May 16, 2020

The Importance of Quality Control in the Automotive Industry

quality control
Megan Ray Nichols
4 min
Quality control is important in nearly every industry. It ensures that each product leaving the factory is of the highest quality and the same as all th...

Quality control is important in nearly every industry. It ensures that each product leaving the factory is of the highest quality and the same as all those items that came before it. One industry where quality control is especially vital is the automotive industry. Why is quality control so important when making parts for cars and trucks, and how can plant managers improve quality control in their facilities?

The Importance of Quality Control

Why is quality control so important in automotive construction?

It keeps costs down, for starters. Investing in high-quality products might seem like an expensive move, but it can save you money in the long run. A product that's lacking in quality control, especially in the automotive industry, can trigger expensive recalls. Products that are dangerous can cause car accidents and be hazardous to drivers.

Quality control can also spot problems before the product is marketed to consumers, leading to better releases. It can ensure that these products meet industry standards — for some components, like the exhaust and emissions systems, meeting the right standards is essential.

VW learned this lesson firsthand in 2015 when it was discovered that the cars being sold in America were equipped with a defeat device that made the computer think it was within emissions standards. VW was slapped with massive fines in both Europe and the United States, and the company had to pay to repair each car.

Quality control in the automotive industry is essential because cars are inherently dangerous if they're not correctly constructed with good quality control.

Having the Right Tools

Automotive quality control is all about having the right tools for the job. These tools will vary a little bit depending on the exact product that's being produced, but there are several core tools that any automotive facility can utilize to improve their quality control plan. These tools include a failure mode analysis, a statistical process control and a measurement system analysis.

A failure mode analysis allows the facility to analyze the production process and predict possible failure points. Once these failure points have been found, countermeasures can be put in place to prevent them from happening during production.

Statistical control processes are used in quality control in nearly every industry. They utilize statistics to control and improve production processes. They'll need to be tweaked slightly to cater to the specific needs of the automotive industry.

Finally, a measurement system analysis isn't applied to a specific product, but to the equipment that's being used to measure and analyze the products before they become available for marketing. This step ensures that the measurements being taken are accurate, enabling the equipment to be used for quality control without too much variance between batches.

Selecting the right tools during production is also important for quality control. Having the correct grit of sandpaper for auto body work, for example, could mean the difference between a smooth finished product and a quarter panel with swirls on it that show up even through the paint.

Improving Automotive Quality Control

What can facility managers and foremen do to improve quality control in their own facilities? Start by creating a quality of culture. Everyone, from the lowest-experience factory worker to the highest manager, should be focused on creating a quality product. This dedication includes having an open door policy where workers are not afraid to bring production problems or quality concerns to the attention of those above them.

If you use suppliers, work closely with them to ensure that their quality control measures align with your own. That way, products that come from other facilities will meet your quality standard without requiring repair or modification, saving both time and money in the long run.

Adopt new technologies as they become available. One emerging trend is the use of augmented reality — digital constructs projected onto real-world locations — to improve quality control. In some cases, even manual quality control inspections can be improved through the addition of augmented reality. While this practice hasn't been widely adopted yet, it presents an interesting new option for companies looking to improve their quality control measures.

In addition, create comprehensive quality control checklists that can be easily followed. Keep each list with the right product throughout the production process to reduce the chance that a step will be missed or forgotten.

Quality control is an essential part of the automotive industry — one that you can't afford to overlook. No matter what steps you take to improve your quality control process, make sure that they're universally adopted throughout your facility — having a team or two clinging to old QC processes could cause countless problems in the long run. Take the time to improve these processes, and it will be easier for you to create products that are consistently high in quality and perfect for the ever-changing automotive industry.

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May 12, 2021

Gartner: Leaders Lack Skilled Smart Manufacturing Workers

2 min
57% of manufacturing leaders feel that their organisations lack the skilled workers needed to support smart manufacturing digitalisation

With organisations rapidly adopting industry 4.0 capabilities to increase productivity, efficiency, transparency, and quality as well as reduce cost, manufacturers “are under pressure to bring their workforce into the 21st century,” says Gartner.

While more connected factory workers are leveraging digital tools and data management techniques to improve decision accuracy, increase knowledge and lessen variability, 57% of manufacturing leaders feel that their organisations lack the skilled workers needed to support their smart manufacturing digitalisation plans.

“Our survey revealed that manufacturers are currently going through a difficult phase in their digitisation journey toward smart manufacturing,” said Simon Jacobson, Vice President analyst, Gartner Supply Chain practice.

“They accept that changing from a break-fix mentality and culture to a data-driven workforce is a must. However, intuition, efficiency and engagement cannot be sacrificed. New workers might be tech-savvy but lack access to best practices and know-how — and tenured workers might have the knowledge, but not the digital skills. A truly connected factory worker in a smart manufacturing environment needs both.”

Change Management

Surveying 439 respondents from North America, Western Europe and APAC, Gartner found that “organisational complexity, integration and process reengineering are the most prevalent challenges for executing smart manufacturing initiatives.” Combined they represent “the largest change management obstacle [for manufacturers],” adds Gartner.

“It’s interesting to see that leadership commitment is frequently cited as not being a challenge. Across all respondents, 83% agree that their leadership understands and accepts the need to invest in smart manufacturing. However, it does not reflect whether or not the majority of leaders understand the magnitude of change in front of them – regarding technology, as well as talent,” added Jacobson.

Technology and People

While the value and opportunities smart manufacturing can provide an organisation is being recognised, introducing technology alone isn’t enough. Gartner emphasises the importance of evolving factory workers alongside the technology, ensuring that they are on board in order for the change to be successful.

“The most immediate action is for organisations to realize that this is more than digitisation. It requires synchronising activities for capability building, capability enablement and empowering people. Taking a ‘how to improve a day in the life’ approach will increase engagement, continuous learning and ultimately foster a pull-based approach that will attract tenured workers. They are the best points of contact to identify the best starting points for automation and the required data and digital tools for better decision-making,” said Jacobson.

Long term, “it is important to establish a data-driven culture in manufacturing operations that is rooted in governance and training - without stifling employee creativity and ingenuity,” concluded Gartner.

Discover Gartner's Five Best Practices for Post COVID-19 Innovation' in manufacturing.

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