Artificial Intelligence in Manufacturing

Massive accumulation of data turned manufacturing into a ‘blue ocean’ for AI adoption. AI for manufacturing has come into being as companies started combining Artificial Intelligence technologies with the Industrial Internet of Things. The use of data science in manufacturing is becoming more and more widespread, as the global transformation gets underway.

The new technology can help  highlight and solve numerous pain points.  AI for manufacturing is likely to change the landscape of the entire industry over the next two to five years. Manufacturers face such challenges as inflexible production lines and soaring costs, not to mention the unstable product quality. Employing data science in manufacturing can help businesses automate processes, forecast market trends, schedule production, and improve monitoring efficiency.


How AI is used in manufacturing – potential use cases

Food quality and labeling

Complexity: 1 (Average for  food checking, below average for  food labeling).

Data needed: Labeled images of normal and defective foods. For text recognition (e.g., for packaging), even non-labeled images may be sufficient.

Influence on business: Reduces production waste, prevents spoiled products from getting to market with high accuracy, less risk of food contamination than manual checks.


  • While human-led control in this task can lead to arbitrary decisions, AI can often pick up  changes in the product that otherwise would be impossible for a human to detect  in a continuous line of similar items.
  • Additional push for manufacturers to introduce AI in their food-producing lines materialized because of COVID-19, as such technologies help significantly limit human interaction with foods. 

Price: Same as for  defect detection in general.

Examples: Another similar use case of AI in this area is checking the quality of food production. Popular usage apart from the production check itself is verification of labeling and dating text on products. Computer Vision systems can perform this task with precision  using text recognition methods.

Steel Quality Inspection

Complexity: 2 (Below average for simple defect detection and higher for defect classification. The main challenges lay in real-time computations).

Data needed: As in the general case, labeled images of normal and defective products are required. Data can be gathered in the production process.

Influence on business: Allows for defects detection on almost all production processes with constant precision.


  • AI systems find steel defects with a much higher probability than human checks.
  • More importantly, they allow manufacturers to check the entire volume of steel produced. In classic quality control systems, only samples of the ready output production are taken. Thus the percentage of detected faults depended on sampling, with entire batches of products being discarded. AI systems allowed for a much more thorough check that does not depend on sampling.

Price: Mainly determined by the machine vision system and real-time computation costs. But it is usually much less than the consequences of producing steel with undetected defects.

Examples: According to Accenture and Frontier Economics, 45% of all major steel-producing companies worldwide are developing such technology and expect the to  implement it in the next 10 years. Many such Computer Vision-based systems exist already and are used in defect control among ready steel production.

Advanced Technology Manufacturing

Complexity: 3 (Slightly above average due to the large variety of products in the industry, each requiring an individual approach).

Data needed: As in the general case, labeled instances of normal and defective production, either in the form of images or in sensor data. They can come from the testing phase for complex products such as cars. Another source of data can be the historic checks in the production areas. Often machines already use detection schemes, just not AI-optimized.

Influence on business: Allows spotting defects in  high-technology products before  the customer renders it unusable, thus improving the company’s reliability.

Benefits: Combines many factors simultaneously and can use large amounts of historical data, which is not fully available for human revision.

Examples: Many companies use automatic AI defect spotting systems that compare the resulting product parameters and performance with expected values. Items that could potentially not work with customers as needed are filtered out. For example, the system can gather parameters and/or images of  aircraft parts and decide if these parameters fit into the standard categories.

Quality Control and Defect Detection

Complexity: 3 (Usually corresponds to the defect’s complexity, but generally, it is below average for surface-level defects).

Data needed: Labeled images of normal and defective production. It is also possible to extract images from the video feeds.

Influence on business: Dramatically reduces the portion of defective production that reaches the customer.


  • Artificial Intelligence can learn the patterns on its own, capturing some details that human observers would have trouble noticing and substituting humans in routine tasks.
  • Using Artificial Intelligence in these situations has proven to reduce the fraction of defective products distributed to customers, increasing  production effectiveness.
  • The same Neural Network can evaluate multiple production locations, freeing up the costly efforts of highly-qualified professionals.
  • Additionally, using Artificial intelligence in the quality check becomes a way for the manufacturers to get along with their competitors. According to a 2019 Deloitte survey on AI adoption in manufacturing, 75% percent of surveyed manufacturers invested in optimizing their production lines, with quality control as one of the main segments.

Price: This is mainly determined by detective hardware that is not yet in place: if the machine vision system is already used, only software and computation costs are left. Computation costs also depend on the level of required customization and the volume of data.

Description: Quality control is the manufacturing area where Artificial Intelligence is spreading rapidly and broadly. One of the main reasons behind this is the available hardware, as manufacturers have used different machine vision forms in quality control for decades. However, previous technologies either involved humans or used some direct decision-making substitutions. For example, manually setting conditions under which the image would classify as “normal.” Therefore, Computer Vision technologies gave a significant advantage in determining faulty production.

Often, such systems perform various types of visual inspection using Computer Vision as a step of the pipeline. The Neural Network is trained on labeled instances of defects to detect them. It learns the patterns behind the labeled classes to later sort defective products on its own.

Examples: Apart from those listed below, AI is widely used in various production stages in all possible manufacturing areas, including checking quality in the automotive industry, pharmaceutical manufacturing, and even printing.

Predictive Maintenance

Complexity: 4 (Starts from the average for the simplest machines and rises with their complexity).

Data needed: Historical sensor data on the machines, usually current real-time sensor data, production metrics, weather parameters, and even visual imagery in crucial machine points.

Influence on business: Reducing  downtime costs with  failure prediction, optimally redistributing the load between units, reducing repair costs, becoming proactive rather than reactive.


  • The first benefit is the reaction time, as the automatic AI system generally would have a much faster reaction to a malfunction, allowing either to react quickly or leave more time for decision-making.
  • The second general advantage is that a system can have a larger variety of indicators under constant control, automatically combining multiple sources of information and making a complex-based decision.
  • Reduced downtime is the biggest value predictive maintenance can bring into manufacturing . If  the imminent breakdown is correctly predicted, manufacturers can redistribute the production load on the other machines while repairing the one in question. According to some estimates, such preventive maintenance can save up to 40% on repair costs.
  • Additionally, such predictions can be based on different data types mentioned above, usually inaccessible to humans in such quantities.

Price: Depends on the machine sensors already installed and availability of a real-time data transfer infrastructure.

Description: Artificial Intelligence in predictive maintenance is an extremely delivering area of application, as it holds several crucial advantages over previous inspection and prediction methods.

Several controlling systems are developed. The primary method of the area is anomaly detection. It is based on the normal and current sensor data, historical performance, and even weather parameters. The other important algorithm is predictive analytics, which tries to estimate when the breakdown will occur and issues the respective recommendation to the operator. According to some estimates, 29% of all AI manufacturing implementations are in maintenance.

Examples: There are numerous examples of companies currently implementing it in production. For instance, predicting the next maintenance time with AI, identifying faulty components, automatic maintenance and status updates for remote locations without human intervention, controlling all processes in the facility centralized, optimal load distribution, real-time security optimization.

Process Automation

Complexity: 4 (Can be lower for simple routine automation, but rises with the complexity of tasks)

Data needed: Highly depends on the task but usually must include gathered historical information. This includes the smaller subtasks, which often form the larger ones. Suppose the robot helps  sort packages in the warehouse. Then it will usually need images of the items it works with and typical commands on how to sort them. In cases of moving robots, warehouse navigational data is required as well.

Influence on business: Increases speed of operations while working with large amounts of data and products. AI systems perform with constant accuracy, are not susceptible to fatigue, and free up human operators for more complex production tasks. 


  • AI allows performing routine operations faster and more efficiently than the usual types of automation, with the ability to learn and improve performance over time.
  • Automation of manufacturing processes using AI is currently aimed mainly at routine tasks. There, traditional automation is impossible, and human work is either hazardous or repetitive.
  • Another push to the industry, similar to food production, was given by the COVID-19 pandemic. AI-powered automation can bring more safety to the manufacturing process and the product itself.

Examples: Manufacturing task automation, internal processes streamlining, irregularities detection, warehouse management, intelligent accounting systems.

Design Improvement

Complexity: 4 (Slightly above average and depends on the type of product and requirements for it. Also is determined by the method we want to use for generation)

Data needed: Resulting product requirements, such as materials, required functionality, size, etc., as well as manufacturing specifications.

Influence on business: Allows to reach the optimal product parameters much faster and reviewing more possibilities, often as a preparation step for human designers.


  • Allows for the review of many possible designs that would be impossible for humans. Often the selection of options arises from some unforeseen optimization opportunities.
  • The development of this field goes hand in hand with the development of 3D printing, but it can be successfully used alone. AI tries to construct such a product that will be the best for the client’s requirements, analyzing potential design decisions.
  • Systems also can automatically integrate the historical testing data in the process, ensuring learning on the previous progress.

Examples: While still in development, some impressive results were already achieved, such as lighter cabin parts for Airbus. It can also help generate more optimal high-technology devices, or even optimal food, polymer production, drug design, or urban planning solutions.

Demand Prediction

Complexity: 4 (Starts from the average for simplest predictions and rises with their complexity).

Data needed: Historical data on the product consumption/demand, sales data, price information, marketing campaigns data, product reviews. Macroeconomic parameters, online search trends, or any other data available are commonly added.

Influence on business: Helps effectively plan the company’s consumption and production for the following periods based on historical data and current parameters, reduces the number of misused resources in the manufacturing process.


  • One of the main benefits of AI-based demand forecasting is flexibility. It can dynamically adapt the prediction based on the new data without intervention.
  • It is much quicker and effective on large quantities of data, as it can automatically find hidden data patterns.
  • Allows for higher customer satisfaction, as one of the biggest reasons for customer dissatisfaction is product unavailability. AI can predict when the company will need the selected products, where, and for which customer. Therefore, it leads to company logistical improvements as well.
  • A more accurate understanding of future demands improves  marketing processes and makes it easier to meet customer demands.

Description: Regression and Time Series Forecasting are usually used to predict  future company indicators based on previous performance. Both short- and long-term forecasting is possible, with the latter type usually serving the company’s strategies planning. Such forecasting can help reinforce strategic development decisions, showing how the company will perform given future parameters. It is also possible to apply Natural Language processing models for gaining access and analyzing text product reviews or social media posts.

Examples: Testing price policies, introducing new products to the market, trends prediction (even fashion trends), and more.

Food Waste Reduction

Complexity: 5 (One of the most challenging cases in the area because of the variety of factors)

Data needed: Customer demands, data on sales of products in question. Also, current and future economic situations, seasonal taste changes, and other data can be used.

Influence on business: Food waste is one of the most unnecessary expenses for any food-related business. Optimization of supply and demand can greatly affect both expenses and customer experience. Knowing from the start what exactly to do with every portion of production can even lead to lower and more attractive food prices on the client’s shelves or menus.


  • Allows for better planning future periods accordingly and realistically, spending the operational cost on the needed items.
  • Adjusts performance in time and learns new tendencies with each new period.
  • Additionally, it can influence climate issues. Food waste is one of the most significant  global waste contributors. According to estimates, it accounts for up to 20% of our overall water usage.

Description: This use case aims to reduce the amount of food that businesses leave in the trash bin. Therefore, the task includes analyzing  past performance data, future trends and predictions, consumer demand, and many additional indicators. As making their food demand predictions is a big challenge for many businesses, such technology allows them to plan their orders to suppliers correctly. This allows the products to be used before their expiration date arrives.

Examples: Production conditions improvement, food safety control, expiration date management and optimizing storage conditions, automated food sorting.

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How Get Started With AI in Manufacturing?

The role of AI for manufacturing is becoming more and more important. However, the full scope of the future transformation is yet to be seen.  AI manufacturing companies can fundamentally redefine their operations and get a significant competitive advantage. If your business is going to be one of them, now is the right time to embrace new technology. 

Business leaders, who want to benefit from AI for manufacturing, should keep in mind three principles: 

  • Data is the starting point. Before employing data science for manufacturing, you should study the available data: its quality, quantity, and type are crucial. This step allows you to understand what can be achieved. 
    Nonetheless, little or no data isn’t a reason to throw in the towel. There is still a way to follow through with data science applications in manufacturing. Look for public sources or contact your business partners. Design a strategy to get some input for data science in manufacturing industry. Create a consumer-facing application to harvest new data or convert some analog processes into digital.
  • Start small. Rushing headlong into the most complex use cases of AI in manufacturing sector is not a good idea. Start with a simple project and get an application ready faster. After that, you can build on it over time, as AI applications are scalable. Your business will gradually explore new capabilities, getting more and more value. 
  • Embrace failures. Implementing Artificial Intelligence in manufacturing market isn’t easy. It requires both time and resources, establishing interdisciplinary collaboration, etc. Each failure contributes to organizational learning and is crucial for progress. It is better to fail fast and early to minimize the investments of time and costs.

The Role of AI in Manufacturing Industry: Summary

Manufacturers have to cope with different challenges in operations and production. Rising costs, insufficient agility of production lines, unstable yield, and quality are the most pressing problems. The use of AI in the manufacturing industry addresses those difficulties.

The past decade has witnessed the rise of algorithms and numerous data science tools for pragmatic applications. For instance, AI has been successfully implemented in the financial sphere. Artificial Intelligence and manufacturing are likely to be closely intertwined in the future. AI can provide solutions for three types of manufacturing problems:

1) Increasing process automation and boost smart operation, cutting operational costs;

2) Forecasing market trends and schedule production, enabling on-demand manufacturing. With the AI in automotive manufacturing or any other sector, the companies will require the lowest inventory possible;

3) Improving quality monitoring and product yield.

With the rapid technological transformation, the Artificial Intelligence manufacturing industry is here to stay for the foreseeable future. AI will tackle the main pain points bringing the entire sector to a new level.


How is AI used in manufacturing?

Artificial Intelligence has numerous uses in manufacturing, including product design customization, optimizing logistics, predictive maintenance, inventory management, etc.

How will AI affect manufacturing?

The new technology will improve process automation, allow on-demand production, and enhance quality inspections.

When was AI introduced to manufacturing?

Robots have been used in manufacturing for decades. Still, it wasn’t until 2019 when AI was combined with the Industrial Internet of Things, and the use of Artificial Intelligence had begun in earnest.

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