17 Remarkable Use Cases of AI in the Manufacturing Industry
Manufacturing | 16 min READ
    
What Is AI in Manufacturing?
AI in manufacturing involves using technology to automate complex tasks and unearthing previously unknown patterns in manufacturing processes or workflows.
Gurudatta Kamath
Gurudatta Kamath

Global Practice Head

Data, Analytics & AI

Birlasoft

Madan Mohan Mewari
Madan Mohan Mewari

SVP, Global Head-Delivery and Operations

Manufacturing and Services

Birlasoft

 
Using critical Fourth Industrial Revolution (4IR) technologies such as machine learning, automation, advanced and predictive analytics, and IoT (Internet of Things), manufacturers can monitor their facilities in real-time. This helps collect vast amounts of operational data to:
  • Track core KPIs like OEE, production rate, or scrap rate
  • Forecast accurate delivery dates and avoid missing deadlines
  • Predict potential disruptions to the supply chain
  • Troubleshoot production bottlenecks
  • Spot and fix equipment inefficiencies as and when they develop
According to McKinsey, the 4IR technologies are expected to create up to $3.7 trillion in value by 2025. AI alone can generate $1.2-$2 trillion in value for manufacturing and supply chain management.
Let’s see AI in action at some manufacturing companies to understand its potential.
AI in Manufacturing Examples – Inspiring Transformations
The impact of AI in manufacturing is game-changing. French food manufacturer Danone Group uses machine learning to improve its demand forecast accuracy. This has led to a:
  • 20% decrease in forecasting errors
  • 30% decrease in lost sales
  • 50% reduction in demand planners’ workload
Fanuc, a Japanese automation company, uses robotic workers to operate its factories round-the-clock. The robots can produce essential components for CNCs and motors, operate all production floor machinery non-stop, and facilitate continuous monitoring of all operations.
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Meanwhile, the BMW Group uses automated image recognition for quality checks, inspections, and to eliminate pseudo-defects (deviations from target despite no actual faults). As a result, they’ve achieved high levels of precision in manufacturing.
Another company that’s benefited from AI in manufacturing is Porsche. They use autonomous guided vehicles (AGVs) to automate significant portions of automotive manufacturing. The AGVs take vehicle body parts from one processing station to the next, eliminating the need for human intervention and making the facility resilient to disruptions like pandemics.
These are just a few examples of companies leveraging AI in manufacturing to improve overall productivity and operational efficiency.
Now, let’s explore why AI is so crucial for building the future of manufacturing.
Why AI is Critical to the Future of the Manufacturing World
Every manufacturer aims to find fresh ways to save and make money, reduce risks, and improve overall production efficiency. This is crucial for their survival and to ensure a thriving, sustainable future. The key lies with 4IR technologies, especially AI-based and ML-powered innovations.
AI tools can process and interpret vast volumes of data from the production floor to spot patterns, analyze and predict consumer behavior, detect anomalies in production processes in real-time, and more. These tools help manufacturers gain end-to-end visibility of all manufacturing operations in facilities across all geographies. Thanks to machine learning algorithms, AI-powered systems can also learn, adapt, and improve continuously.
Such capabilities are crucial for manufacturers to thrive in the aftermath of pandemic-induced rapid digitization.
According to McKinsey, companies using AI have witnessed cost savings and revenue growth. 16% of those surveyed noticed a 10-19% decrease in costs, whereas 18% saw a 6-10% increase in overall revenue.
AI systems also enable predictive analytics, which helps tackle operational challenges and disruptions to supply chains as well as the workforce. A McKinsey report suggests that AI can improve forecasting accuracy in manufacturing by 10-20%, which translates to a 5% reduction in inventory costs and a 2-3% increase in revenues.
Other benefits of AI in manufacturing include:
  • Predictive maintenance to reduce unplanned downtime
  • Operate near-shore facilities using advanced manufacturing technologies (3D printers, robots) to reduce labor costs and stay resilient despite supply chain disruptions
  • Create optimal, AI-enabled generative design to ensure efficiency and reduce waste
Now, let’s see how AI can be used in the manufacturing industry.
AI and the Future of Manufacturing
AI and the Future of Manufacturing

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How is AI used in the manufacturing industry?
In manufacturing, AI can offer the most value in planning and production floor operations. According to a BCG report, the most important AI use cases in the manufacturing industry are:
  • Intelligent, self-optimizing machines that automate production processes
  • Forecasting efficiency losses for better planning
  • Detecting quality defects to facilitate predictive maintenance
We’ll explore the most prominent use cases from each category mentioned above in subsequent sections. But before proceeding, let’s understand how AI will transform manufacturing.
How will AI change manufacturing?
The emergence of 4IR technologies will usher in the era of smart manufacturing with digital factories. According to the IFR (International Federation of Robotics), there were already 2.7 million industrial robots operating in factories worldwide in 2020. This was a 12% increase as compared to 2019, and with digitization initiatives growing exponentially, the trend is projected to grow further.
Manufacturers will continue to invest in technologies like AI and machine learning to further bring down production costs and improve time-to-market. In the aftermath of a global pandemic, manufacturers will strive to make their businesses more resilient by adopting technologies that automate tasks, forecast disruptions, and facilitate end-to-end control of all operations.
Now, let’s explore some AI use cases in manufacturing.
AI In Manufacturing Industry | Use Cases
  1. Artificial intelligence in logistics
  2. AI robots
  3. Artificial intelligence in supply chain management
  4. AI autonomous vehicles
  5. AI for factory automation
  6. AI for IT operations
  7. AI in design and manufacturing
  8. Artificial intelligence and IoT
  9. AI in warehouse management
  10. AI process automation
  11. AI for predictive maintenance
  12. AI-based product development
  13. AI-based connected factory
  14. AI-based visual inspections and quality control
  15. AI for purchasing price variance
  16. AI order management
  17. AI for cybersecurity
1. Artificial intelligence in logistics
A constant challenge with manufacturing is the losses from overstocking or under-stocking inventories. Overstocking often leads to wastage and lower margins. Under-stocking can translate into losses in sales, revenue, and customers.
Using AI, manufacturers can:
  • Track the production floor operations
  • Provide more accurate demand forecasting
  • Cut inventory-related losses and simplify resource management
Using technologies like 3D printing, manufacturers can produce serial parts in-house or at near-shore facilities, reducing their reliance on far-off, low-cost manufacturing locations and managing their inventories more efficiently.
Manufacturers can also use robots to replace human couriers (a useful technology especially during pandemics) and ensure uninterrupted last-mile deliveries. Marble, a last-mile logistics company, uses LIDAR sensors to deliver packages safely and more affordably.
2. AI based robots
AI robots tap into machine learning algorithms to automate decision-making and repetitive tasks at manufacturing plants. Since these algorithms are self-learning, they keep improving to handle their assigned processes better.
Additionally, AI robots don’t need breaks and aren’t as susceptible to errors as humans. So, manufacturers can easily scale their production capacity.
Robots can also do the heavy lifting on production floors while humans take charge of more delicate tasks. This improves workplace safety and overall production performance. McKinsey predicts that collaborative and context-aware robots can improve productivity by up to 20% in labor-intensive settings.
In the automotive industry, several manufacturers are already using robots to handle car assembly lines. In e-commerce and packaging, robots are a low-cost, faster, and less error-prone alternative to humans. Other applications include:
  • Welding
  • Painting
  • Drilling
  • Product inspection
  • Die casting
  • Grinding
17 Remarkable Use Cases of AI in Manufacturing
3. Artificial intelligence in supply chain management
AI-enabled systems can help manufacturers assess various scenarios (in terms of time, cost, revenue) to improve last-mile deliveries. AI can predict optimal delivery routes, track driver performance in real-time, and assess weather and traffic reports besides historical data to forecast future delivery times accurately.
AI can also give manufacturers greater control over their supply chains from capacity planning to inventory tracking and management. They can set up a real-time and predictive supplier assessment and monitoring model to get notified the moment there’s a supplier failure and assess the extent of supply chain disruption immediately.
One example is that of the carmaker Rolls Royce. It uses advanced machine learning algorithms and image recognition to power its fleet of self-driving ships, which in turn improves its supply chain efficiency and safely transports its cargo.
McKinsey predicts that AI-enhanced supply chains will reduce:
  • Forecasting errors by 20-50%
  • Lost sales by 65%
  • Over-stocking inventories by 20-50%
4. AI autonomous vehicles
Autonomous vehicles on the production floor, like the ones used by Porsche in a previous example, can automate everything from assembly lines to conveyor belts. Self-driving cars and ships can optimize deliveries, operate 24/7, and speed up the overall delivery process. The demand for autonomous vehicles is rising steadily and is expected to make up 10-15% of global car sales by 2030.
Connected vehicles equipped with sensors can also track information about traffic jams, road conditions, accidents, and more in real-time to optimize delivery routes, reduce accidents, and even alert the authorities in case of emergencies. This improves delivery efficiency and road safety.
5. AI for factory automation
Factory operators rely on their experience and intuition to monitor a plethora of signals across numerous screens and adjust equipment settings manually. This system puts the onus of troubleshooting, running tests, and other tasks on the operators as well, which further strains their capacity to work. As a result, operators sometimes take shortcuts, incorrectly prioritize activities, and don’t necessarily focus on adding economic value.
Two problems arise with this approach:
  • Human-intensive system can be error-prone, lead to equipment malfunction, and reduce overall factory efficiency
  • Reliance on experience makes it harder to replace factory operators. Also, when a skilled operator leaves, it also results in the loss of contextual knowledge on factory operations.
With AI, manufacturers can reduce labor costs significantly while improving overall productivity and efficiency at their plants. Other applications include:
  • Automate several complex tasks in factories
  • Spot any anomalies quickly because of continuous tracking and monitoring of operations and alert the technicians right away
  • Build a central repository for all operational data, along with context, making employee transitions a lot easier
  • Reduce the number of resources required to run a factory
  • Scale production easily according to demand fluctuations and manufacturing strategies
A prominent example of factory automation is Siemens. The company has teamed up with Google to improve shop floor productivity using computer vision, cloud-based analytics, and AI algorithms.
6. AI for IT operations
AI for IT operations, also known as AIOps, is crucial to optimize IT operations. According to Gartner, AIOps combines big data and machine learning to automate IT operations processes.
The biggest use case for AIOps is automating big data management. This would involve:
  • Collecting and integrating data from sensors and equipment in factories
  • Real-time tracking and monitoring of the shop floor and measuring their performance against set benchmarks
  • Using predictive analytics to identify, predict, and prevent IT service issues as well as to perform accurate capacity planning
  • Using big data analytics to track and improve resource utilization as well as infrastructure performance on the cloud
Other use cases include event correlation and analysis, performance analysis, anomaly detection, causality determination, and IT service management.
7. AI in design and manufacturing
AI-enabled software can help create several optimized designs for a single product. The software, also known as generative design software, requires engineers to provide certain input parameters such as:
  • Raw materials
  • Size and weight
  • Manufacturing methods
  • Cost and other resource constraints
Using these parameters, the algorithm can generate various design permutations.
The software lets engineers can test various designs against a wide range of manufacturing scenarios and conditions to pick the best possible outcome. The carmaker Nissan is using AI to develop never-seen-before car designs in the blink of an eye. The process would take human designers months, or even years to complete.
Such software can also be used to pick the right recipes that lead to the least amount of raw material and energy waste.
8. Artificial intelligence and IoT
IoT refers to smart, connected devices equipped with sensors that generate large volumes of operational data in real-time. In manufacturing, this is known as IIoT or the Industrial Internet of Things. Together with AI, IIoT can help manufacturing processes achieve greater levels of precision and productivity.
Some of the most prominent uses cases of IIoT include:
  • Wearables like smart glasses to view instructions hands-free and perform real-time situation awareness
  • Continuous monitoring of equipment performance, energy use, environment temperature, and the presence of toxic gases for better workplace safety
  • Smart lighting and HVAC control for efficient energy consumption
  • Industrial analytics using data from edge devices on the production floor
Use Cases of AI in Manufacturing Industry
Use Cases of AI in Manufacturing Industry

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9. AI in warehouse management
AI can automate several aspects of warehouse operations. Since they collect data in real-time, manufacturers can monitor their warehouses continuously and plan their logistics better.
Demand forecasting can further help manufacturers take action to stock up their warehouses in advance and keep up with the customer demand without enormous transportation costs.
Robots in the warehouses can track, lift, move, and sort items, leaving the more strategic tasks to the humans and reducing workplace injuries.
Automated quality control and inventorying can reduce warehouse management costs, improve productivity, and require a smaller labor force. As a result, manufacturers can increase their sales and profit margins.
10. AI process automation
AI-powered process mining tools can identify and eliminate bottlenecks in production processes automatically. These tools also allow manufacturers to compare factory performance across several regions. This lets them standardize and streamline workflows to build better manufacturing processes.
Another use case is RPA (robotic process automation) where robots perform repeatable tasks on the shop floor independently. Human intervention is required only when the robots are faced with exceptions or anomalies. Similarly, robots can use computer vision to screen and inspect processes without any human intervention.
Process automation can also:
  • Reduce cycle times
  • Increase yields
  • Improve accuracy
  • Improve workplace safety
  • Boost employee morale and productivity
According to McKinsey, using AI in the semiconductor industry to automate processes can enhance yield by up to 30%, reduce scrap rates and testing costs.
11. AI for predictive maintenance
A McKinsey report states that the greatest value from AI in manufacturing is because of predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. BCG calls predictive maintenance the first Industry 4.0 priority, especially for cement producers.
AI-powered systems can:
  • Capture and process big data (including audio, video, and GPS) from sensors on the shop floor
  • Help spot anomalies or equipment inefficiencies to prevent unplanned equipment breakdown. This could be an odd sound in a vehicle’s engine or an assembly line malfunction.
  • Prevent unplanned equipment downtime to improve factory efficiency while reducing costs
  • Besides, fixing malfunctions in individual components is cheaper than replacing an entire machine.
12. AI-based product development
AI-based product development can help manufacturers create several simulations and test them using AR (augmented reality) as well as VR (virtual reality) before starting production. As a result, manufacturers can:
  • Reduce trial and error costs
  • Decrease time-to-market
  • Support their engineers in predicting any problems and prevent them before the product hits the market
  • Streamline the process of maintenance and debugging
With AI-based product development, manufacturers can enrich and speed up their innovation to come up with new and more progressive products before the competition.
Also, since the AI algorithms have a continuous feedback loop, they get better with each iteration and help build better products.
13. AI-based connected factory
Connected factories or smart factories built using sensors and the cloud are the way forward for the manufacturing industry. Smart factories help:
  • Provide real-time shop floor visibility
  • Monitor asset utilization
  • Establish remote, touchless systems
  • Enable real-time interventions
  • Build a single source of truth for all production data
  • Scale production capacity without any major disruptions
An example is GE’s “Brilliant Factory”. GE built one such facility in Pune, India to increase productivity and reduce downtime. They witnessed a 45%-60% increase in OEE in their connected machines.
14. AI-based visual inspections and quality control
AI-powered defect detection taps into computer vision, which uses high-resolution cameras to monitor every aspect of the production process. Such a system can flag defects that the human eye might miss and trigger correcting measures automatically. This helps reduce product recalls and cut down on wastage.
Detecting anomalies like toxic gas emissions on the fly also helps prevent workplace hazards and enhances worker safety at factories.
Another AI-based system is AR overlays, which compare the actual assembly parts with those provided by suppliers to spot any quality deviations. AR can also help with remote training and support so that technicians from any location can connect with those at a facility and guide them.
15. AI for purchasing price variance
For manufacturers, any variance in the cost of raw materials can affect their margins. Estimating raw material costs accurately and choosing the right vendors is a major challenge.
Using AI-powered dashboards, manufacturers can track:
  • Resource features like pitch, diameter, material type, or finishing
  • Supplier dimensions like country, brand name, or performance data
Using AI-powered algorithms, manufacturers can:
  • Group the right product parts required in manufacturing
  • Predict a standard purchase price using historical data and market trends
  • Build a baseline for price comparisons across suppliers
This also simplifies the task of tracking parts purchased from different suppliers and manage all procurement data using a centralized system.
16. AI order management
Order management processes must be agile, cost-effective, and able to adapt as per fluctuations in the market, demand, consumer expectations, or manufacturing strategies. Using AI-based systems or robots, manufacturers can:
  • Automate order entry and other “copy-paste” jobs
  • Use sensors to track inventories to create purchase requisitions automatically
  • Manage the complexity of different types of orders across multiple channels
  • Make inventory planning and order management more seamless and transparent
An example of the benefits of AI-based order management is a Birlasoft use case. Birlasoft set up Oracle’s JD Edwards EnterpriseOne 9.1 for a pharmaceutical business to simplify inventory planning and order management. The implementation helped the business reduce order entry costs and ensure order profitability.
17. AI for cybersecurity
Research shows that manufacturers suffer the most from cyberattacks, as even a brief shutdown of the assembly line can prove costly. As the number of IoT devices increase, the threats will continue to grow exponentially. Smart factories are particularly susceptible to cyberattacks.
AI-driven cybersecurity systems and risk detection mechanisms can help secure production facilities and mitigate threats. Using self-learning AI, manufacturers can spot attacks across cloud services and IoT devices and interrupt them in seconds, with surgical precision. The system can also alert the right teams to act immediately to prevent any further damage. Using sandboxing, code signing and other such security measures can help combat cyber threats to IIoT technologies.
Key Takeaways – Why Manufacturers Must Adopt AI Sooner
A McKinsey survey found that companies embracing digital transformation in manufacturing are leading the industry. They adopted 4IR technologies, such as big data analytics, AI, AR and VR, IoT, predictive analytics, automation, and robotics, among others.
As a result, they recorded several benefits in terms of manufacturing efficiency, productivity, and costs. These include:
  • 30-50% decrease in machine downtime
  • 15-30% increase in labor productivity
  • 10-30% improvement in throughput
  • 10-20% reduction in quality-related costs
Early adopters have already started gaining a competitive advantage by substantially lowering operating costs, improving time-to-market, and optimizing performance. These benefits will only grow over time, increasing the gap between the early adopters and the laggards in the industry.
Since implementing AI in manufacturing requires significant investment in terms of time, effort, and resources, and requires upskilling your personnel, it’s crucial to adopt AI as soon as possible.
Equally important is to complete pilot projects to avoid the pilot purgatory and scale them as quickly as possible. For those who haven’t even considered incorporating AI in their manufacturing processes, the time is running out.
 
 
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