What advantages does AI bring to the manufacturing industry?
The explosion of artificial intelligence systems like Chat GPT and AI image generators have brought AI to the forefront of public attention. In light of this, it seems like AI has opened up a whole new realm of possibilities for consumers.
Yet, perhaps unbeknownst to the general public, artificial intelligence has been used and developed within the manufacturing sector for decades. And AI in manufacturing is still progressing at an astounding rate, enabling manufacturers to aid their workforces with advanced processes, gain keen insights that help them be proactive, and even predict future outcomes. In a report from Deloitte, 93% of surveyed manufacturing employees believe that AI will substantially drive growth for the industry.
With this in mind, let’s explore 10 AI in manufacturing examples and see the current exciting possibilities and benefits of artificial intelligence in the industry.
3 Key Technologies to Understand Manufacturing AI Systems
Before we look at our AI in manufacturing examples, it will be useful to give ourselves a brief run-through of artificial intelligence and how it works.
Artificial intelligence relies on 3 key technologies.
1. Machine Learning
Machine learning allows AI systems to learn from experience. This form of artificial intelligence uses heuristic algorithms that learn from underlying patterns with structured data sets. Once the machine has learned a sufficient amount of data, the machine and its AI can then implement actions and perform certain levels of decision-making without any external programming.
2. Deep Learning
Deep learning, a subset of machine learning, uses an artificial neural network to learn from large amounts of unstructured data. This is, at the moment, AI technology’s best attempt at simulating the human brain's neural pathways. This technology works by processing unstructured data through various computational layers until the final result or output is achieved. The more Deep Learning algorithms are given the opportunity to learn, the better they get at completing tasks repeatedly and refining their performance with each encounter.
3. Autonomous Things (AuT)
Autonomous Things (AuT) are artificially intelligent systems/machines that interact and accomplish tasks in the physical world. Common examples of AuT in manufacturing are IoT-enabled smart machines, collaborative robots, and self-navigating vehicles to name a few. Due to the advancements of Modern AI, AuT can perceive and interact with their environment, making quick autonomous decisions in the face of unpredictability and varying human involvement.
10 Exciting & Breakthrough AI in Manufacturing Examples
AI in Manufacturing Example #1: Collaboration of Robots and Humans
Since the third industrial revolution which saw the emergence of automation, robots have typically been relegated to performing repetitive tasks with extreme efficiency. The problem is that these robots have often been removed from people, shut behind barricades or glass enclosures. And there’s a good reason for this separation. Since the robot or machine typically followed a strict process with little self-awareness, humans and robots were unlikely partners.
However, with AI technology, manufacturing robots receive a huge leap forward in the realm of collaborative work and shared workspaces.
Collaborative robots, or cobots, are robotic machines that work alongside humans, performing manufacturing tasks like basic assembly, screwing, fastening, sanding, polishing, and more with high levels of precision. With the advancements in AI technology, cobots bring several benefits to the manufacturing table.
- Adaptability and Scalability: Using machine learning algorithms, cobots can handle a wide variety of tasks while cycling through multiple functions. Since the AI in cobots can be reprogrammed relatively easily, AI-enabled cobots can adjust to scaling manufacturing strategies throughout their lifetime.
- Collaborative Operation: Using advanced sensing technologies, cobots perceive and learn from their environment, enabling them to be responsive to human gestures and movements while making them safer for humans within shared workspaces.
- Cost Reduction: Cobots bring certain monetary advantages to the manufacturing shop floor. As opposed to traditional industrial robots, cobots don’t need a dedicated space to function, which frees up valuable space for other manufacturing activities.
This collaboration between humans and cyber-physical systems allows workers to focus on strategic tasks, innovation, and problem-solving while also enhancing overall productivity, safety, and flexibility within the manufacturing environment.
AI in Manufacturing Example #2: Advanced & Intelligent Data Analysis
One of the key benefits of AI in manufacturing is its keen ability to process large amounts of data quickly through both machine learning and deep learning algorithms. This capability is sorely needed in today’s manufacturing environment.
The manufacturing industry in the US generates a whopping 1,812 petabytes of data every year, more than any other sector. This number is almost double the next highest sector which only generates 911 petabytes per year. While this level of data acquisition is impressive and beneficial for the industry, it's no surprise that companies are feeling the weight of processing all that data.
However, AI-powered analytics tools take these large data sets and turn them into concise indicators and actionable items, leading manufacturers to experience the following benefits.
- Real-Time Data Processing: AI’s ability to process data quickly allows manufacturers to make decisions based on up-to-date and accurate information.
- Autonomous Data Analysis: Instead of vast spreadsheets that require some form of human construction and compilation, AI-powered data analysis can be relatively autonomous, enabling teams of people to focus on decision-making and less on compiling data manually.
- Interoperability: Artificial intelligence facilitates the centralization of information across IoT devices, other enterprise software, and people. This interoperability helps create a strong digital thread that connects and unifies the manufacturing environment.
AI in Manufacturing Example #3: Enhanced Communication with Translation AI
Good communication is a high priority for any manufacturing business. While standardization and other lean methodologies all help to raise the level of communication within a business, there’s one last hurdle that has been extremely difficult to overcome: the language barrier.
For businesses and supply chains that span the globe, communication and standardization of methods can take a lot of work. How can you standardize processes globally if your facilities speak different languages?
Advancements in AI translation technology have enabled businesses to share knowledge with their other locations, suppliers, and customers, regardless of language. This translation AI leverages deep learning neural networks to analyze and recognize patterns from multiple languages, enabling businesses to communicate standards effectively.
By leveraging AI translation tools, our work instruction software enables businesses to instantly translate their standardized instructions. This capability means that US factories can standardize their best practices and share them with other facilities in locations like Germany or India, ensuring that the best practices are followed consistently around the globe.
Since the length of any text will vary depending on the language, VKS leverages AI translation tools to show the length of the longest translated text automatically. The longest translated text is represented by the yellow bars behind the native text. In the example below, the German text is the longest, requiring no representation.
AI in Manufacturing Example #4: Digital Twins Mimic and Solve Live Manufacturing Events
The idea of twin technologies has been around since the Apollo 13 space mission in 1970. At the time, NASA used a real-life replica of the Apollo 13 spacecraft on Earth to troubleshoot issues. Using what they discovered, they directed Astronauts in space with the right methods to solve their oxygen issues.
Today, twin technology uses cheaper and more powerful digital copies of real-life systems. Through IoT sensors, anything that occurs in the real world is automatically replicated in the digital twin environment.
With the introduction of AI, digital twins have advanced even further, becoming autonomous key decision-makers within the manufacturing environment.
For instance, electric motors with IoT sensors can measure temperature, vibrations, RPM, usage hours, etc. These sensors then relay this information to an AI-powered system that processes the data to precisely monitor the conditions of the machine.
The AI system then uses its machine learning algorithm to perform the following actions.
- Real-Time Anomaly Detection: Using the mass amount of data generated by the digital twin, AI algorithms detect anomalies and errors within the machine in real time, helping to reduce downtime and waste.
- Proactive & Predictive Analytics: Using this anomaly detection capability, the digital twin AI can predict issues that may arrive in the future based on trends and/or fluctuations. Companies can use this data to enact preventive measures, enabling businesses to be proactive instead of reactive.
- Adaptive & Autonomous Decision Making: With vast amounts of IoT sensor data, the AI digital twin can auto-adapt to real-time conditions and requirements, such as energy consumption, resource usage, maintenance needs, and other factors.
- Dynamic Simulation: Using AI-driven simulations, the digital twin can run test scenarios in a completely safe and cost-effective digital environment. This capability enables businesses to make informed decisions for machines that are highly influenced by complex and dynamic factors.
AI in Manufacturing Example #5: Predictive Maintenance
A key example of AI in manufacturing over the past few years has been predictive maintenance, which actively monitors machine use to determine the machine's need for service and/or maintenance.
In the past, regular maintenance has been largely preventative, where a machine experiences planned maintenance downtime at regular pre-scheduled intervals. This ensures that machines are always safe to use and not exposed to extensive wear and tear.
The problem is that this process is inherently wasteful since the machine may not need maintenance at the pre-scheduled time. Not to mention, maintenance crews are going into the situation blind without any diagnostic data. Both of these factors contribute to an increase in the time and frequency of planned downtime.
However, through IoT sensors and machine learning, AI-powered predictive maintenance systems can monitor the health of machines in real time, enabling maintenance crews to know exactly when machines need to be serviced and what parts need to be replaced. Knowledge of both of these factors allows manufacturers to experience less downtime and save money.
AI in Manufacturing Example #6: Cyber-Physical Systems Facilitate Quality Control
Through the combination of AI-powered autonomous things (AuT) and quality control systems, alongside quality control inspectors, manufacturers can detect and analyze defects with incredibly high precision and consistency.
First and foremost, quality starts with your workers. Our work instruction software provides inspectors with a standardized method to follow, thereby decreasing inspection times by 75%.
At the same time, manufacturing inspectors can add to their quality control measures by using the following AI-powered quality control systems and tools.
- Vision System: Through computer vision and image processing techniques, AI quality systems and paired cobots can detect defects that the naked eye would miss. These defects can include scratches, imperfections, or improperly fastened bolts and/or screws.
- Root Cause Analysis: With high levels of traceability across the production line, AI quality systems can trace defects to their initial root cause, enabling manufacturers to quickly fix issues at the source.
- Statistical Process Control (SPC): Using machine learning algorithms, AI systems can monitor statistical trends and variations that may affect quality. This data can be compiled into an SPC chart, allowing manufacturers to visualize these trends and take corrective actions when needed.
- Defect Sorting: Quality control AI can sort and track defective units based on their type and severity. As the system monitors and classifies defects, the AI will calculate if the unit is worth salvaging based on the specific cost and time requirements of the production line.
Pro Tip: Using VKS DataConnect, our work instruction software will communicate with quality inspection tools such as vision systems and test benches. Once a unit is complete, the operator can place the unit within the testing area and activate the automated inspection tool directly from their VKS Guidebook.
AI in Manufacturing Example #7: Inventory & Warehouse Management
The examples of AI in manufacturing are not solely based on production. Increasingly, manufacturers are turning to AI systems to help manage their pre and post-production inventories.
These AI systems can perform the following duties.
- Monitor inventory levels and the flow of materials.
- Alert teams when inventories are low.
- Autonomously order more materials when needed.
- Identify bottlenecks within the warehouse and production lines.
- Create more efficient routes for workers, machines, and products to travel.
For example, companies like Amazon, which use a combination of human workers and automated robots within their fulfillment centers, use AI to facilitate the flow of incoming and outgoing orders. The AI creates the optimal sequence of pick-ups and the optimal route for multiple moving robots.
Similar to optimizing production based on the theory of constraints, an AI system can evaluate historical data, detect constraints in the production line, and adjust inventory levels accordingly.
Likewise, AI can benefit pharmaceutical or food service companies where ingredients have a specific shelf life. An inventory AI will calculate the required amounts, when they will arrive, when they will depart, and other potentially impactful factors.
AI in Manufacturing Example #8: Intelligent & Connected Supply Chains
A healthy supply chain is the key marker of a high-performance manufacturing operation. Manufacturers need their supplies and products to be on time. They also need to keep track of their materials and products before and after production.
However, managing materials and products across multiple suppliers and customers can be complex and time-consuming.
AI enables businesses to extrapolate data from multiple suppliers and retailers, creating one strong centralized and connected source of information. This data can be transformed into actionable insights through several methods.
- Supply Chain Visibility: With one centralized source of data, AI provides manufacturers with real-time supply chain visibility into the movement of goods, production status, inventory levels, and order tracking. AI can then use this data to generate alerts when key activities or issues occur.
- Predictive Supply Forecasts: By analyzing past activities and determining possible future outcomes, AI enables manufacturers to predict how much product they will need and when they will need it.
- Enhanced Supplier Relationships: AI supply chain management tools can analyze supplier performance over time and highlight potential issues or trends. With this data, manufacturers can proactively improve supplier relationships and/or find better suppliers that fit their needs.
- Logistics Optimization: Much like inventory paths, AI-powered supply chains can analyze traffic, weather, fuel efficiency, and delivery priorities to determine the optimal logistics routes.
AI in Manufacturing Example #9: Demand Forecasting Drives Future Strategies
As AI takes data analysis to new levels of possibilities, it is no surprise companies are using it to predict product demand and anticipate fluctuations in their target markets. Through machine learning algorithms, AI uses various data sources to forecast product demand. These data sources can include:
- Historical Data: Past sales data, seasonal trends, and other recurring patterns.
- Contemporary Trends: Social media patterns, competitor activities, customer behaviors, and marketing shifts.
- External Factors: Economic performance of target population, weather forecasts, and geopolitical events.
For example, clothing companies highly rely on seasonality, fashion trends, and customer preferences to make sales. This AI enables them to monitor key market indicators and adjust their productions and inventories accordingly.
However, the future is never 100% predictable, meaning that demand forecasting comes with a certain level of inherent risk if taken at face value. For this reason, AI can also run through risk analysis and various simulations to give companies a complete picture of the forecasted demand as well as the potential costs should they proceed with the prediction.
AI in Manufacturing Example 10: Cost Reduction and Enhanced Profitability
To finalize our list of AI in manufacturing examples, let's explore one of the most important motivating factors to implement artificial intelligence: money.
Through all the examples we’ve explored, the overall benefit of AI in manufacturing is a more effective and efficient system, which has major implications on how profitable a company can be.
AI-powered digital twins, predictive analytics, advanced multi-lingual communication, autonomous cobots, and cyber-physical inspection systems empower businesses to accomplish the below two objectives.
- Save money through increased efficiencies and waste mitigation.
- Create more revenue through advanced production methods.
So, with all that AI has to offer, how will you use AI in manufacturing?
