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IoT in manufacturing: The four stages of maturity

Suhas Sreedhar | April 3, 2017
Use cases in industries such as aeronautics and chemicals are a proving ground, and a roadmap to adoption is emerging

3. Customizable IoT solutions made possible by apps: When smartphones like the iPhone debuted, the hardware was impressive—touchscreens with integrated cameras and Internet capabilities held a lot of potential. But it wasn’t until app markets debuted that the real revolutionary effect of the smartphone was unlocked.

It’ll be similar with IoT. Hardware-wise, smart machinery with sensors and connectivity are impressive. But it will be the software that makes use of these features, and the vast data networks that underpin them, which truly unlock IoT potential in manufacturing.

Customized solutions will be the biggest boon. A manufacturer of smart phones and a manufacturer of laptops might share some industrial IoT equipment, but each manufacturing supply chain can be powered by a series of custom apps designed to optimize their specific processes.

The important element of this is that, just as smart phone apps exist on an established platform (iOS or android), custom IoT apps will exist on top of established data networks, which link disparate parts of the manufacturing supply chain. The advantage of this is that companies can create custom solutions without running into the problem of data silos and incompatibility between divisions and supply chain partners.

4. Advances in manufacturing automation: A lot of the initial focus on IoT is getting information out of equipment. The increased visibility provided by IoT means greater insight into processes, and better opportunities for efficiency and cost savings. But the most mature stage of IoT adoption will be a two-way flow of information.

Instead of just getting insights out of equipment, manufacturers will be able to push information back to them, changing settings, orders, operations, all securely and remotely. The linkage between information and control will allow the big data analytics and machine learning algorithms responsible for guaranteeing performance to adjust operations automatically based on real-time conditions.

IoT equipment will thus become the eyes, ears, and limbs of an intelligent manufacturing system that exists in the cloud of networks, big data, and machine learning. In the end, what we’ll have is complete feedback between real-time data, analytics, and control.

The progression toward these stages won’t necessarily be straightforward and linear. Different manufacturers will experiment with different aspects of this overall journey toward IoT maturity. But that end vision, of a fully automated loop running on real-world data, is the ultimate endgame of IoT in industry.

Here’s a scenario that showcases this feedback loop.

Based on certain data, a manufacturer notices that a piece of equipment is undergoing strain because it is producing one specific part at high volume. To avoid potential breakdown, the manufacturer decides to switch that part’s production to another piece of equipment, to ease the load.

 

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