions and object interactions.

Employing a digital twin on specific production lines or entire factories gives organisations a comprehensive picture of what’s happening, enabling detailed digital experimentation. It offers greater control over the process and allows predicting the effect of configuration changes and process modifications before committing resources. In fact, Saudi Arabia’s latest The Line project is not built directly but is designed through a digital twin backbone.

One of the significant benefits of employing digital twins is that manufacturers can utilise this technology without overhauling their existing solutions—leading to quicker value realisation at a reduced cost. By integrating a digital twin alongside the current Manufacturing Operations Management (MOM) infrastructure, manufacturers can create more value from years of technological investments without needing to “rip and replace” existing systems.

When it comes to automation, AI and robotics are closely linked. AI-driven platforms can learn to handle traditionally manual tasks that once required human intervention, from cable installation to assembly and parts picking. Indeed, AI has the ability to automate 40% of the average workday, marking a huge leap towards fully digital smart factories, enhancing performance, reducing production costs, and boosting profit margins.

Enhancing Health & Safety

Health and safety have always been a top priority in manufacturing, with the industry considered the second highest-risk environment. Even with adherence to all safety regulations, factory employees working with hazardous machinery remain at constant risk.

Adopting AI and Machine Learning applications can help prevent accidents by analysing data from cameras and IoT devices, including monitoring workers’ locations and proper use of personal protective equipment. AI can instantly flag a dangerous situation, triggering an alarm to notify employees of danger and shutting off machinery. With appropriate training and fine-tuning, ML algorithms can also engage in predictive analytics and scenario modelling to identify the causes of past accidents and avert future ones.

AI empowers manufacturers to shift from a reactive to a proactive safety approach, addressing the root causes of problems. This leads to fewer injuries, smaller productivity loss due to mishaps, and quicker incident resolution.

Data Analytics

Operational productivity is another key area in which AI can make an impact. Predictive maintenance, which continuously monitors the condition and usage of a connected device or piece of equipment, can help foresee potential issues, minimising downtime by up to 50% and extending machinery lifespan by up to 40%. Timely maintenance offers a far more cost-efficient option than fixing breakdowns.

AI can also aid in analysing customer data, enabling senior leadership teams to respond quickly to market fluctuations. AI-driven product development can accelerate prototyping, saving time and resources on testing through advanced algorithms and simulations, assisting engineers in creating superior products.

AI’s potential isn’t limited to optimising production lines; it can similarly enhance customer experiences and buying journeys, considering factors like historical sales data, industry, and geographical region. Moreover, predictive analytics enable manufacturers to address prospective buyers effectively.

By understanding customer purchasing habits and product usage, manufacturers can build trust, streamline the buying process, and convert one-time buyers into recurring customers and brand advocates. Accurate demand forecasting improves sustainability and business growth in the long run.

Industrial cybersecurity and AI

While AI has many benefits for manufacturing, generative AI’s potential to create fake or potentially harmful data also poses a significant risk to industrial cybersecurity, jeopardising the reliability and availability of industrial systems. For instance, AI can be used to create fake sensor readings, process parameters, or control commands that can interfere with plant operations and cause safety incidents. 

In addition, as with any other industry, AI can also create phishing emails, reports, alerts or news articles, which can deceive recipients and lead to a data breach. This makes it crucial to increase awareness among industrial stakeholders about the use cases and limitations of generative AI and the possible risks and ethical considerations posed by misuse.

With that said, AI does have a vital role to play in shoring up network intrusion detection within factories and protecting them against damage should an attacker set their sights on infiltrating their network. Here, machine learning is applied to gather intelligence on a factory’s baseline data patterns over time, programmed to identify and call out any anomalies or deviances from what the technology understands to be typical for that network. These anomalies are then flagged to an IT administrator who can choose to investigate the suspicious activity, and take action should it seem a breach in the network may have occurred. 

AI’s role in digital transformation

Undoubtedly, AI carries a suite of worthwhile applications for the manufacturing industry. It gives manufacturers unprecedented control over every process, and, coupled with technical experts, it can unlock new avenues for digital transformation and modernisation. Amidst rising competition and growing demand for quality and safety, AI and ML applications offer a pathway to boosting revenue and sustainability without compromising quality and customer satisfaction.

In theory, the future may see factory automation powered by AI achieving unparalleled productivity levels, operating around the clock with minimal human intervention. There may not even be a need to keep factory lights on, with digitalisation and AI significantly improving energy efficiency.

However, to witness a tangible return on investment (ROI), it’s crucial to identify the specific issues and obstacles a business encounters and align the appropriate solutions accordingly. Simply adopting a new technology isn’t enough; it must be seamlessly woven into business practices, coupled with robust processes and internal capabilities to gauge and extract its full potential. If managed correctly, it can create an exemplary model of peak efficiency, driving quality, time, and cost savings — a perfect blend for a sustainable future business.

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