Imagine a factory floor where every part, process, and product is continuously monitored and optimized in real time. Leveraging IoT technology for more precise and consistent quality control is the key to this manufacturing paradise.
In this article
You'll discover how the Internet of Things enables the shift from reactive to proactive quality control (QC) processes. We'll go over:
In manufacturing, quality control is a set of defined processes and practices that ensure your products meet the required standards. From detecting product anomalies and adjusting environmental conditions to asset tracking and even facility management, quality control gives you insight into what’s going on during production.
Another term that goes hand in hand with QC is Quality Assurance. And while they both aim at the same thing, they're fundamentally different:
Most equipment issues happen slowly and in several phases. Therefore, one of the most effective ways to manage quality is real-time monitoring and control of production. If the insulation on one of the factory's machines fails, the machine will start heating up. But it will take hours, even days, before the machine fails completely, and if you regularly check the machine's temperature, you can fix the issue and prevent the worst from happening.
The main benefits of quality control:
Let's see how to achieve all of this by implementing modern technology.
Historically, quality control has been done in person and manually—workers on a factory floor pick a random finished product to check its dimensions or any other specifications. Today, the Internet of Things allows for automated real-time monitoring and data collection throughout production, resulting in faster and cheaper processes with fewer mistakes.
Unfortunately, most manufacturers have to overcome several hurdles when implementing IoT technology. It starts with the fact that much factory equipment predates modern technology and ends with questions like: "Is it worth the investment?" Let's tackle the three biggest challenges you'll face.
IoT devices eliminate the need to replace old machinery. Rather than investing in entirely new equipment, just add the IoT sensors and modules to the ones you already have, and voila, the machines are now connected to the network. Then, the data immediately starts streaming into industrial IoT platforms like Spotflow.
Once the machines are connected, you need to understand the data and ensure you can use it in your systems. That's where Spotflow shines because it makes integration with any system a breeze. Our Device SDK supports multiple languages for embedded software and the platform supports a range of data storages and data queues.
Moreover, the whole platform is data-agnostic, so you can work with any data format, such as binary, unstructured, or structured. Whatever information you process, from telemetry to videos or logs, just make sure your IoT platform can handle it.
Moneywise, the best approach is to start small and scale up. That's why we recommend kicking off with an out-of-the-box IIoT platform rather than developing a tailor-made solution. And you'll see the benefits as soon as you start getting insights into your data. Here’s an example.
After implementing Spotflow, you start collecting basic data, such as scrap_rate per machine. The data will show you that machine A produces 10% more scrap than the average machine of its type. Before you started collecting data, you couldn’t average the scrap rate. But now you know, and you can ask other important questions, like:
Even this simple metric can drive significant changes because you will start analyzing the data, identifying trends and deviations, and recognizing areas for improvement. All of this quickly leads to monetary savings.
While some operations need devices to monitor processes in real time, others implement tools for remote control or automated visual inspection. However, most quality control processes in manufacturing will rely on a mixture of tools from the following groups.
IoT sensors are responsible for continuously monitoring any number of parameters, from temperature and pressure to fill levels and scrap rate. Moreover, continuous data collection in real time makes predictive maintenance possible. And we've already seen how even a single measured parameter can dramatically improve performance while lowering costs.
Cameras, in connection with machine vision systems, can monitor the accuracy and consistency of individual parts and the final product while detecting and notifying managers of any defects. As we mentioned, Spotflow is data agnostic, so it's easy to incorporate a video feed into your data streams. Also, our Device SDK transfers big data (think video files) to the cloud securely and reliably without the need to code anything extra.
The industrial IoT platform analyzes the incoming data almost instantly. You can set alerts that notify selected groups when a monitored value is outside a given threshold—for example, when a scrap rate is above the average/median value of all machines within a specific time window. This means that people are notified as soon as a problem is detected, reducing the delay for remediation action.
Sometimes, the location and environmental conditions do not allow for a stable connection. That is why we developed the Spotflow Device SDK to prioritize critical data to be delivered to the cloud first while deprioritizing other data. This is handy in applications with low or fluctuating data upload speeds. The Device SDK also ensures that no information is lost, caching the data even when the connection is down for an extended period.
From production to the transport of physical goods, quality control is essential for smart manufacturing. Implementing automated QC processes is the starting point for this transformation, leading to higher-quality products, lower costs, and increased customer satisfaction.
The future of manufacturing lies in successfully integrating IoT into all aspects of operations. If you'd like to gain a competitive advantage in your industry, contact us today, and we'll get you started.
