AI has seeped into every corner of modern life, from the apps we scroll through to the tunes we groove to and the art we admire. It’s everywhere, with everyone wanting to jump on the AI train and add AI features to their products. The appeal? AI’s knack for swiftly handling tasks that would usually eat up our time and energy. Whether it’s curating personalised recommendations, composing music, or crafting art, AI can pull off feats once thought to be the realm of human creativity alone.

However, looking beyond the hype, AI carries significant implications for manufacturing and industrial sectors. In these domains, AI isn’t just a trendy term – it’s a driver of substantial change. Armed with the ability to sift through massive datasets and reveal valuable insights, AI can improve processes and inefficiencies that have persisted for years. From predictive maintenance and robotics to quality control and supply chain management, artificial intelligence technologies offer a wealth of solutions to modern-day problems.

Defining AI

Artificial intelligence (AI) is the simulation of human intelligence by machines or computer systems. AI mimics a range of intelligence processes, including:

  • Learning – AI learns by gathering information from various sources such as datasets and interactions, extracting patterns, recognising trends, and deriving insights to make informed decisions, much like how humans learn through education and experience.
  • Reasoning – AI applies rules, logic, and inference mechanisms to process data and arrive at decisions or solutions, mirroring human cognition. It deduces relationships, analyses cause-and-effect, and formulates hypotheses to handle problem-solving tasks.
  • Self-Correction – AI uses self-correction mechanisms like feedback loops and continuous learning algorithms to refine models, optimise predictions, and adapt, similar to how humans learn from their mistakes and experiences.

As a discipline, AI covers a wide range of techniques, from basic rule-based systems to advanced machine learning algorithms and deep learning neural networks. Rule-based systems follow predefined logic, while machine learning algorithms enable systems to learn from data without explicit programming. Inspired by the human brain, deep learning neural networks are adept at handling large volumes of unstructured data.

AI or Not?

Because AI is such a buzzword right now, many technologies are being referred to as AI without being AI. In most cases, AI will involve machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, or robotics.

Some technologies that are frequently mistaken for AI include:

  • Automation – Involves the use of technology to perform tasks with minimal human intervention. While AI can power automation systems, not all automation is AI-driven. For example, robotic process automation (RPA) automates repetitive tasks using predefined rules and logic.
  • Expert Systems – Expert systems are computer programs designed to mimic the decision-making abilities of a human expert in a specific domain. They use rules and logic to provide recommendations or solutions based on input data, but they don’t learn or adapt like AI systems.
  • Data Analytics – Data analytics involves analysing data to extract insights and inform decision-making. While AI techniques like machine learning can be used for advanced analytics, traditional statistical methods and algorithms are also employed in data analytics.
  • Predictive Analytics – Predictive analytics uses historical data and statistical algorithms to forecast future trends or outcomes. While machine learning models can be used for predictive analytics, not all predictive analytics solutions leverage AI.

While these technologies are often intertwined with AI, they don’t quite fit the bill by themselves.

Industrial and Manufacturing Applications of AI

In industrial settings, artificial intelligence is proving to be particularly transformative – enhancing efficiency, reducing operational costs, and improving product quality. Here are some examples of AI at work in the manufacturing and industrial sectors:

Predictive Maintenance

Predictive maintenance (PdM) is gaining traction in various industries as a key strategy to enhance equipment efficiency and extend lifespan. By significantly reducing downtime and maintenance costs, this approach proves to be an invaluable asset for operational continuity. The integration of AI into predictive maintenance is the next natural step to increasing the benefit of such proactive programs.

AI models can analyse data from vibration and temperature IoT sensors installed on key bearings, rollers, and motors. By monitoring vibration patterns and temperature changes, AI can preemptively alert maintenance teams about potential misalignment or imminent bearing failures. Advanced data analytics platforms like SAP Predictive Maintenance and Service and IBM Maximo sift through this data to identify patterns and predict potential breakdowns.

COREMATIC developed an intelligent yield mapping system to detect and classify sweet potatoes during harvest. The system utilises AI and advanced data collection to register GPS tags every few meters. Despite the challenging agricultural environment, the system was designed to recognise a wide range of variants. After extensive refinement, this technology is now used daily by Greensill for predictive maintenance and yield optimisation.

Quality Control

In the realm of quality control, AI technologies have redefined manufacturing precision and consistency. AI-powered vision systems are employed to inspect products to identify and classify defects through high-resolution image capture or video feeds. These machine-learning models, trained on thousands of images, can detect manufacturing anomalies faster and more accurately than human inspectors. When defects fit pre-defined criteria, the product is flagged for removal or secondary inspection. This guarantees that only the best products reach the market.
COREMATIC has developed an advanced bath detection system for the aluminium smelter industry that efficiently identifies and removes unwanted material. This innovative system utilises computer vision technology and time-of-flight infrared cameras.

Supply Chain Optimisation

The supply chain is a complex web of moving parts, from sourcing raw materials to managing inventory and delivering finished products to customers; inefficiencies can arise at any point in the process. AI algorithms can intake data on these operations, analyse them for optimisation, and provide real actionable insights. According to McKinsey, early adopters of this AI for supply chain optimisation improved their logistics costs by 15%.

Here are some examples of the types of supply chain data that AI can work with:

  • Historical sales data, market trends, and customer behaviour patterns.
  • Stock levels, reorder points, lead times, and inventory turnover rates.
  • Supplier performance metrics, delivery times, quality control data, and pricing information.
  • Transportation routes, carrier performance, delivery schedules, and transit times.
  • Production schedules, equipment utilisation rates, and manufacturing lead times.
  • Costs associated with procurement, inventory holding, transportation, and warehousing.

For instance, if a supplier frequently misses delivery deadlines, AI can quickly pinpoint this as a supply chain inefficiency and potential bottleneck. Armed with this knowledge, the manufacturer can then take steps to find a more reliable supplier with consistent delivery schedules.

Bosch is leveraging AI to better its global supply chain, from procurement to distribution. By analysing data from various sources, including weather forecasts, traffic patterns, and supplier performance, Bosch is looking to anticipate disruptions, mitigate risks, and ensure the smooth flow of materials and components.

Robotics and Automation

In many factories, robots equipped with AI are used to automate repetitive tasks such as assembly, packing, and even complex functions like welding and material handling. These robots can adapt to new tasks through machine learning, becoming more efficient over time.

Collaborative robots, or cobots, are designed to work alongside human operators in manufacturing environments. These robots are equipped with advanced sensors and safety features that allow them to detect and respond to humans safely and seamlessly.

Amazon was one of the first companies to take the leap – believing that robots could transform their operations. Today, Amazon employs a variety of robots, including collaborative robots (cobots) and autonomous mobile robots, in conjunction with advanced automation technologies. These robots, equipped with state-of-the-art AI features like computer vision and machine learning, collaborate with employees in fulfilment centres to streamline tasks such as package sorting, inventory management, and workflow optimisation.

The Buzz Continues

AI’s rapid integration into various industries and its remarkable efficiency gains and cost savings have propelled it into buzzword territory. The technology’s ability to learn and improve over time without human intervention makes it an attractive proposition for any business looking to innovate and stay competitive. AI delivers tangible benefits in high-stakes environments like manufacturing—where precision, efficiency, and uptime are key, further fueling the hype.

While “AI” may be used more as a marketing ploy than a precise technical term in certain contexts, its real-world applications, particularly in industrial and manufacturing settings, truly justify the attention it receives. The excitement surrounding AI shows no signs of slowing down.