5 Ways AI and IoT Are Affecting Manufacturing

A McKinsey & Company study surmised that nearly half of work activities – from retail and food service to teachers and health workers – could be automated today through technology that already exists. The factory environment is a prime example of where technology has enabled increased output and will continue to do so. As reported in an online article in The Manufacturer, the number of factory jobs in the U.S. has declined 60% in the past sixty years, with jobs morphing into new, complementary careers coming onto the scene.

Although the potential benefits of the IoT (Internet of Things) have been talked about for years, the technology is still being refined. As that takes place, jobs for people will include designing, building and programming the machines that use IoT. In fact, the demand for robotics specialists is expected to double in the foreseeable future. In addition, IT jobs like developers and data scrapers, who extract data and prepare it to be utilized, will be needed to establish the framework for IoT and artificial intelligence (AI) machine learning to operate within.

Following are areas of manufacturing where IoT and AI are already working their magic:

1. Machine Vision

Machine vision is one of the most accessible and cost-effective forms of AI plus IoT that manufacturers can employ today, including:

• Spotting machine defects, handling quality control and tracking production.
• Identifying health risks and safety issues.
• Validating employee work time using HD cameras and punch-in systems.
• Blocking access or stopping production when dangerous situations are detected.
• Tracking parts and materials using barcodes to automate inventory.

2. Cost Optimization

Using predictive analysis, AI can calculate the raw materials needed based on sales predictions. Sensors in the factory can track the manufacturing process to find, solve and even prevent production bottlenecks. Once configured, machines can work independently and around the clock, reducing the need for the majority of human input.

3. Predictive Maintenance

Machine learning can be used to gather historical and live data to analyze operations and predict when and how parts may fail. The factory’s repair team can then schedule preventive maintenance instead of reacting to rush situations and extended downtime when machines fail unexpectedly. Another McKinsey & Company report indicated that when factories employ sensors and AI for predictive maintenance, they reap the benefits of reduced maintenance costs (up to 25%) and increased production line availability (up to 15%).

4. Production Optimization

Machine learning algorithms can interpret data originating from sensors and operators to calculate changes in processes that will improve production. AI can actually provide feedback to operators in real-time to improve production on-the-fly. Although they require a large investment, industrial robots outperform people physically while working with them to optimize production.

5. Cobots

For a smaller investment than industrial robots, factories can implement cobots, or collaborative robots, designed for safe, direct interaction with humans in a shared space or in close proximity. Cobots can provide substitute labor for dangerous tasks and increase production speed and endurance. They can also be supervised and reprogrammed, making them more flexible and adaptable than the larger industrial models.

While combining AI with smart devices that use IoT will result in fewer decisions that require human intervention, more people will be needed to perform maintenance, supervision and scheduling of the tech that’s in use. Jobs will arise that we haven’t even conceived of yet. As AI and IoT innovations continue to evolve for use in factories, we will see efficiency, safety and production increases along with a shift in the type of careers available to people.

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