Lean manufacturing is founded on five key ideas that have been driving productivity and quality improvements for more than a century:

  • Determine the worth of a product to customers.
  • Map out its value chain and eliminate any processes that aren’t necessary.
  • Create a flow that allows it to continuously continue through the remaining steps.
  • Establish a production pull system.
  • Make every effort to improve the procedure.

Companies have become world leaders as a result of these principles. While production facilities have changed throughout the years, lean manufacturing principles remain the same. Indeed, improvements in artificial intelligence and factory robotics further automate the production process, offering new avenues for poor implementation.

Robotic Powered Factories & AI

For decades, robots have been utilized in industrial facilities, but their limitations and the requirement for human interaction have hindered the widespread adoption of more autonomous production lines. As long as the components do not change, a robotic arm in a car assembly line may be trained to combine them over and over. An automated production process may halt if a new component is introduced or a damaged one is discovered.

Consider how powerful that robotic arm could be if it could “see” the broken component and conclude that it is defective. Or that it could distinguish between different types of features and decide how to respond. These actions are now possible thanks to machine vision and deep reinforcement learning.

Maximizing throughput is one of the main concerns in factories and warehouses. Consider a collection of machines that can autonomously coordinate their actions to achieve a shared aim. These machines work in groups, and if they’re well-coordinated, they can even work together as a team. Autonomous guided vehicles (AGVs), cranes, and equipment processing products on a manufacturing floor are examples of machinery used.

AGVs are used to transport items from one location to another, such as moving an item from one processing stage to the next. It would help if you answered multiple difficulties at once to route a fleet of AGVs:

  • To which AGV should each trip be assigned?
  • ensuring that AGVs do not clash with one another or get in one other’s way
  • Assuring that the AGV fleet is fully charged

Autonomous guided vehicle teams have already been deployed in robotic powered factories and warehouses, with more to follow. Artificial intelligence techniques like deep reinforcement learning can operate as command centers, directing groups of robots to work together as a team and accomplish more than they could individually. Because emergent behavior does not occur at the level of an individual machine but only when they coordinate as a group, this type of teamwork is also known as emergent behavior.

Warehouse cranes may also transport goods from loading docks to racks. The goal is to store arriving items so that sending them out is as efficient as feasible. This is known as the put-away problem. Algorithms like deep reinforcement learning can help physical operations run more efficiently by ensuring that items are stored in places where placing them in an outward truck will require minor work.

Shipping, freight forwarding, oil and gas, mining, and automotive are just a few of the industries that will be impacted by the twin breakthroughs of robotics and AI.

Maintenance for scattered equipment is one variation of fleet routing. Reactive or scheduled maintenance is making way for AI-driven predictive maintenance, which uses historical data regarding failure frequency and real-time data from the equipment’s Internet of Things (IoT) sensors to decide the best time for machine and robotic repairs.

AI is redefining what is possible across the industrial floor, not just in terms of machine vision. Machine learning algorithms may assist robotics in learning new parts without human supervision.

These AI development targeted at robotics will be a game-changer for utilizing lean manufacturing approaches. AI-assisted robotics can accomplish more in fewer stages, aligning with lean manufacturing’s core process reduction and simplification principles. Robotics that can make judgments on their own, recognize new parts and processes, and accelerate the flow of goods will substantially cut the number of manufacturing steps and the time between ordering and shipping.

Conclusion

Beyond using more powerful AI robotics, AI is already impacting robotic powered factories in other ways. With AI branches like reinforcement learning, areas like resource cost management and price predictions are being improved, and this trend will only accelerate as these technologies become more generally available. The future factories may have a different appearance, but their metamorphosis into robotic-powered factories of the future will usher in a new era for lean production.

To know more about AI robotics, contact ONPASSIVE team.