AI in Manufacturing Industry: Use Cases and Examples

By leveraging AI algorithms, manufacturers can automate and optimize various stages of product development - design, prototyping and testing. Let’s explore some use cases.

We have been talking about AI since quite some time. How far have we reached? The goal is not just to improve efficiency and make the process better, smoother and faster but also to make it sustainable, secure, and free from potential side effects.

AI has a common mission - to improve productivity and to reduce the chances of errors (and prevent industrial hazards in manufacturing). AI was introduced into manufacturing to fasten the process, to prevent workplace hazards, to predict about any shortcomings or deficiencies, if any machinery needs attention;

Additionally, AI can help in automating tasks, reducing the costs, and improving the quality. Besides anomaly detection, introduction of artificial intelligence in manufacturing helps in predictive maintenance, and data analysis.

Impact of AI on the Manufacturing Industry

While Google search is waxed with terms pertaining to AI in manufacturing like: (1) Robotics, (2) Quality Control, (3) Predictive maintenance, (4) Automation, and (5) Supply Chain - Lehman gets very little chance to know what this actually means.

Undoubtedly AI trends have the secret sauce to speed up traditional, mundane tasks, and boost the (1) performance, (2) efficiency, and (3) reduce the chances of errors and potential faults, but they need employees working in the unit to up skill themselves to operated machines, employees will need to up skill themselves to survive, or AI will definitely take away their jobs.

According to Fortune Business Insights, the use of artificial intelligence in 2019 was close to $1.82 billion, which will rise up to $9.89 billion in 2027, at a CAGR of 24.2% (2020 - 2027).

Statistics that go in Favor of Smart Manufacturing Units

Market Drivers and Trends for increasing adoption of artificial intelligence in manufacturing units are:

(1) Increasing adoption of production and process optimization.

(2) High investment for AI-driven collaborative robots

(3) High impact of industry 4.0.

Segmentation

● By offering: (1) Hardware, (2) software, (3) services

● By technology: (1) Computer vision, (2) machine learning, (3) natural language processing, (4) context awareness

● By application: (1) Process control, (2) production planning, (3) logistics and inventory management, (4) quality management

● By industry: (1) Automotive, (2) medical devices, (3) semiconductor and electronics, (4) energy and power, (5) heavy metal and (6) machine manufacturing

This increasing adoption of AI in manufacturing sector pans across continents: (1) Asia Pacific, (2) North America, (3) Latin America, (4) Middle East and Africa.

Noteworthy Industry Developments

Rockwell Automation Inc. has launched their new AI module - Factory talk Analytics Logixi to enhance their industrial production. It is also capable of providing Accurate Predictive Analysis without the need of data scientists.

Schaeffler AG has strategically collaborated with Mitsubishi Electric Corporation to refine and uplift their E-factory solutions for all their future productions. They will also jointly focus on Industry 4.0 solutions to provide Smart Manufacturing in the Operational Activities.

Major players in this area are: (1) GE, (2) SAP, (3) IBM, (4) NVidia - inception program, (5) Amazon, (6) Mitsubishi Electric - Cooling and Heating, (7) Rockwell Automation, (8) Google, (9) Microsoft, (10) SEIMENS, and many more.

Tagging Artificial Intelligence into Manufacturing Units - Use Cases

While implementing AI into manufacturing setups bears an initial cost, this arrangement can help in reducing the overall cost in the long run. Best AI Apps cannot think but learn behavior and repeating patterns. It follows a repeating pattern, and makes calculations via algorithms to predict something in advance. This solution can help people within manufacturing setup to take important decisions.

More AI use cases in manufacturing units include:

● Reducing the amount of raw materials that are getting wasted, or which could potentially go waste.

● AI can also automate time-consuming tasks, reduce redundancy so that people can focus on more creative work.

● AI can optimize maintenance schedules by checking the status of the equipment and anticipate faults.

● It can also warn the workers about hazards on the shop floor.

● While streamlining the workflow and reducing human stress, AI systems can (1) monitor machine productivity, (2) track performance, and (3) detect defects.

● It can help reduce maintenance costs.

● AI can also detect inventory, by checking when something is about to go out of stock, it places new orders, identifying potential disruptions, and improving logistics efficiency.

● With AI, the production and manufacturing process can go 24*7, thereby producing more units, increasing revenue, with little human supervision.

● Generative design software based on artificial intelligence are able to create optimized product designs based on specified criteria like performance requirements and manufacturing constraints.

● AI robots are also being used in (1) material handling, (2) assembly and (3) inspection.

● Natural Language Processing (NLP) AI systems analyze customer feedback, process maintenance logs, and extract insights from unstructured data sources to enable better decision making and problem solving.

● AI-powered virtual assistants help streamline communication within manufacturing organizations by (1) offering information in real-time, (2) scheduling meetings, and (3) performing administrative tasks.

● AI is used to optimize energy consumption in manufacturing facilities by (1) analyzing energy usage patterns, (2) identifying areas for improvement, and (3) implementing energy-saving measures.

Challenges with AI in Manufacturing Industry

AI development companies too faces some challenges:

● Investment and ROI

● Data quality and management

● Skills and talent

● Resistance to change

● Security and privacy

● Ethical concerns

● Integration with legacy systems

Conclusive

These were just a few examples of how AI is transforming the manufacturing industry, driving efficiency, productivity, and innovation across various processes and operations.

AI and manufacturing are perfect fit because in manufacturing plants, units tend to produce many identical parts and products, which generates huge amounts of data, which in turn can be fed into the artificial intelligence algorithms, and thereby help them learn, improve, identify problems and process optimizations.

Currently many projects, and experiments are going on into the manufacturing sector - automotive sector, matrix production systems, where we do not have any more typical century old, linear production system, production line; the cars in this case are being put into a pod and the pod moves through the factory, in a way that is optimized for this specific product. If we look at the Cars in the high segment, each car is different from the next car. And therefore a linear production sequence doesn't make a lot of sense. This is not future science fiction. This is happening today already.

A perfect example of the use of artificial intelligence in manufacturing is robotics. Right now you need about two weeks of highly paid engineers, to optimize the robots so that they can really do what they need to do.

In the future, we'll have robots that do this by themselves. They will see how the welding points are done compare it to what they should be doing and then learn and improve. There will be perhaps one engineer to overlook all of this but the robots will learn by themselves using artificial intelligence.