Unleash the Power of Artificial Intelligence in Manufacturing ! Discover How AI Redefining Product Quality and Productivity!

Prof.(Dr.) D G Mahto, Founder & CEO -


Abstract

Artificial intelligence (AI) embodies the concept of a machine equipped with cognitive abilities akin to human beings, including the ability to think and learn. When deployed in industry, AI holds the potential to dramatically boost operational performance, be it through automated processes or the employment of highly sophisticated robots.

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.

The emergence of 4IR technologies will usher in the era of smart manufacturing with digital factories. 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.

Keywords
Artificial intelligence (AI),  Concept of a Machine   Cognitive abilities,  Human beings, Think and Learn, Optimisation ,

Learning Outcomes 
After undergoing this article you will be able to understand the following 
1. Emergence of AI in Manufacturing: An Introduction 
2. Why investment on AI is necessary in today's world?
3. What are the area of application of AI Technology?
4. What are the steps of implementing AI Technology in manufacturing?
5. What are the benefits of AI in Manufacturing?
6. What are the disadvantages of AI in Manufacturing?
7. How is the future of AI in manufacturing?
8. Which AI tools are superior for manufacturing?
9. How is the future of AI in manufacturing?
10. Top Strategies to implement AI in Manufacturing.
11. Conclusions
12. FAQs
References 



1. Emergence of AI in Manufacturing: An Introduction 

AI has the potential to transform the manufacturing industry completely. Examples of possible upsides include increased productivity, decreased expenses, enhanced quality, and decreased downtime. Big factories are just some of the ones that can benefit from this technology. Many smaller businesses need to realise how easy it is to get their hands on high-value, low-cost AI solutions.

There are many possible uses for AI in manufacturing. It improves defect detection by using complex image processing techniques to classify flaws across a wide range of industrial objects automatically.

AI use cases revolve around the following technologies:

  • Machine learning: Using algorithms and data to automatically learn from underlying patterns without being explicitly programmed to do so. 
  • Deep learning: A subset of machine learning that uses neural networks to analyze things like images and videos.
  • Autonomous objects: AI agents that manage tasks on their own, such as collaborative robots or connected vehicles. 
2. Why investment on AI is necessary in today's world?
From predictive analytics to natural language processing, AI is not merely a technological tool but a catalyst for innovation. Companies that strategically integrate AI into their operations can gain a competitive edge by unlocking new revenue streams, enhancing customer experiences, and optimizing internal processes.

Artificial Intelligence, once a buzzword, is now a transformative force driving efficiency, innovation, and competitive advantage across diverse sectors. From healthcare to finance, manufacturing to customer service, AI applications are permeating every facet of the business landscape. The current state of the AI industry reflects not just a technological evolution but a fundamental shift in how businesses operate.

3. What are the area of application of AI Technology?

Manufacturers can benefit from AI implementations in several ways. Here are 10 examples of AI use cases in manufacturing that business leaders should explore now and consider in the future.

1. Cobots work with humans
Collaborative robots -- also called cobots -- frequently work alongside human workers, functioning as an extra set of hands.

While autonomous robots are programmed to repeatedly perform one specific task, cobots are capable of learning various tasks. They also can detect and avoid obstacles, and this agility and spatial awareness enables them to work alongside -- and with -- human workers.

Manufacturers typically direct cobots to work on tasks that require heavy lifting or on factory assembly lines. For example, cobots working in automotive factories can lift heavy car parts and hold them in place while human workers secure them. Cobots are also able to locate and retrieve items in large warehouses.

2. RPA tackles tedious tasks
While manufacturing companies use cobots on the front lines of production, robotic process automation (RPA) software is more useful in the back office. RPA software is capable of handling high-volume or repetitious tasks, transferring data across systems, queries, calculations and record maintenance.

RPA software automates functions such as order processing so that people don't need to enter data manually, and in turn, don't need to spend time searching for inputting mistakes. In this way, RPA has the potential to save time and labor.

RPA is also known for being able to handle server issues and downtime. In the event of these types of complications, RPA can reboot and reconfigure servers, ultimately leading to lower IT operational costs.

Components of AIManufacturers are increasingly adding AI components into their processes to boost efficiency while lowering costs.
3. Digital twins help boost performance
Companies can use digital twins to better understand the inner workings of complicated machinery.

A digital twin is a virtual model of a physical object that receives information about its physical counterpart through the latter's smart sensors. Using AI and other technologies, the digital twin helps deliver deeper understanding about the object. Companies can monitor an object throughout its lifecycle and get critical notifications, such as alerts for inspection and maintenance.

As an example, sensors attached to an airplane engine will transmit data to that engine's digital twin every time the plane takes off or lands, providing the airline and manufacturer with critical information about the engine's performance. An airline can use this information to conduct simulations and anticipate issues.

4. Predictive maintenance improves safety, lowers costs
Manufacturing plants, railroads and other heavy equipment users are increasingly turning to AI-based predictive maintenance (PdM) to anticipate servicing needs.

If equipment isn't maintained in a timely manner, companies risk losing valuable time and money. On the one hand, they waste money and resources if they perform machine maintenance too early. On the other, waiting too long can cause the machine extensive wear and tear. The latter can also expose workers to safety hazards.

PdM systems can also help companies predict what replacement parts will be needed and when.

5. Lights-out factories save money
An AI in manufacturing use case that's still rare but which has some potential is the lights-out factory. Using AI, robots and other next-generation technologies, a lights-out factory operates on an entirely robotic workforce and is run with minimal human interaction.

Manufacturers can potentially save money with lights-out factories because robotic workers don't have the same needs as their human counterparts. For example, a factory full of robotic workers doesn't require lighting and other environmental controls, such as air conditioning and heating. Manufacturers can economize by adjusting these services.

Robotic workers can operate 24/7 without succumbing to fatigue or illness and have the potential to produce more products than their human counterparts, with potentially fewer mistakes.

6. Machine learning algorithms predict demand
AI systems that use machine learning algorithms can detect buying patterns in human behavior and give insight to manufacturers.

For example, certain machine learning algorithms detect buying patterns that trigger manufacturers to ramp up production on a given item. This ability to predict buying behavior helps ensure that manufacturers are producing high-demand inventory before the stores need it.

7. Inventory management prevents bottlenecks

Some manufacturing companies are relying on AI systems to better manage their inventory needs.

AI systems can keep track of supplies and send alerts when they need to be replenished. Manufacturers can even program AI to identify industry supply chain bottlenecks.

For example, a pharmaceutical company might use an ingredient that has a short shelf life. AI systems can predict whether that ingredient will arrive on time or, if it's running late, how the delay will affect production.

8. AI boosts supply chain management

One strong AI use case in manufacturing is supply chain management. Large manufacturers typically have supply chains with millions of orders, purchases, materials or ingredients to process. Handling these processes manually is a significant drain on people's time and resources, and more companies have begun augmenting their supply chain processes with AI.

For example, a car manufacturer might receive nuts and bolts from two separate suppliers. If one supplier accidentally delivers a faulty batch of nuts and bolts, the car manufacturer will need to know which vehicles were made with those specific nuts and bolts. An AI system can help track which vehicles were made with defective hardware, making it easier for manufacturers to recall them from the dealerships.

9. AI systems detect errors

Manufacturers can use automated visual inspection tools to search for defects on production lines. Visual inspection equipment -- such as machine vision cameras -- is able to detect faults in real time, often more quickly and accurately than the human eye.

For example, visual inspection cameras can easily find a flaw in a small, complex item -- for example, a cellphone. The attached AI system can alert human workers of the flaw before the item winds up in the hands of an unhappy consumer.

10. AI systems help speed product development

Some manufacturers are turning to AI systems to assist in faster product development, as is the case with drug makers.

AI can analyze data from experimentation or manufacturing processes. Manufacturers can use knowledge gained from 

4. What are the steps of implementing AI Technology in manufacturing?

The first step 

involves instilling in AI the skills and techniques of existing workers. This approach not only facilitates AI’s advancement but also encourages its continuous development. Over time, it will be capable of performing various tasks autonomously, without requiring ongoing human training. When this state is reached, we will have genuinely crossed the threshold into the factory of the future.

The next stage 

entails resorting to crowdsourcing. By doing so, you can gather information or data that can be analyzed by AI. It can process this data within seconds and compare it to other information in its database. You will obtain a “hive mind” that enables AI to acquire collective knowledge and even understand what everyone generally knows.

Lastly, using unsupervised learning, 

AI can become autonomous. This means it can learn in a self-taught manner without explicit directives. But how does it go about learning? It would employ reinforcement learning. 

This method is more efficient for guiding an AI towards the desired results, but in an indirect manner. In this case, the machine maintains high-level control.

5. What are the benefits of AI in Manufacturing?
The superior benefits of applying AI in manufacturing are as follows:

1. Increased productivity among engineers

AI simplifies calculations and coding to remove the burden of the most challenging mathematical problems. It performs these functions automatically or bundles them up into user-friendly, sometimes no-code tools that engineers with varying degrees of experience can leverage to accelerate their workflow.

In fact, AI application increases employee productivity across the board by providing critical insights and automating repetitive processes. Because of AI automation, employees can spend less time on mundane work and double down on the more creative elements of their job, increasing their job satisfaction and empowering them to achieve their potential.

2. A more efficient and innovative design process (generative design)

AI drives software that can independently deliver production-level designs. It’s a game-changer. It does so based on a company’s existing and historical product catalog as well as goals and parameters (spatial, materials, costs, etc.) inputted by a designer or engineer. In a process known as generative design, the software creates multiple permutations for the operator to choose from and learns from each iteration to improve its future performance.

3. An enhanced customer experience 

In many industries, it’s hard to differentiate on product (multiple manufacturers are making more or less the same things) or price (margins are already razor-thin with escalating costs and global competition.) The next logical step is to differentiate by providing a superior customer experience.

AI can help improve CX at multiple points along the customer journey. Here are two examples:

Sales:

AI can help improve sales rep performance in multiple ways. A couple of examples: It can guide reps through the sales process to ensure even low-performers and new hires provide outstanding service. And it can give reps intelligent product and pricing recommendations in real-time to maximize margins and customer satisfaction. 

Increasingly, however, AI isn’t being used to improve sales rep performance but replace reps altogether. With an AI algorithm integrated into your website, buyers can configure and buy even the most complex, configurable products without human interaction. Not only does this reduce costs for the seller, but it dramatically improves CX for most buyers who prefer self-serve over human interaction.

Shipping and Delivery:

There’s no better way to get customers bent out of shape than to promise a specific delivery or lead time and miss the mark. The downstream financial consequences can be severe. 

Manufacturing companies usually accept that mistakes are inevitable with orders coming in all the time, multiple logistics companies involved, outdated IT systems, and inventory scattered across numerous locations. 

Manufacturers leveraging AI can calculate with near-100% certainty when orders can be shipped and when they will arrive at their customers’ warehouses. They can also use AI to keep customers informed along the way, meeting and exceeding expectations.

4. Better inventory management and demand forecasting

Most manufacturers have experienced the pain of being over- or under-stocked at crucial moments, leaving money on the table and/or indirectly pushing customers into the arms of competitors. Inventory management has so many moving parts (shifting demand, omnichannel sales, material availability, production capacity, etc.) that humans can’t get right all the time. But, AI can.

AI’s near-limitless computational potential makes maintaining appropriate stock levels achievable. Manufacturers can use AI to forecast demand, dynamically shift stock levels between multiple locations, and manage inventory movement through a bafflingly complex global supply chain.

According to Mckinsey Digital, AI-powered forecasting reduces errors by up to 50% in supply chain networks. It reduces lost sales due to out-of-stocks by 65% and warehouse costs by 10 to 40%. The estimated impact of AI within the supply chain is between $1.2T and $2T in manufacturing and supply chain planning. That’s a huge deal.

5. Improved quality control

The accuracy, infallibility, and speed of AI compared with humans can make the quality control process cheaper and much faster than in the past. AI can pick up microscopic errors and irregularities that humans would miss, improving productivity and defect detection by 90%. 

Using AI in the manufacturing process often obviates the need for quality control. AI can either correct faults as it goes or (because it’s not fallible like human beings) create products that are essentially guaranteed to be error-free for better product quality.

6. Predictive maintenance

Predictive maintenance monitors the condition of manufacturing plant machinery and estimates when maintenance should be performed (hint: before faults occur). Predictive analytics reduces downtime, and routine maintenance costs, which is often carried out unnecessarily. 

AI and machine learning increase the effectiveness of predictive maintenance. The technology combines vast quantities of data captured from sensors in machinery (detecting heat, vibration, movement, noise, etc.), computer vision, and even external data like the weather and the health of other connected machines, leading to significant savings. 

According to the U.S. Department of Energy data, predictive maintenance can provide savings of 8% to 12% over preventive care and reduce downtime by 35% to 45%. Extending the life of machinery and limiting unwanted shut-downs has a positive environmental–as well as financial–impact.

7. 24/7 manufacturing operation

As a human being myself, I’m ashamed to say we’re not the best workers. We need regular maintenance, fuel, and downtime; even then, we can only operate for about 8 hours daily.

Conversely, AI can work round the clock performing tasks with a higher degree of accuracy. It doesn’t get tired or distracted, it doesn’t make mistakes or get injured, and it can work in conditions (such as in the dark or cold) that we humans would balk at. 

The ability to operate a factory at peak performance 24/7 without the need to pay human operators has a massive impact on a manufacturer’s bottom line. Meanwhile, reducing the workload that needs to be carried out by employees is an effective way to stave off the labor shortage.

8. Streamlined factory layouts

Determining the optimal factory layout is a skill that sounds relatively straightforward. In reality, however, designing the shop floor for maximum efficiency in the production process is incredibly complicated, with thousands of variables that must be considered. This is where AI steps in.

With the lifecycles of products constantly changing, factory floor layouts should be fluid too. Manufacturers can use an AI solution to identify inefficiencies in factory layout, remove bottlenecks, and improve throughput. Once the changes are in place, AI can provide managers with a real-time view of site traffic, enabling rapid experimentation with minimal disruption.

RIICO is an AI system used to simulate and optimize factory floor layouts in industries where the lifecycles of products are constantly changing. It’s a bit like Sims with a virtual factory floor and a drag-and-drop interface.

6. What are the disadvantages of AI in Manufacturing?

Cons of AI in Manufacturing

1. AI can be expensive

Adopting AI in the manufacturing sector can cut labour costs but the initial implementation of AI can be pretty costly, especially in startups and small companies. Initially, there will be ongoing maintenance costs as well as expenses to protect systems from cyberattacks as ensuring cybersecurity is also important.

2. You need skilful experts

AI is an evolving field, and therefore AI experts with the requisite skills are few. Since these toolset needs regular sophisticated programming, it’s essential to consider expert availability. And also, because they are in high demand, the cost of employing them is also high.

3. AI is open to vulnerabilities

AI is vulnerable to cyberattacks, and as AI becomes more sophisticated and widespread, cybercriminals will try to come up with new hacking methods. If there is even a small gap, it can disrupt the production line. In fact, a small breach can potentially shut down an entire manufacturing business. So one should always be up-to-date with security measures and be aware of the possible cyberattack, which would be costly.

7. How is the future of AI in manufacturing?

Artificial Intelligence (AI) has emerged as a transformative technology across various industries, and manufacturing is no exception. In recent years, AI has been making significant strides in revolutionising manufacturing processes, optimizing efficiency, and driving innovation.

By leveraging the power of data analysis, machine learning, and robotics, AI is reshaping traditional manufacturing methods and propelling the industry into a new era of intelligent automation.

AI has several applications in every manufacturing phase, from raw material procurement and production to product distribution. The key application of AI is predictive maintenance. By applying AI to manufacturing data, manufacturing enterprises can better predict and prevent machine failure. This, in turn, cuts down expensive downtime in manufacturing processes. AI in manufacturing has many other potential uses, such as improved demand forecasting, quality assurance, inspection, and warehouse automation.

AI is crucial to the concept of “Industry 4.0,” the trend toward greater automation in manufacturing factories, and the enormous generation and transmission of data. AI and ML are necessary to ensure that organizations can unlock the value in the vast amounts of data created by manufacturing machines. Applying AI to this data can lead to greater cost savings, safety improvements, supply-chain efficiencies, and other benefits.

8. Which AI tools are superior for manufacturing?
AI can be used to optimize manufacturing processes and to make those processes more flexible and reconfigurable. Current demand can determine factory floor layout and generate a process, which can also be done for future demand. Those models can then be used to compare and contrast them.
The name of some of the top AI tools for manufacturing are the following:

Predictive maintenance
Gemini
Bing AI
Grammarly
Notion AI
Jasper
Robotics
Synthesia
Ai systems detect errors
Fireflies
IBM
Inventory management
Neurala
Otter.ai
Salesforce
Gen Digital
ChatGPT
A i robots
Ai in warehouse mmanagement
AIVA
Crayon AI
DreamStudio

9. Top Strategies to implement AI in Manufacturing.
You must plan, check and retrospect before starting your AI Journey in the industry.
The top Strategies for implementation of AI are
1. Identify the top challenges you’re facing.
2. Thoroughly assess your data.
3. Develop a strategy to thoroughly understand your existing infrastructure and your organization's direction.
4. Consider how you will deploy your AI models — in the cloud, on-premises, or in an air-gapped environment.
5. Plan how to monitor and track the AI models’ success once they’re deployed. 
The lifecycle of AI models includes ensuring that they continue to perform as expected, and consistently addressing any issues that might arise after deployment.
6. Defining AI-first strategy
What does an “AI-first strategy” look like? AI is perceived as a core competitive resource and is put in front of other potential focuses as a strategic priority.
7. Select your platform and software. 

Summarily, What is AI Strategy?
It is 
  • Segment customers and products into groups that have similar behaviors and needs.
  • Predict customer purchases and churn risk.
  • Estimate the lifetime value of a customer or product.
  • Optimize manufacturing supply chains and perform predictive maintenance to increase uptime.
Think on, What are the Five Big Ideas in AI?
  • Big Idea #1 – Perception.
  • Big Idea #2 – Representation & Reasoning.
  • Big Idea #3 – Learning.
  • Big Idea #4 – Natural Interaction.
  • Big Idea #5 – Societal Impact.

10. Conclusions

AI is everywhere, disrupting every industry and opening unlimited possibilities. AI can turn questions into discovery, insights into action, and imagination into reality. Are you ready to use AI to deliver real advantages for your business? Confidently turn your AI strategy into successful projects. Unlock your data with simple pipelines that fuel your models.

Train and tune those models at scale using innovative software and powerful supercomputers to improve accuracy and speed results.

11. FAQs
Q. How does AI improve efficiency in manufacturing?
AI increases operational efficiency for manufacturers by minimizing, or completely removing, repetitive tasks. Employees can focus on high-value activities that require more resources, while AI reduces the time spent on low-value tasks to increase the speed of production and business output.

Q. How does AI improve visual inspection in manufacturing?

AI algorithms can be trained to see similar to like humans learn to see and identify objects. Unlike traditional quality control where feature-engineering was used to define defective units, AI algorithms learn to recognize them by being shown examples. Instead of identifying detailed and specific measurements and programming your system to make the pass/fail decision based on these measurements, the algorithm learns from examples, e.g. pictures or other physical measurements, and then determines whether a new product it is shown is good or defective.


References 



Prof. (Dr.) D. G. Mahto is a multifaceted professional, serving as a Director, Professor, Philanthropist, HEI Builder, prolific writer, international consultant, reviewer, and expert career advisor at "Career Education for Success—Discover, Apply, Succeed!" He has guided countless individuals, products, and brands in discovering their true potential through insightful content, purposeful concepts, and motivational writing. With a deep sense of leadership and a commitment to community upliftment, he inspires others with his success stories and takes on the challenge of driving technological transformation, redefining social impact, and fostering interpersonal relationships for a more meaningful, balanced life. Prof. Mahto is dedicated to bridging the gap between dreams and reality for young people, striving for qualitative change in their lives. To explore his articles, books, and international journal contributions, visit his blogs at Career Education Success Now, Career Edus, and Discover Knowledge Wisely. You can also reach out to him via email.

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