There are various challenges associated with manufacturing industries that affect their overall capability and production. It can process inefficiency, production errors, costly maintenance of machines, and anything out of the blue. Most of these challenges arise due to human errors, manual processes, and unavailability of real-time insights, which results in lower production quality, escalated expenses, and missed opportunities. A significant issue that today’s manufacturing plants are facing is not able to detect faults in the production process. The traditional methods to identify faults do not give results in today’s highly complex assembly line or manufacturing chain. That’s why it is becoming essential to introduce AI to address these challenges.

Manufacturers can leverage the capabilities of AI to overcome obstacles such as detecting faults, reducing errors, and optimizing maintenance. AI can consume enormous amounts of data sets, analyze them, and give meaningful insights and patterns. It can provide you with the required tools by which you can make informed decisions, optimize resource allocation, and enhance inventory management.

According to Precedence Research, the global market for artificial intelligence in manufacturing is expected to reach USD 3.8 billion by 2022. It is expected to reach roughly USD 68.36 billion by 2032, with a phenomenal compound annual growth rate (CAGR) of 33.5% between 2023 and 2032.

As per a survey conducted by VentureBeat, nearly 26% of organizations are now leveraging generative AI to improve their decision-making processes. Furthermore, 66% of manufacturers implementing AI in their daily processes have found a growing dependence on this transformative technology.

In this article, we will delve into the importance of AI in the manufacturing industry while exploring its use cases that showcase its potential to drive operational excellence and propel the industry toward new horizons.

Top Use Cases of AI in Manufacturing

In the manufacturing landscape, the integration of AI has pushed its boundaries and brought new horizons of efficiency, precision, and innovation in the industry. Artificial Intelligence (AI) is used in several industrial business departments to redefine operations through streamlining procedures and optimizing resource allocation. Here are a few such manufacturing-related AI application cases:

1. Supply Chain Management:

The supply chain has a predominating role in the manufacturing industry, and AI is emerging as a game-changer in this field. Using AI solutions for manufacturing, you can improve the supply chain processes to a great level and improve efficiency, accuracy, and cost-effectiveness.

AI in the supply chain employs predictive analytics, which optimize inventory management, enhance demand forecasting, and streamline logistics. The machine learning algorithms present in AI can go through historical data to identify patterns and accurately predict demand fluctuations. An automobile parts manufacturer, for example, may estimate spare part demand using ML models, which enables them to manage inventory levels and cut costs.

2. Cobots:

Collaborative robots, aka Cobots, are imperative in the AI-driven manufacturing industry as they collaborate with human operators and boost productivity. In e-commerce, they are mainly used at fulfillment centers to help pick and pack. These cobots collaborate with human workers, navigating complex environments and detecting items using artificial intelligence algorithms.

For example, Amazon’s cobots help expedite order fulfillment, streamline logistics, and improve operations. They can perform complex tasks precisely and adaptably. Cobots can manage complex assembly operations and quality control inspections while collaborating smoothly with human workers. This proactive strategy eliminates downtime, lowers maintenance costs, and ensures peak equipment performance.

3. Warehouse & Inventory Management:

AI is playing a revolutionary role in the warehouse management field of manufacturing. AI-powered manufacturing solutions have changed the way how warehouses operate, resulting in higher efficiency, accuracy, and cost savings.

Inventory management is the utmost use case for warehouses. AI can accurately predict demand patterns by analyzing historical sales data, current stock levels, and market trends. Thus, the warehouses can optimize their inventory levels and reduce storage and carrying costs while ensuring product availability.

4. Assembly Line Optimization:

In an assembly line, AI can be harnessed to improve production process accuracy, efficiency, and flexibility. It can leverage real-sensor data along with past performance metrics to improve workflow, reduce downtime, and enable predictive maintenance. It can also identify flow or anomalies in the products to ensure a high product quality.

Predicting any issue in the assembly line in advance can save a lot of money by preventing excessive wear and tear and informing you of the required maintenance. AI transforms assembly lines into data-driven, flexible environments through continuous learning and adaptation, ultimately increasing output, cutting costs, and maintaining high manufacturing standards.

Read More – How AI Helpful in Warehouse Management

5. Consumer Preferences Identification:

Your company’s research department can use AI to identify customers’ preferences. It will use vast consumer preference datasets such as reviews, social media comments, purchase history, etc. It will evaluate the sentiments, trends, and emerging preferences of your customers so that the manufacturers can make data-driven decisions. They will decide the design, features, and marketing strategies of the product accordingly. With this strategy, they would be able to meet evolving customer demands more effectively. It will also reduce product development cycles and ultimately improve customer satisfaction.

6. Performance Optimization:

In the manufacturing industry, performance optimization is a critical aspect. AI algorithms may find trends, detect abnormalities, and create data-driven predictions by evaluating historical data, real-time sensor data, and other relevant variables. This allows producers to optimize operations, reduce downtime, and increase overall equipment performance.

7. Quality Assurance:

AI can revolutionize traditional quality control as it can ensure a higher level of accuracy and consistency. AI allows manufacturers to use computer vision algorithms to examine photos or videos of products and components. These algorithms can detect faults, anomalies, and deviations from quality norms with unprecedented precision, far exceeding human capabilities.

AI in Manufacturing

8. Streamlined Paperwork:

One of the applications of AI is Robotic Process Automation (RPA), which is highly useful in bringing automation to paperwork. Using this technology, all your paperwork activities, like purchase orders, invoices, quality control reports, etc., will get streamlined. Doing these processes manually will take a lot of time, and there can be many errors as well, which delay the process.

The implementation of conversational AI in manufacturing industries will bring automation to these manual processes. There are intelligent bots in AI that can extract the data from the paperwork, classify the information, and enter it into appropriate systems.

9. AI-Driven Order Management:

AI in order management will bring a lot of benefits to the manufacturing industry. The examination of old data, consumer preferences, and market trends will streamline the entire order fulfilment process. As we mentioned above, machine learning algorithms will also help analyze demand.

When manual, repetitive tasks are automated, using data-driven insights, AI solutions can improve the order management system to a great level. It will speed up the decision-making and drive a responsive & customer-focused approach to order fulfilment.

10.  Connected Manufacturing Plants:

Connected industrial is an excellent example of how you can incorporate AI in different production processes to develop intelligent, networked ecosystems. AI can facilitate the development of a networked system in which there will be machine-to-machine communication, which will let you do quick modifications if there are any changes in demand.

In short, there will be less overhead costs and high operational efficiency with AI by connecting plants together. It will also boost productivity by data-driven, adaptive manufacturing systems that adjust quickly as per the circumstances.

11.  Managing Purchasing Price Variance:

The strategic application of artificial intelligence (AI) in managing purchasing price variance in manufacturing significantly improves cost management and procurement efficiency. Artificial intelligence is used in this use case to examine and forecast changes in the price of raw materials, which have an immediate effect on production profits. Artificial intelligence (AI) algorithms can accurately arrange necessary product parts and anticipate standard buy prices by assessing detailed resource features like pitch, diameter, material type, and finishing. They can also incorporate supplier-specific data like nation, brand, and performance. This projection creates a baseline for comparing pricing between various vendors by accounting for past purchase data and current market trends. As a result, manufacturers benefit from a consolidated procurement data management system that simplifies the supervision of parts purchased from many vendors.

12.  Wearable Technologies for a Safer Workplace:

You can develop a highly safe workplace environment for your employees by bringing AI with wearable technologies. These devices will allow the employees and workers to access essential instructions and information hands-free so that there will be minimal distractions, and they can focus on the task with enhanced situational awareness.

For instance, AI-based smart glasses can provide real-time data, safety alerts, and instructions directly on the worker’s vision. It will improve the speed of task completion and also reduce the likelihood of an accident.

Bonus Read – Top 50 AI Business Ideas for Startups to Try

13.  Additive Manufacturing:

The additive manufacturing is popular with the name of 3D printing. In 3D printing, three-dimensional projects are developed by adding material layer by layer as per the digital design of the object. In this method, an item is manufactured by sequentially spreading layers over each other until the entire project is formed. The introduction of AI in this field is bringing revolution to it. It is defining new ways of 3D printing in how objects are designed, produced, and optimized.

Basically, AI can increase the precision of 3D printing and allow the development of complex designed objects. Along with this, it will also facilitate the automation of 3D printing operations so that machines will self-adjust and make decisions in real time.

14.  Smart Energy Management:

Energy efficiency is significantly increased by integrating AI-driven smart lighting and HVAC control systems with energy management. This shows how artificial intelligence and industrial facilities work well together. In order to automatically modify lighting and climate control settings in real time, these AI systems intelligently assess data on consumption patterns, outside weather conditions, and occupancy levels. They guarantee efficient energy use this way, cutting down on waste and running expenses. This supports regulatory compliance and sustainability objectives in addition to significantly lowering the environmental impact of manufacturing activities.

15.  Yield Optimization and Batch Analysis:

In a variety of industries, including pharmaceuticals, chemicals, food processing, and electronics, artificial intelligence (AI) for yield optimization and batch analysis is essential for improving productivity and product quality. Artificial intelligence (AI) systems examine past production data, environmental parameters, and quality control measures to find trends and variables that affect the manufacturing yield and quality of each batch. They do this by utilizing machine learning algorithms and data analytics. With this in-depth analysis, producers can anticipate and address problems that may result in lower yields or batch failures, maximize production efficiency, and guarantee constant product quality.

AI in Manufacturing

16.  Copywriting:

Copywriting refers to creating attractive, interesting content that engages readers and persuades them to buy products and services. It effectively communicates the value that we are offering to the readers through our products or services. In copywriting, AI can help to create compelling content, product descriptions, email campaigns, and other forms of content by which the user interacts, knows about your brand, and takes relevant action.

17.  AI in CNC Machine:

Artificial intelligence (AI), which significantly boosts automation, productivity, and precision, is transforming CNC machining. AI in CNC machining helps achieve two main objectives: productivity and efficiency. In order to assist engineers and skilled operators in optimizing the machine or removing impediments that slow it down, artificial intelligence (AI) analyzes data collected during production.

18.  Process Optimization:

Machine learning and other AI approaches evaluate production data to find inefficiencies and improve manufacturing procedures. AI systems can recommend process changes by examining variables like material utilization, energy consumption, and product cycle times. This results in higher output, lower expenses, better use of resources, and improved operational efficiency all around. By identifying bottlenecks, cutting waste, and putting continuous improvement programs into place, manufacturers can increase productivity and attain better levels of efficiency.

19.  Defect Detection:

Effective defect detection systems are rare on assembly lines, which frequently rely on simple configurations that need to be manually programmed by qualified engineers. These devices generate numerous false alerts, necessitating laborious manual checks. Manufacturers can increase productivity and save time by reducing false alarms and using artificial intelligence and self-learning capabilities.

20.  Cybersecurity:

Manufacturers are the ones who are most affected by cyberattacks, according to research, as even a short assembly line outage can be expensive. The hazards will only get bigger when more and more Internet of Things devices are in use. Cyberattacks are especially likely to target smart manufacturing.

Production facility security and threat mitigation can be aided by AI-driven cybersecurity systems and risk detection techniques. Manufacturers can identify and quickly stop assaults on cloud services and IoT devices with surgical accuracy thanks to self-learning AI. Also, the system can notify the appropriate teams to take prompt action in order to stop additional harm.

How Octal Can Help You Deploy AI in Manufacturing?

In this article, we have gone through myriad use cases or applications of AI in manufacturing. It has a role in every aspect of manufacturing, whether it is managing inventory, supply chain, forecasting, defects, operations, or anything else. You can leverage the AI development services at Octal to maximize the benefits of AI in your manufacturing plant or company. At Octal, the best AI development company in India, we have expertise in deploying AI solutions with advanced technologies like OCR, RPA, NLP, ML, and others to meet client end goals. Let us know your requirements.

THE AUTHOR
Digital Marketing Head
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Kamal Kishor, a digital marketing enthusiast and seasoned writer, idolizes exploring the dynamic technology market. With a background in computer engineering, he combines technical expertise and creative storytelling in his tech blog.

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