How AI will transform the Manufacturing Industry?

AI is widely employed in a variety of areas, including gambling, finance, retail, commercial, and government, and is slowly making its way into the manufacturing sector, helping industrial automation. AI-driven robots are paving the way to the future by providing a slew of benefits, including new prospects, increased production efficiency, and a tighter match between machine and human interaction.

In the meantime, what role does artificial intelligence play in the development of industries and processes? Are there any current business use cases that provide reasonable value? Do all artificial intelligence applications necessitate a high level of expertise?

In order to address these questions, an organization should choose one of two paths. The first step is to determine whether or not a manufacturing company is ready to build and deploy AI-based solutions in-house. The capacity to use AI-based solutions and expertise as part of commercial services is the second.

Impact Of AI In Manufacturing Industry

The manufacturing business has always been willing to adapt to new technologies. Since the 1960s, drones and industrial robots have been a component of the manufacturing business. The next automation revolution is only a few years away. If organizations can maintain inventories tight and cut costs by implementing AI, the Manufacturing Industry is likely to experience an empowering development.

Having stated that, the manufacturing industry must prepare for well-organized manufacturing plants in which the supply chain, design team, production line, and quality control are all well-coordinated into an intelligent engine that generates valuable knowledge insights.

While your company may not be on track to be the next Skynet, most manufacturers are amazed at how quickly commercial AI solutions are being used to improve or revolutionize traditional manufacturing processes. The more our manufacturing practice looks at this, the more we’re impressed by how useful it can be in delivering incremental or even game-changing business results.

Let’s look at how AI technologies will transform the manufacturing industry!

Role Of AI In Manufacturing Industry

1. Predictive Maintenance

For manufacturers, ongoing machinery maintenance is a significant investment, and the change from reactive to predictive maintenance has become a must. AI has succeeded to save organizations important time and resources by applying powerful AI algorithms and artificial neural networks together with IoT, to create predictions regarding asset malfunction and briefing technicians ahead of time. Getting real-world advice from an IoT app development company can help your company save money.

It is used to predict when to perform maintenance, how long equipment can function without failing, how to prioritize equipment maintenance, and even how many spare parts to have on hand. Artificial intelligence allows you to boost uptime, lower maintenance personnel expenses, and change the cost of spare components on your balance sheet.

Furthermore, predictive maintenance has aided in the extension of equipment life and has resulted in a reduction in overall labor expenses.

2. Safety in the Workplace

Artificial intelligence in workplace screening and safety has become one of the most widely chosen use cases, owing to the pandemic. AI may be used to recognize employees, conduct thermal checks, and track staff interactions for contact tracing and facility sanitization.

The same technology has also resulted in long-term solutions for preventing workplace safety incidents or expediting post-incident root cause analysis (for example, think slips, trips, and falls). Healthy personnel, a safer workplace, and sustained operations are all benefits of these solutions.

3. Machine Vision

AI mixed with computer vision is another topic to keep a close eye on.

The industry has witnessed rapid adoption in hundreds of proven use cases, including production, warehouse, quality inspection, and fleet management. These solutions have become more inexpensive as well as functional because of AI

Manufacturers are now employing machine vision and AI to reduce transactions and enhance capacity in warehouses and logistics.

Another application is fleet management, where in-dash cameras are used to monitor everything outside the car, including signs, other vehicles, walkers, and driving behaviors. The majority of these systems incorporate in-cab monitoring and other safety features that benefit drivers as well as businesses. These solutions are cost-effective, with benefits such as route and fuel optimization, lower insurance premiums, and fewer at-fault accidents.

Because of their widespread commercialization and low cost, these technologies are now available to any manufacturing company, and they frequently self-fund returns in weeks or months.

4. Building Administration and Security

Many manufacturing companies struggle to strike a compromise between the cost and demand for real security teams while also protecting their assets and staff. The majority forego traditional physical security, putting the safety of their facilities and personnel at risk. Companies can now afford to install security solutions that recognize common security scenarios, such as the arrival of a guest or delivery car, theft, or an active shooter, thanks to developments in sensors, building management systems, and artificial intelligence.

5. Directed Automation

The use of AI and robots in the manufacturing sector is particularly noticeable since they revolutionize mass production. Robots can do repetitive tasks, design the working prototype, increase competence, build automation solutions, eliminate human mistakes, and provide an improved quality check.

6. Human-Robo Collaboration

In manufacturing factories all across the world, there are a large number of robots at work. People are concerned that robots will replace them in their jobs. Laborers are subject to limitations and can work in shifts. Robots, on the other hand, are increasingly profitable and can work 24 hours a day, seven days a week.

Robots can be used to connect assembling units. Individuals can be hired for higher-level programming positions as well as business-form executives.

Organizations can focus on commercial activities and build their deals and benefits as the use of robots in the manufacturing industry grows.

7. Digital Twins

Finally, the AI and Digital Twins approach employ advanced twin technology to create a virtual representation that replicates the plant, object, or machine segment’s physical features.

The computerized twin can mimic the constant data linked to this current reality by using cameras, sensors, and other data collection methods.

In simpler terms, an advanced twin can create a live replica of a manufacturing plant. Using a computerized model, designers may accurately predict wear, development, and partnerships with various devices.

8. Quality 4.0

A vast number of manufacturers believe it is difficult to maintain consistent quality. Because of the increasingly complex character of items and extra parts, companies all over the world may have a challenge in maintaining product quality.

They may produce exceptional quality things by applying AI calculations created through AI using industry 4.0 technologies. If an issue arises during the initial stages, we may address it immediately away. Quality 4.0 ensures that things are of higher quality and that manufacturers have a higher yield.

9. Cybersecurity

With an ever-increasing range of phones and limited cybersecurity resources, we’re turning to artificial intelligence to assist us to address some of the most pressing cybersecurity issues.

As you can see, artificial intelligence is quickly becoming a part of commercial solutions across the industrial industry’s whole value chain. These AI solutions give businesses the ability to deploy and deliver on top and bottom-line goals right away, while also guiding them toward more advanced use cases in the future.

10. Simplified Operating Costs

Bringing AI to the manufacturing business would require a considerable capital investment, but the return on investment will be significant. Businesses can benefit from significantly lower operational costs when intelligent machines begin to handle day-to-day tasks.

11. Supply Chain Optimization

AI Technology can aid in the optimization of manufacturing supply chains by anticipating market demand.

Its algorithms can effectively estimate market demand by taking into consideration a range of criteria such as location, weather patterns, and socioeconomic situations, which helps organizations optimize their go-to-market strategies well in advance.

12. 24×7 Production

Humans are required to work in three shifts to ensure continuous output, whereas robots can work in the production line 24 hours a day, seven days a week. Businesses have been seen expanding their manufacturing capacity in order to fulfill the increased demand of clients all around the world.

13. Generative Design

Manufacturers are striving to match the demand for higher personalization and quality of products as consumption rises.

Generative design is a procedure in which developers may now input characteristics such as material types, available manufacturing processes, budget limits, and time constraints into an AI algorithm to generate a list of feasible configurations. Machine Learning is also used by the algorithm to test various setups in order to fine-tune the best options.

The key benefit of this approach is that it removes personal biases and tests design possibilities in a number of production scenarios to guarantee that performance requirements are met.

14. New Opportunities for Humans

Workers will be able to focus on sophisticated and inventive tasks as AI takes over the manufacturing facility and automates mundane and tedious human labor. Humans may focus on driving innovation and bringing their businesses to advanced levels while AI takes care of unskilled tasks.

Use Cases for AI in the Manufacturing Industry

1. Supply Chain Management With Artificial Intelligence

Manufacturers can use AI-enabled systems to evaluate multiple scenarios (in terms of time, cost, and income) to optimize last-mile deliveries. AI can forecast future delivery times precisely by predicting the best delivery routes, tracking driver performance in real-time, and assessing weather and traffic reports in addition to past data.

From capacity planning to inventory tracking and administration, AI can help manufacturers gain more control over their supply chains. They can set up a real-time and predictive supplier assessment and monitoring mechanism to be notified as soon as a supplier fails and analyze the scope of the supply chain interruption.

The manufacturer Rolls Royce is one such example. It powers its fleet of self-driving ships with advanced machine learning algorithms and image recognition, which increases supply chain efficiency and ensures cargo is transported securely.

According to McKinsey, AI-enhanced supply networks will reduce:

• Errors of 20-50 percent in forecasting
• 65 percent of sales were lost.
• Inventory overstocking by 20-50 percent

2. Autonomous Vehicles Powered By Artificial Intelligence

On the factory floor, autonomous vehicles, such as the ones utilized by Porsche in the previous example, may automate anything from assembly lines to conveyor belts. Self-driving cars and ships can optimize deliveries, work 24 hours a day, and cut the delivery time in half.

Autonomous vehicles are predicted to account for 10-15% of worldwide automotive sales by 2030, according to forecasts.

Connected vehicles with sensors may also track real-time data regarding traffic jams, road conditions, accidents, and other factors to improve delivery routes, prevent accidents, and even warn authorities in the event of an emergency. This increases delivery efficiency while also increasing road safety.

3. AI For Factory Automation

Factory workers use their judgment and experience to monitor a myriad of signals across several screens and manually alter equipment settings. This method also places the burden of troubleshooting, testing, and other activities on the operators, putting further strain on their ability to operate. As a result, operators are prone to taking shortcuts, prioritizing activities wrongly, and failing to focus on providing economic value.

Manufacturers can dramatically reduce labor expenses while enhancing overall productivity and efficiency at their factories with AI. There are also the following applications:

• Several complicated factory tasks can be automated.
• Because of the continual tracking and monitoring of activities, you’ll be able to spot any irregularities quickly and alert the specialists.
• Reduce the number of resources required to manage a factory by creating a central repository for all operational data and context, making employee moves easier.
• Easily adjust production to demand variations and manufacturing strategy.

4. AI For IT Operations

AIOps, or artificial intelligence for IT operations, is critical for optimizing IT operations. AIOps blends big data and machine learning to automate IT operations activities, according to Gartner.

The most common use of AIOps is the automation of massive data management. This would entail:

• Data from sensors and equipment in factories is collected and integrated.
• The shop floor is being tracked and monitored in real-time, and their performance is being measured against set criteria.
• Predictive analytics is being used to discover, anticipate, and avoid IT service faults, as well as to undertake precise capacity planning.
• Big data analytics is being used to track and enhance resource use as well as cloud infrastructure performance.

Event correlation and analysis, performance analysis, anomaly identification, causality determination, and IT management are some of the other use cases.

5. AI for Cybersecurity

According to research, manufacturers are the most vulnerable to cyberattacks, as even a momentary halt in production can be costly. The risks will continue to develop rapidly as the number of IoT devices grows. Cyberattacks are particularly vulnerable in smart factories.

AI-driven cybersecurity systems and risk detection algorithms can aid in the security and mitigation of industrial facilities. Manufacturers can discover assaults across cloud services and IoT devices using self-learning AI and interrupt them with surgical accuracy in seconds. The technology can also notify the appropriate teams, allowing them to take fast action to avert more damage. Sandboxing, code signing, and other security procedures can assist IIoT technology combat cyber attacks.

6. Management of AI Orders

Order management methods must be flexible, cost-effective, and adaptable to market changes, demand, consumer expectations, and manufacturing strategy.

• Manufacturers can automate order entry and other “copy-paste” operations with AI-based systems or robots.
• To construct purchase requisitions automatically, use sensors to track inventories.
• Organize the complexities of many order types among several channels.
• Improve the efficiency and transparency of inventory planning and order management.

A Birlasoft use case illustrates the advantages of AI-based order management. To ease inventory planning and order administration, Birlasoft set up Oracle’s JD Edwards EnterpriseOne 9.1 for a pharmaceutical company. The company was able to cut order entry expenses and increase order profitability as a result of the deployment.

7. Artificial Intelligence for Purchasing Price Variation

Any variation in the cost of raw materials can have an impact on a manufacturer’s profit margins. It’s difficult to precisely estimate raw material costs and choose the right vendors.

Manufacturers can use AI-powered dashboards to track:

• Pitch, diameter, material kind, and finishing are examples of resource characteristics.
• Dimensions of suppliers include country, brand name, and performance data.

Manufacturers can use AI-powered algorithms to:

• Organize the necessary product pieces for manufacture.
• Using historical data and market trends, forecast a typical buying price.
• Create a benchmark for price comparisons between vendors.

This also makes it easier to keep track of parts ordered from many sources and handle all procurement data in one place.

8. Visual inspections and Quality Control using AI

Computer vision, which uses high-resolution cameras to monitor every part of the manufacturing process, is used in AI-powered defect identification. A system like this can detect flaws that the human eye would miss and take corrective action automatically. This reduces the number of product recalls and waste.

Detecting anomalies such as harmful gas emissions on the fly also aids in the prevention of workplace dangers and improves factory worker safety.

AR overlays, which compare the actual assembly parts with those provided by suppliers to spot any quality discrepancies, are another AI-based method. AR can also assist with remote training and support, allowing technicians from any location to communicate with and guide those at a facility.

9. AI-based Connected Factory

The manufacturing industry’s future lies in connected or smart facilities built with sensors and the cloud. Factory automation can help:

• Visibility of the shop floor in real-time
• Keep an eye on how assets are being used.
• Set up touchless, remote systems.
• Allow for real-time intervention.
• For all production data, create a single source of truth.
• Increase manufacturing capacity without causing significant disruptions

GE’s “Brilliant Factory” is one example. To boost production and reduce downtime, GE built one of these facilities in Pune, India. In their networked machines, they saw a 45 percent to 60 percent boost in OEE.

10. AI-based Product Development

Manufacturers can use AI-based product development to construct several simulations and test them using AR (augmented reality) and VR (virtual reality) before going into production. As a result, producers will be able to:

• Costs of trial and error are reduced.
• Reduce the time it takes to get a product to market.
• Assist their engineers in anticipating and preventing problems before the product hits the market.
• Streamline the maintenance and debugging procedure.

Manufacturers can enrich and speed up their innovation with AI-based product creation, allowing them to come up with new and more modern items ahead of the competition.

Furthermore, because AI algorithms have a constant feedback loop, they improve with each iteration and aid in the development of better goods.

11. Predictive maintenance with AI

According to a McKinsey analysis, the largest value from AI in manufacturing comes from predictive maintenance, which is worth $0.5-$0.7 trillion globally. Predictive maintenance is the first Industry 4.0 priority, according to BCG, particularly for cement plants.

• Capture and process large data (including audio, video, and GPS) from sensors on the shop floor with AI-powered systems.
• Aid in the detection of anomalies or inefficiencies in equipment to avoid unplanned equipment failure. This could be a strange sound coming from a vehicle’s engine or a problem with the assembly process.
• Prevent unnecessary equipment downtime to boost industrial productivity while lowering expenses.
• Furthermore, repairing individual component failures is less expensive than replacing the complete machine.

12. Automation of AI Processes

Artificial intelligence-powered process mining technologies can automatically detect and eliminate bottlenecks in production processes.

Manufacturers can also use these technologies to compare production performance across multiple geographies. This enables them to standardize and streamline workflows in order to create more efficient manufacturing processes.

Another application is RPA (robotic process automation), in which robots independently perform repeatable activities on the shop floor.

Only when the robots encounter exceptions or abnormalities do humans need to intervene. Similarly, robots can screen and inspect processes without the need for human participation using computer vision.

Automation of processes can also:

• Cut down on cycle times.
• Increase your output.
• Enhance precision
• Enhance workplace security
• Employee morale and productivity can be improved.

According to McKinsey, utilizing AI to automate processes in the semiconductor sector can increase output by up to 30%, cut scrap rates, and lower testing costs.

13. Warehouse Management With Artificial Intelligence

Several parts of warehouse operations can be automated using artificial intelligence. Manufacturers can better manage their operations and monitor their warehouses since they collect data in real-time.

Demand forecasting can also assist manufacturers in planning ahead of time to fill their warehouses and meet customer demand without incurring exorbitant transportation costs.

Warehouse robots can track, lift, move, and sort objects, allowing people to focus on more strategic activities and reducing workplace hazards.

Automated quality control and inventorying can lower warehouse management expenses, increase productivity, and minimize the number of employees needed. Manufacturers can enhance their sales and profit margins as a result.

14. Artificial intelligence and IoT

Smart, linked devices with sensors that create enormous amounts of operational data in real-time are referred to as IoT. The Industrial Internet of Things, or IIoT, is a term used in the manufacturing industry to describe this. IIoT can help manufacturing processes attain higher levels of precision and productivity when combined with AI.

The following are some of the most well-known IIoT use cases:

• Smart glasses, for example, can be used to watch instructions hands-free and provide real-time context-awareness.
• For improved workplace safety, continuous monitoring of equipment performance, energy use, environment temperature, and the presence of dangerous gases is required.
• For effective energy use, smart lighting and HVAC management are used.
• Data from edge devices on the factory floor is used for industrial analytics.

15. Design And Manufacturing With Artificial Intelligence

AI-assisted software can assist in the creation of many optimum designs for a single product. Engineers must submit certain input parameters to the software, often known as generative design software:

• Raw materials
• Size and weight
• Manufacturing methods
• Cost and other resource constraints

The method may produce many design variations using these parameters.

Engineers can use the program to test multiple designs against a variety of production situations and settings in order to select the best possible outcome. Nissan is employing artificial intelligence to create never-before-seen car designs in the blink of an eye. Human designers would take months, if not years, to finish the process.

This type of software can also be used to select the best recipes for wasting the least amount of raw materials and energy.

16. AI-Powered Robots

Machine learning algorithms are used by AI robots to automate decision-making and repetitive operations in manufacturing plants. Because these algorithms are self-learning, they continue to improve in order to better handle their assigned activities.

Furthermore, AI robots do not require breaks and are not as prone to errors as humans. As a result, firms can quickly expand their manufacturing capacity.

On factory floors, robots may do the heavy lifting while humans handle the more delicate duties. This increases workplace safety as well as overall productivity. According to McKinsey, collaborative and context-aware robots can boost productivity by up to 20% in labor-intensive environments.

Several corporations in the automotive industry are already deploying robots to help with assembly lines. Robots are a low-cost, speedier, and less error-prone option in e-commerce and packaging. Other applications include:

• Welding
• Painting
• Drilling
• Product inspection
• Die casting
• Grinding

17. Logistics And Artificial Intelligence

Losses from overstocking or understocking inventory are a persistent concern in manufacturing. Overstocking frequently results in waste and poor profit margins. Sales, revenue, and consumers can all suffer as a result of understocking.

Manufacturers can use AI to:

• Keep track of what’s going on on the factory floor.
• Increase the accuracy of demand forecasting
• Reduce inventory losses and streamline resource management.

Manufacturers can build serial parts in-house or at near-shore facilities using technologies like 3D printing, minimizing their dependency on far-off, low-cost manufacturing locations and better controlling their inventories.

Manufacturers can also deploy robots to replace human couriers and assure uninterrupted last-mile delivery (a vital technique during pandemics, for example). LIDAR sensors are used by Marble, a last-mile logistics startup, to deliver products safely and more affordably.

AI Extra Benefits

Artificial intelligence and industrial automation have come a long way in recent years. Machine learning techniques, sensor advancements, and hence increased processing power have all contributed to the creation of a new generation of robots. Through machine intelligence, learning, and speech recognition, AI enables machines to receive and extract data, recognize patterns, learn, and adapt to new things or settings. Manufacturers will be able to do the following with AI:

• Make quick, data-driven decisions.
• Improved production outcomes are facilitated.
• Improve the efficiency of your processes.
• Reduce your operational costs.
• Improved scalability is made possible.
• Make product development easier

Furthermore, because AI is strong at interpreting and translating natural language, it will be easier for workers and managers to communicate with software. Users of software, for example, generally prefer to search for items rather than browse a lengthy menu. AI allows the software to understand the user’s intentions, making the system more spontaneous, resulting in better output and fewer errors.

Manufacturing Industry Trends with Emerging AI

Manufacturing will be impacted by AI in ways we haven’t considered. Nonetheless, we can look at some more notable cases right now.

Computer visualization has been used for quality assurance for a long time, detecting product faults in real-time. However, today that manufacturing requires more data than ever before, along with the reality that plant managers do not want to pay humans to enter data, AI plus computer vision can rationalize how data is gathered.

A factory worker should be able to take raw materials from the shelf and have the stock transaction made automatically based on the operation being observed by a camera. This will be the natural user interface, with the user simply performing the work at hand rather than inputting or scanning information into a system.

Second, AI will have an effect on the IoT. The Internet of Things will make it possible to send supplies and services to clients who may not be aware that they are necessary. IoT may also send detailed information back to producers and distributors, allowing them to examine quality and variables that may cause problems.

This will make augmented generative design processes easier, allowing items to be reimagined in more evolutionary ways.

What’s Next in AI for Manufacturing Industry?

Finally, manufacturing will be entirely automated in the near future. Artificially Intelligent Systems-enabled manufacturing processes would be capable of performing the essential procedures. It will also be capable of inspecting, improving, and performing quality checks on products without the need for human intervention.

Artificial Intelligence in manufacturing is predicted to increase from USD 1.0 billion in 2018 to USD 17.2 billion by 2025, according to Marketsandmarkets estimates, with a CAGR of 49.5 percent over the forecast period.

Conclusion

Proponents of AI argue that it is simply an evolution of automation, a natural outcome of Industry 4.0. It may be effective at creating, improving, and lowering the cost of goods. Human inventiveness, on the other hand, cannot be replaced in coping with unanticipated changes in tastes and demands—or in deciding whether to manufacture anything at all.