Use Cases of AI in Manufacturing

  • Artificial Intelligence (AI)

Artificial Intelligence is the ability of Digital Computer to perform task in way of Human Brain. Artificial Intelligence refers to simulation of human Intelligence in Machines which are programmed to think like human beings. The goal of AI includes Computer enhanced learning, Reasoning & Perception. AI is nowadays used in different industries from Finance to Health care. AI in manufacturing uses machine learning methodologies in wide applications which has been described below.


  • AI in Manufacturing

AI in manufacturing involves use of technology to automate complex tasks and previously unknown workflows in manufacturing processes. There are many applications of AI in manufacturing such as IOT, Machine learning etc. AI in manufacturing is the use of machine learning (ML) solutions to optimize manufacturing processes with improved data analysis and decision-making.

Adopting Artificial Intelligence in manufacturing Industries is growing from automobiles sector to health care. Many countries have started using AI practices in Manufacturing (for eg  European Countries 51%, Japan 30% and US with 28%).

The Trend of use of AI in Manufacturing has accelerated after Covid 19 Pandemic. Sectors where AI is widely used in Manufacturing are:

·       Maintenance 32 %

·       Quality 30%



Manufacturing data is very appropriate for analyzing AI/machine learning as it has various processes & workflows. In manufacturing analytical data makes easier for machines to analyze. Many variables impact the production process, and it is very hard for humans to analyze, machine learning models can analyze & forecast data of discrete variables in crucial situation. For e.g., Bernoulli Naïve Bayes

Due to COVID-19 pandemic there has been a rise in the interest of practioners in AI applications. As per below Graph, it is being observed that because of pandemic, industries have shifted their attention to artificial intelligence. The graph shows rising trend from year 2016 to 2022.


  •  AI in Manufacturing Examples – Game changing Transformations

The impact of AI in manufacturing is changing the Picture. French food manufacturer Danone Group has been using machine learning to improve its demand forecast accuracy. This has led to a bellow impact:

20% decrease in forecasting errors
30% decrease in lost sales
50% reduction in demand planners’ workload
Fanuc, a Japanese automation company, has been using robotic workers to operate its factories. The robots can produce essential components for CNCs and motors. Robots has automated all production floor machineries without a stop & facilitate continuous monitoring of all operations.


  • Why AI is Critical to the Future of the Manufacturing World

Every manufacturer aims to find new ways to save and make money, reduce risks, and improve                overall production efficiency. This is important for their survival and to ensure a profitable and sustainable future. The key lies with AI-based and ML-powered innovations.


AI and the Future of Manufacturing

  • Common Use Cases of AI in manufacturing

1. Predictive maintenance

In order to perform Predictive Maintenance Manufacturers, leverage AI technology to upload periodic Maintenance schedules & identify potential downtime of the Machines. In such case IOT helps in real time monitoring of the machine parameters using sensors so Predictive Analysis can be done on the machine parameters to predict machine maintenance.

2. Generative design

In Generative design, Machine learning algorithms are used to identify Engineer’s approach to design a particular component or Assembly. Every component has some parameters of design like Size, weight, material, strength etc. These parameters are entered in the design software & multiple design options are given out through machine learning solution. By using this technique, numerous design option for one product is rapidly produced by manufacturers.

3. Cost estimation of raw material

Unpredictability in the cost of raw materials has constantly been a risk factor for manufacturers. It is essential that businesses deal with the varying cost of raw materials in order to sustain in the competitive market. By taking into consideration the earlier trend, AI-powered software can forecast the cost of materials. For instance, fluctuation in the fuel and steel prices.

4. Robotics

Industrial robots are also known as manufacturing robots. They help to avoid repetitive tasks, eliminate or curb human error occurred due to negligence thus ensuring that human workers deviate their attention to other areas of productive operation. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding etc.

Industrial robots are not new and have been in existence in the manufacturing plants from late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Certain manufacturing robots are enabled with machine vision that assists the robot to attain accurate flexibility in complicated and haphazard environments.

5. Quality assurance

Quality assurance, the word itself explains the meaning. It means sustaining a required standard of quality in a service or product. Assembly lines are data-driven, interlinked, and independent networks.
Assembly lines depend upon a set of factors and procedures. Best possible end-products are produced by taking into consideration the guidelines provided by assembly lines. Majority of the faults are noticeable, so this helps the AI systems to identify the distinctions from the standard outputs by making use of machine vision technology. For instance, if an end-product fails to meet its expected standards then AI systems will provide a notification to users which will help them to amend and make necessary changes.

Source: Capgemini Research Institute analysis


6. Demand Forecasting

Machine learning solutions stimulates inventory planning activities since they are great at dealing with demand forecasting. AI solutions offers better results than traditional forecasting methods. For e.g., Parle Frooti is most demanded in summer while in winter the demand goes down. This data is analyzed by AI tools to give accurate results to avoid Out of stock conditions.


  • Benefits of AI in manufacturing

1. Safety

Manufacturing is the sector where major accident & injuries takes place. The safety of workers is prime important so that’s why we have different techniques like 5S, Six sigma etc. The major accidents happen in Hydraulic press shop. Unwanted disasters in high-risk jobs can be curbed with the help of robots.

2. Cost Reduction

Operation costs of manufacturers can be decreased with the help of AI technologies. Several applications are taken into consideration in order to reduce the cost.

·       Leveraging AI technologies improves organizations analytics capability. This leads to optimum use of resources, decreasing inventory costs and make better projections.

·       Cost reduction can be achieved by using robots to execute Manufacturing process. They also reduce cost of quality by maintaining high level of quality output.







Author Details

Palkar Rahul Deepak

I have brought about 8+ years of experience in Consulting of ERP Solutions & Manufacturing Industry. I have wide exposure to Manufacturing Industry & Supply Chain Management. I have worked on various Development & Enhancement Projects. Currently I am working on Enhancement Project for Emerson Measurement Solutions

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