4 Ways Artificial Intelligence Will Impact Manufacturing
Nearly everything that surrounds us today is the product of a mass production cycle on an assembly line. In the last two decades, the explosion of consumer culture has caught the biggest of manufacturers off guard.
Organizations must be prepared to not just address growing consumer demands but also make sure that it is achieved in a cost-effective and profitable manner.
But the big question is − how will global corporations strike the fine balance between production and quality control? How will they ensure that customer demands are met without compromising on the bottom line? The answer lies in Artificial Intelligence.
Let’s have a look at the 4 ways Artificial Intelligence will impact manufacturing.
1. Human-Machine Synchronization
Industrial robots have always played an important role on assembly lines. However, they have traditionally been limited to repetitive tasks involving heavy lifting and tedious work. Artificial Intelligence allows robots to learn a multitude of skills, and work alongside their human counterparts for maximum effectiveness. For companies that can’t afford to spend hundreds of thousands of dollars on an industrial robot, this would not only be cost effective but also an easy way to improve efficiency.
2. Predictive Maintenance
Service and maintenance has traditionally been a pain point for the manufacturing sector. Since industrial equipment is typically serviced on a fixed schedule irrespective of operating condition, it results in wasted labor and raises the risk undiagnosed equipment failures. With Artificial Intelligence, it is now possible to create virtual models of large-scale machinery and constantly monitor performance to diagnose or predict potential equipment failures.
3. Better Quality Control
Balancing mass production with quality control is a challenging task. The faster a manufacturer pushes a process, the more likely they’re prone to hit errors and reduce output quality. With Artificial Intelligence, brands can leverage cameras powered by computer vision algorithms to identify defects and detect root causes of failure immediately. This allows manufacturers to identify and resolve production failures by swiftly performing anomaly detection on hundreds of units in a few seconds.
4. Improved Inventory Management
Estimating consumer demand inaccurately is a risk that can result in massive losses for manufacturers. With Artificial Intelligence, manufacturers can combine businesses processes such as order management, procurement, and inventory management in a single convenient platform to optimize supply chains and ensure calculated production. Not only would this result in improved production efficiency but also help brands avoid the risk of stagnant inventory or lost sales.
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