You’re probably familiar with the phrase ‘the Internet of Things’, often shortened to just ‘IoT’. And the meaning of the phrase is tied to the digitization of non-computer products so that information is coming to the internet from increasingly non-traditional sources. From phones to cars to clothes to even manufacturing equipment. The Internet of Things is changing lives, but it’s also changing business.
Learn more about how the future of tech is changing even the highest-tech of manufacturing, resulting in streamlined, more efficient factories and lean businesses more aware of their markets, end users, inventory, and processes.
High Productivity Manufacturing
Imagine this: high-tech manufacturing systems loaded with complex sensors and computing skills anticipate the areas most likely to have faults or errors and adjust processes. The machines are governed by complex AI, learning increasingly-efficient way to achieve high results. Each mechanical process is recorded in some way, this can be done using sensors or through the use of advanced cameras. The latest Machine Vision Cameras are capable of recording at extremely high resolutions at a high frame rate, which means they can report data back into a deep learning system with extreme accuracy. Once the AI receives data in whichever form has been specified, it continues to draw conclusions and narrow the margins of error in manufacturing, improving consistency and thus efficiency also.
For most people, it sounds like science fiction. But for some high-tier manufacturing centers, it’s the present. Consider the high-tech processed used with semiconductor manufacturing by Fralock, an aerospace manufacturing and design company: many of the materials and products are so sensitive that human error is far more risky and more likely than computer-trained and governed processes.
This form of high-productivity manufacturing has already had a jumpstart internationally, in nations where skilled labor is prohibitively expensive, like Japan. More and more manufacturing centers relied on intelligent machines, which represented a large upfront investment but over the life of the manufacturing center were far cheaper and more efficient than human labor. These centers provided the jumping off point for manufacturing systems with artificial intelligence.
The Next Industrial Revolution
The potential capabilities of these new systems are so powerful that many field and industry leaders call it the fourth industrial revolution. The first three included the development of steam engines, conveyor-based line assemblies, and the tech revolution of the information age. With smart, high-tech manufacturing, production centers become a big step closer to fully-automated.
But that’s just the potential of all these new changes in sum. Getting factory managers and decision makers together to implement all the changes necessary to advance to this level will certainly be difficult and potentially cost-prohibitive. Creating significant value and cost reductions are also key to bring the future of high-tech manufacturing to the present, in addition to more efficient processes.
Not only will this require buying new machines capable of sensing, transmitting information, and receiving potentially complex commands from AI; it means hiring the individuals to write the software to collate the information which sensors provide, turning it into actionable details and comprehensive workflows.
It also means that this data must be connected to industry information, including market and company context. To contextualize any one facility’s information makes it easier to make good business decisions on the floor, reducing unnecessary inventory and naturally progressing to lean models. In an ideal world, this connection would be used to help manufacturing anticipate demand, producing only as much as needed and reducing both inventory storage costs and materials cost.
The Bottom Line
The future of high-tech manufacturing is lean, streamlined, and involves virtually no human effort. But there are more than a few barriers to this future, from the cost of machines, the complexity of managing these relationships, to the cost of the human labor required to write the software for specific plants, machines, and industries. Common sense isn’t enough: intense mathematics, statistics, and code-writing will all be necessary, and it’s unfortunate that those industries are all staffed well below demand, driving up the cost of these critical skills.
