Industrial Robotics Impact on Automotive Sector
Automation has shaped the modern economy like no other process. Automation, the application of science to mechanical processes, has revolutionized almost every other sector of our economy, from production to services to telecommunications. But perhaps the most ripe sector for impact is the automobile industry. It is the largest manufacturing sector in the U.S., employing millions of people. As a result of this high level of usage, it is susceptible to many threats from products obsolescence, product design changes, and worker resistance to automation.
One obvious area of automation risk is in the manufacturing process itself. Cars are built on the production line - a assembly line - and as such they are subject to mechanical breakdowns, which can be both minor and catastrophic. This is particularly true in the case of automotive industry components such as brake linings, which are often subjected to long hours of heat-tension and high temperatures, exposing them to extreme stresses and wear. In addition, excessive use of automated machinery, such as the automated welding process used in assembling cars, can lead to errors and downtime.
Automation is also inherently linked with another risk, that of machine downtime. In the case of car assembly line robots, a lot of mechanical parts may need to be repaired after a single repair or failure, and even a single minor issue can significantly delay production. A problem with one component, however, can affect all of the car's components and even cause a complete breakdown of the assembly line. As a result, this type of downtime can significantly reduce customer satisfaction - customers would rather have their cars than to wait for days for a part to be repaired and will consequently opt to look for a new vehicle. The effect on an automotive company of too many stoppages in production can be severe and can have a significant impact on its overall profitability.
Automation does have some shortcomings, however. One major shortcoming is that, as with any other form of computer software, industrial robots cannot think for themselves. They are still mechanical creatures that respond to instructions, but they cannot think for themselves. Automotive robotics experts are beginning to address this issue, but there are many factors that complicate this issue. For example, in the case of car assembly line robots, it may be difficult for drivers to determine when the robot has gone haywire and needs to be repaired before continuing work. In the case of automotive robotics, a single robot or a set of robots could possibly take over a task previously handled by multiple robots.
As a result, automation is the best option for tasks where humans can execute but require very specific expert knowledge. For example, car assembly is often the best example here. If you hire a group of non-automotive workers to assemble cars at your factory, you will be wasting a tremendous amount of time if the workers lack the specialized knowledge needed to perform the job properly. Likewise, if you install automated material handling equipment, you will be wasting a tremendous amount of time if the machinery is programmed wrong. Therefore, robots are often best used where specialized knowledge is necessary and repetitive tasks are present.
It should be noted, though, that although robotic automation is a great idea in many instances, the automotive industry should not forget about traditional manufacturing methods which have been a staple of human industry since the industrial revolution. Even when robotic automation is effective and reduces labor costs, it is not a substitute for manual labor in the manufacturing process. Far from it, since more time must be spent producing each product, it can actually cost the company more money. So, although the automotive industry may benefit from the concept of Robot integration, it would be wise for it to continue on its path toward increasing production and revenue while avoiding becoming obsolete by adopting other manufacturing methods.