Why automakers need smart manufacturing operations

Why automakers need smart manufacturing operations

Intelligent manufacturing solutions enable companies to react faster to problems in a production line or plant and to changing market conditions.

The automotive industry is undergoing rapid changes that are forcing manufacturers to accelerate their adoption of the Industrial Internet of Things (IIoT), big data analytics, artificial intelligence, digital twins and more. These technologies need to be integrated to move towards a smart manufacturing strategy for industrial manufacturing. Such an approach enables automakers to make data-driven decisions that can optimize their operations, reduce costs, and improve product quality.

Achieving these goals requires manufacturers to leverage Industry 4.0 and the digitization of the manufacturing process to develop smarter, more connected manufacturing systems.

See also: Overcome automotive market uncertainty with digital twins

Factors driving the need for smart manufacturing

There are many factors driving the need for smart manufacturing. One of the most important is that automakers, like all manufacturers, are under pressure to reduce production costs while maintaining product quality. And another factor is that consumers want highly customized and customized products and fast delivery of those products. And, of course, automakers must have the ability to react quickly to changes in the marketplace and supply chain.

But there is more going on in the industry. There are changes in automobiles themselves that are making smart manufacturing a necessity. They include:

More sophisticated electronics and software: Today’s cars are devices with many sensors and processors. They also include advanced driver assistance systems (ADAS) and infotainment software. These elements introduce new potential problems.

In recent years, the chip shortage has had a major impact. In 2021, the auto industry lost more than $200 billion to chip shortages. Eleven million fewer vehicles were produced. The production facilities remained idle. In 2022, many automakers have decided to ship some of their most popular models without all the chips they were designed to include.

On the software side of the equation, many see software as providing the differentiating capabilities to provide a competitive advantage, but also create development challenges. Manufacturers must integrate independent software elements into a complete platform. Solving the challenges associated with these complex systems has delayed production and the introduction of new models. And bugs in software systems have led to class action lawsuits.

Embrace of Electric Vehicles (EV): Starting at the design stage, changes need to be made. For example, good aerodynamic properties help a gas engine car achieve better fuel economy. Electric vehicles must be optimized for maximum range. There are similarities, but the design process is different.

Electric vehicles require new supply chains. The battery systems alone are unique to these new cars. And many electric vehicles use structural elements made of different composite materials than traditional cars.

And finally, electric vehicles require new production systems to assemble, build and test cars.

Stricter regulatory and sustainability requirements: Governments around the world are enacting new pollution control regulations aimed at reducing emissions from cars to help curb the impact of climate change. In addition to calling for more fuel-efficient technology, it is also driving the need to move towards selling more electric vehicles.

Additionally, new requirements are emerging to use more sustainable and lightweight materials in cars themselves. And make the end-to-end manufacturing process more sustainable. This means running plants more energy-efficiently and considering sustainability issues along the entire supply chain.

See also: Digital Twins and IT/OT Convergence Drive the Industrial Internet

How smart car manufacturing can help

Enter smart manufacturing. Technologies such as AI and Industrial IoT can be used to analyze the production and operation of work cells, assembly lines and entire plants. The application of these technologies can be used to quickly identify and resolve problems.

Many manufacturers are turning to digital twins and virtualization. Specifically, there is growing use of virtualization and digital twins of work cells, production lines and entire plants. These virtual representations can help an automaker identify production issues and streamline operations before launching a new product. They can also be used for virtual commissioning.

Such solutions allow manufacturers to react more quickly to problems in a production line or plant and to changing market conditions. Also, with such technology, automakers run what-if simulations to see how making a change here or there affects production. The purpose of such efforts is to identify the best configuration to establish a flawless launch.

An important side benefit of smart manufacturing is that the results are repeatable. Once a manufacturer optimizes a work cell, assembly line or entire plant, processes and best practices can be captured and replicated. Thus, a manufacturer can duplicate or relocate optimized production lines anywhere in the world.

Where is all this leading? In addition to providing manufacturing benefits, data generated on an assembly line and within a factory floor can be used for other purposes.

Manufacturers are connecting their operating technology systems with their business systems to optimize parts inventory, implement predictive maintenance, and more. Additionally, some are leveraging a continuous communication loop in which operational data is fed back to a digital twin to enable production lines to self-manage to prevent errors, minimize downtime, and produce quality parts first time at launch and throughout the product lifecycle.

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