Artificial intelligence has skyrocketed in popularity in recent years. Due to the introduction of Chat-GPT, the average person has become more accustomed to the use of AI, sparking conversations as to how it can help in other areas of life - most importantly, business.
However, this is not new for most industries. AI has been commonplace in many industries for years, simplifying menial tasks, and using algorithms to assist in management; with AI fulfilling a wide variety of uses, such as quality control and demand forecasting, businesses can enhance their productivity and efficiency without employing more workers.
Manufacturing is one major industry to implement AI in their day-to-day running. This complete guide will cover all aspects of AI in manufacturing, including its background, uses, technology, benefits, challenges, and future trends.
What is AI in manufacturing?
The growth of AI in manufacturing has been exponential over the past decade, with advancements in machine learning algorithms, increased computing power, and vast amounts of data driving its expansion. AI technologies have permeated various sectors, including manufacturing, where they are revolutionising processes, enhancing efficiency, and driving innovation.
The global AI market has witnessed remarkable growth in recent years. Reports from various market research firms indicate that the global AI market size is expected to grow from $108 billion in 2022, to a projected $738 billion by 2030. Factors such as increased adoption across industries, advancements in AI algorithms, and the proliferation of big data are contributing to this growth.
AI's influence on the manufacturing industry has been profound. It is transforming various aspects of production, supply chain management, quality control, and predictive maintenance.
How is AI used in manufacturing?
AI has a wide variety of uses in the manufacturing industry and its implementation seems near limitless. From problem solving to identifying defects, here are some of the most prominent roles that AI plays in the industry:
Predictive maintenance
AI plays a crucial role in predictive maintenance by leveraging machine learning algorithms to analyse large volumes of data collected from sensors and equipment. These algorithms can detect patterns and anomalies in the data, enabling the prediction of potential equipment failures before they occur.
AI can analyse historical maintenance records and sensor data, allowing the system to view past instances of equipment failure. By correlating them with sensor data such as temperature, vibration, and pressure, these models can identify early indicators of impending issues.
Furthermore, AI enables predictive maintenance by continuously monitoring real-time data streams from sensors installed on machinery. AI algorithms can then detect subtle changes in equipment behaviour that may indicate a potential problem.
Quality control
AI systems assist with quality control through the use of advanced algorithms and data analytics techniques. These systems can analyse images of products or components to detect defects with high accuracy.
Machine learning algorithms are trained on large datasets of both defect-free and defective images, allowing them to learn patterns and characteristics associated with different types of defects. These systems can then automatically identify and flag defects in real-time, minimising the need for manual inspection and reducing human error.
Demand forecasting
In order to stay ahead of the curve, every manufacturing business needs to implement some form of demand forecasting or risk being bested by the competition. AI drastically improves this by using advanced algorithms to analyse diverse data sources. These include:
- historical sales records;
- market trends;
- social media sentiment; and,
- economic indicators
Algorithms within these models will learn from past data to identify patterns and correlations, continually refining forecast accuracy.
AI also enables businesses to incorporate both structured and unstructured data into their forecasting models, providing deeper insights into consumer behaviour and market dynamics. On top of this, AI facilitates demand sensing by analysing real-time data streams to detect changes in demand patterns, allowing companies to adapt quickly to market shifts.
Supply chain optimisation
Most manufacturing businesses see the benefits of implementing AI to optimise supply chains. These systems allow companies to automate manual and menial tasks, saving time for workers in the long run. This includes the likes of:
- warehouse logistics
- inventory management
- supplier relationship management
With full AI integration, businesses can expect to decrease operating and labour costs, increase productivity, and improve their overall efficiency.
Autonomous robots
Autonomous robots have continuously developed and improved over the years, with AI being instrumental in their evolution. AI aids in robot's perception, allowing them to sense and interpret their environment through sensors such as cameras or radar. AI algorithms process this sensory data to recognise objects, people, and obstacles, enabling robots to navigate safely and interact effectively with their surroundings.
This also enables autonomous robots to analyse sensory inputs and choose appropriate actions based on predefined goals and constraints. Machine learning algorithms give robots the ability to learn from experience and adapt to changing environments, which is imperative in the manufacturing industry.
Furthermore, AI enables collaboration among autonomous robots through techniques such as swarm intelligence and distributed coordination. AI algorithms coordinate the actions of multiple robots to achieve common objectives, such as collaborative assembly or search and rescue missions.
Energy management
A common misconception is that the employment of more AI tools will increase the energy output of a business. In actual fact, with the correct optimisation and integration, AI can instead lower the overall output due to its management of other systems.
These algorithms can analyse real-time data from sensors and production equipment to identify energy-intensive processes and inefficiencies. Through detecting patterns and correlations in energy usage, AI models can suggest adjustments to production schedules, equipment settings, and energy sources to minimise consumption while maintaining productivity.
Furthermore, through predictive maintenance, AI enables proactive identification and resolution of energy-related issues like an equipment malfunction. This saves businesses significant costs, reduces their environmental impact, and enhances the overall efficiency of the operation.
Technologies driving AI in manufacturing
AI is not just one universal piece of technology. Instead, it encompasses a wide range of processes that aid the many areas that businesses wish to make more efficient. Here are some of the most important AI technologies that are improving the manufacturing industry.
Machine learning
Machine learning is a subset of artificial intelligence focused on creating algorithms and models that enable computers to learn from data and make predictions or decisions without clear programming. It involves training algorithms on large datasets to recognize patterns and relationships, allowing them to improve their performance over time through experience.
Machine learning techniques encompass a range of approaches, including supervised learning, unsupervised learning, and reinforcement learning, each tailored to different types of tasks and data. Applications of AI machine learning are vast and diverse, spanning tasks like image recognition, natural language processing, and predictive analytics.
Computer vision
Computer vision enables computers to interpret and understand visual information from images or videos. AI helps to develop algorithms and models that can analyse and extract meaningful insights from visual data, like identifying objects, detecting patterns, and recognising faces.
These techniques encompass a wide range of tasks, including image classification, object detection, image segmentation, and facial recognition. Applications of this in manufacturing are diverse and include autonomous vehicles, medical imaging, surveillance systems, and quality control.
Natural language processing
Understanding human language is incredibly important for AI processes and natural language processing (NLP) enables it to do so. NLP gives computers the ability to understand, interpret, and generate human language. This involves developing algorithms and models capable of analysing and extracting meaning from textual data, including (but not limited to):
- documents;
- emails;
- spreadsheets;
- social media posts; and,
- spoken language
In the manufacturing industry, NLP can be applied to tasks such as analysing maintenance logs, equipment manuals, and technical documents to extract insights and identify patterns. For example, NLP algorithms can automatically parse and categorise maintenance reports to identify common issues or trends, helping manufacturers optimise maintenance schedules and improve equipment reliability.
Internet of Things (IoT)
The Internet of Things (IoT) is a network of interconnected devices embedded with sensors, software, and connectivity features that enable them to collect and exchange data. These devices can communicate with each other and with other systems over the internet, enabling remote monitoring, control, and automation of various processes and environments.
AI algorithms can analyse data collected from IoT sensors and devices, enabling intelligent decision-making and automation of processes. In the manufacturing industry, IoT can be applied to monitor equipment performance, optimise production workflows, and enable predictive maintenance. For example, by deploying IoT sensors on machinery and using AI algorithms to analyse sensor data, manufacturers can detect anomalies, predict equipment failures, and proactively schedule maintenance to prevent downtime.
AI and IoT solutions enable manufacturers to improve operational efficiency, reduce costs, and enhance overall productivity by leveraging real-time insights from interconnected devices and systems.
Real world examples of AI usage
1. General Electric
General Electric uses AI in their analytics software to rapidly identify problems, discover root causes, and mitigate risks. These tools use predictive maintenance techniques for monitoring assets, reducing downtime and increasing reliability of these systems.
Through using AI and predictive maintenance, this allows software to save users time and money by identifying potential shutdowns or bottlenecks before occurring.
2. Amazon
Amazon offers an automated supply chain solution that leverages machine learning to help sellers optimise their shipping and delivery. AI systems help to optimise delivery routes and warehouse layouts, calculating the optimal placement and route for items in fulfilment centres.
For demand forecasting, AI helps Amazon by analysing historical sales data, seasonality, and market trends, painting an accurate picture of what to expect. This enables fulfilment warehouses to optimise their supply levels and plan accordingly.
Moreover, Amazon employs autonomous robots powered by AI in their warehouses. This assists in warehouse automation and optimising logistics, as it gives menial and time-consuming tasks to robots instead, including picking, packing, and sorting.
3. BMW GROUP
BMW Group uses AI to perform repetitive and long tasks, removing the need for workers and increasing productivity. This includes using AI for quality control, layout planning, and warehouse logistics.
One of the most important implementations of AI at BMW has been automated image recognition. This allows manufacturing plants to quickly determine if productions are in line with the company norm, comparing model designations with thousands of others in an online database. This is also incredibly useful for detecting defects and correcting them before a later stage in development, thus saving time and money.
What are the benefits of AI in manufacturing?
AI is making sweeping statements throughout all industries, and manufacturing is no different. Here are some of the most important advantages AI provides businesses with:
- Improved operational efficiency - AI enhances operational efficiency in manufacturing by automating repetitive tasks, optimising production processes, and reducing cycle times. Machine learning algorithms analyse vast amounts of data to identify inefficiencies and bottlenecks, enabling manufacturers to streamline workflows and maximise resource usage.
- Enhanced product quality - AI improves product quality by enabling real-time monitoring and control of production processes. Computer vision systems equipped with AI algorithms can detect defects and anomalies with high accuracy, ensuring that only high-quality products reach customers.
- Cost reduction - Manufacturers reduce their costs by optimising AI's ability to allocate resources, minimise waste, and enhance operational efficiency. Predictive maintenance powered by AI prevents costly equipment breakdowns and reduces maintenance expenses by scheduling maintenance activities proactively.
- Increased flexibility and adaptability - By enabling agile production processes and rapid response to changing market demands, AI enhances manufacturing flexibility and adaptability. Production planning and scheduling systems can dynamically adjust production schedules based on real-time demand signals, enabling manufacturers to respond quickly to fluctuations in market conditions.
- Empowered workforce - AI empowers manufacturing workers by alleviating them of menial tasks and enabling them to focus on more complex issues that require human creativity and problem-solving skills. Collaborative robots can work alongside human workers, assisting them in repetitive or physically demanding tasks and improving overall productivity.
- Facilitated decision making - Data-driven decision making in manufacturing provides businesses with actionable insights and predictive analytics. Advanced analytics platforms powered by AI analyse vast amounts of data from across the manufacturing ecosystem, enabling stakeholders to make informed decisions in real-time. Whether it's optimising production schedules, predicting equipment failures, or identifying market trends, AI-driven decision support systems empower manufacturers to make strategic decisions that drive business success and competitive advantage.
What are the challenges of AI in manufacturing?
AI is not a one-size fits all solution. Instead, the correct technology and methodology needs to fit the scenario at hand, otherwise it may instead jeopardise the task and become less efficient.
Here are some of the challenges that come with implementing AI in the manufacturing industry:
- Data quality and availability - Ensuring the availability and quality of data is essential for successful AI implementation in manufacturing. Challenges may arise due to fragmented, inconsistent, or incomplete data from various sources. Manufacturers need to invest in data collection infrastructure, data cleansing, and integration processes to ensure that AI systems receive high-quality data for analysis.
- Integration - Integrating AI solutions with existing manufacturing systems and processes can be complex and costly. Legacy equipment and systems may not be compatible with AI technologies, requiring retrofitting or upgrading. Interoperability issues between different systems and platforms can further complicate the integration process, necessitating careful planning and coordination.
- Security and privacy - As manufacturing systems become increasingly interconnected and digitised, cybersecurity threats become more prevalent. AI-powered manufacturing systems may be vulnerable to cyberattacks, data breaches, and unauthorised access, posing risks to intellectual property and sensitive information. Robust cybersecurity measures and compliance with data privacy regulations are essential to safeguard AI-driven systems.
- Overreliance - Overreliance on AI systems without human oversight or intervention can lead to unintended consequences and errors. Manufacturers need to strike a balance between automation and human supervision to ensure that AI systems operate safely and effectively. Human judgement and expertise are essential for interpreting AI-generated insights and making informed decisions in complex and dynamic manufacturing environments.
- Regulatory and compliance - AI applications in manufacturing raise ethical, legal, and regulatory considerations related to accountability, transparency, and bias. Compliance with regulatory frameworks governing AI use, such as safety standards and product liability laws, is essential to mitigate legal and reputational risks. Manufacturers need to adopt ethical AI principles and guidelines to govern the development and deployment of AI technologies responsibly. Additionally, ensuring compliance with data privacy regulations, such as GDPR and The Data Protection Act, is crucial for protecting customer and employee data privacy rights.
Future trends
Alongside current implementations of AI, there are also many new things in development or continuously updating. Here are just three examples of future AI uses and what they mean for manufacturing:
Edge computing
Edge computing for AI involves performing AI-related computations and tasks closer to the data source or device, rather than relying solely on centralised cloud servers.
By processing data locally at the edge of the network, edge computing reduces latency, conserves bandwidth, and enables real-time decision-making in applications such as IoT, autonomous vehicles, and industrial automation.
AI driven design
AI-driven design involves using artificial intelligence algorithms to automate and enhance various aspects of the design process, such as generating concepts and creating similar alternatives. We're already seeing examples of AI design throughout various industries, through programs such as Adobe Photoshop.
Through leveraging AI, designers can explore a wider range of design possibilities, accelerate iteration cycles, and create more innovative and efficient solutions across various domains, including product design, architecture, and engineering.
Digital twins
AI digital twins are virtual representations of physical assets or systems that use artificial intelligence to simulate, predict, and optimise their behaviour in real-time. This is incredibly helpful in situations where creating a product may be too costly or take too much time.
Digital twins enable businesses to gain deeper insights into the performance, maintenance needs, and operational efficiency of assets, allowing for proactive decision-making and improved outcomes.
Conclusion: is AI the future of manufacturing?
In conclusion, AI undoubtedly holds immense promise as the future of manufacturing, already showing its ability to revolutionise the industry in unprecedented ways. With its ability to enhance operational efficiency, improve product quality, reduce costs, and empower the workforce, AI is poised to drive significant advancements across all facets of manufacturing operations.
As manufacturers continue to embrace AI technologies and integrate them into their processes, the industry will witness transformative changes that will shape the future of manufacturing for years to come. This will allow manufacturers to stay ahead of the curve, adapt to evolving market demands, and unlock new opportunities for innovation and growth in the dynamic landscape of modern manufacturing.
Who knows what AI will look like in 5, 10, or even 50 years. It is exciting to look forward and wonder how the world will change as technology continues to evolve at such a fast pace.
AI in manufacturing FAQs
What is an example of generative AI in manufacturing?
Product design benefits from generative AI as it can help rapidly produce vast quantities of a specific design alongside similar alternatives. This cuts out the need for a designer to physically design the other alternatives, saving both time and money.
What industry uses AI the most?
Many industries use AI for efficiency and productivity. The most popular industries include healthcare, education, marketing, retail, and recruitment, bringing convenience to workers and businesses at a variety of levels.
Which company uses AI the most?
Whilst it's hard to determine exactly which company uses AI the most, Amazon is definitely a top contender. They use AI for supply chain optimisation, forecasting demands, and optimising delivery routes. Amazon also uses autonomous robots throughout their logistic warehouses to alleviate their workers of time-consuming and repetitive tasks.
How is AI environmentally friendly?
AI usage in specific roles, when compared to human counterparts, can achieve similar or higher efficiency with less energy consumption. Whilst one business using AI will hardly make a difference, the impact that a worldwide shift can make will definitely show.
How can AI influence the competitiveness of manufacturing companies?
Adaptable and intelligent robots offer manufacturers a dynamic edge, allowing businesses to adapt and perform new tasks. By giving businesses flexibility to quickly restructure based on new market needs and demands, using AI gives them a competitive advantage.
How is AI being used in food production?
AI is revolutionising food production by optimising processes, enhancing quality control, and improving sustainability. In agriculture, AI-powered systems analyse data from sensors and drones to optimise crop yield and monitor plant health, leading to increased productivity and reduced environmental impact. In food processing and packaging, AI algorithms are used to automate quality inspection, detect contaminants, and ensure compliance with safety standards, ensuring the delivery of safe and high-quality food products to consumers.
