The digital age has given customers access to an increasing number of products and services. This has provided opportunities for competitors of all sizes to penetrate markets with relative ease and quickly capture a sizable portion of the market. One strategy for maintaining your competitive advantage in a global and highly competitive world is by ensuring high-quality products or services on a consistent basis. However, performing quality control for an entire product run is unrealistic with manual inspection, as the more products to inspect, the greater the chances of human errors to occur. With this in mind, automating your quality control processes will provide the efficiency and accuracy needed to ensure that you’re providing the highest quality product or service at a competitive price point. Below, you’ll find the necessary steps for the successful implementation of automated quality control.
Find or Create Reference Datasets
Every system needs an identified “current state” before further observation, measurement, and monitoring can take place. Automated quality systems operate in the exact same way, as they require an initial data set that contains relevant quality indicators that represent the current state of quality measurement. These are industry dependent. So, to use manufacturing as an example, production throughput, yield, customer returns, supplier material usability, percentage of defective products, and schedule realization are possible quality data points for an automated quality control system. Your particular quality control data will be unique to your enterprise and may stem from several different departments, e.g., accounting, logistics, production, etc. Once you’ve identified your key quality indicators, you’ll need to aggregate that data into a form that your automated system can access and use as a reference.
Define Behaviors and Quality Thresholds
In order to automate quality, you need to determine acceptable quality thresholds and automate responses when a flaw is found. Existing flaws should be cataloged and mapped to an automated behavior. Such behaviors can range from simply alerting management through email, SMS, or other/multiple communication channels to triggering a machine shut down. When automating quality control, one of the most significant challenges your organization will encounter is pinpointing exactly what the system should do when an unknown flaw is detected. Since your machines won’t be able to make decisions unless there is reference data available, this is one of the scenarios where human input will still be needed.
Design a Customized Automated Quality Control System
Customization is a must for securing a robust automated quality control system. It cannot be overemphasized that the ideal quality control system depends on the particulars of your enterprise. Even if both you and your competitors are manufacturing widgets, your business logic and approach to production will differ. This is where the Industrial Internet of Things (IIoT) can be leveraged to gather data throughout the value chain. Sensors and actuators are being used in numerous manufacturing processes to make sure that products not only meet the appropriate quality thresholds but are also properly stored and transported. These IIoT components collect mass amounts of data that can be fed into a service or analytics layer, which will then automatically analyze the data in comparison to your previously identified thresholds. For example, a machine vision system can detect surface flaws by comparing images with reference data. Vibratory and heat sensors can relay real-time data to an engineer for predictive maintenance monitoring. Keep in mind, it is possible to automate quality without eliminating the human element, for example, if there is a need to have people present on-site to make decisions when new defects are found.
Redefine the Role of Your Quality Control Engineer
Switching to an automated quality control system makes data available across your entire organization and will change the way quality control engineers function. Relying on automation reduces the need for specialized training and makes quality control more accessible to a wider array of employees. Furthermore, the data gained from an automated quality control inspection system helps decision-makers react quickly when a potential problem is discovered. Your quality control engineers play a crucial role in successful automation. They will need to look for ways to optimize this system, such as identifying new indicators that could be measured and controlled, refining acceptance thresholds, and using the data harvested by the quality control system to improve processes, such as manufacturing, storage, transportation, and product design.
Automated Enterprise Quality Management Software
Quality control automation is, indeed, a necessity for today’s organizations. Quality control automation can be achieved once you have determined what the quality control system needs to measure, which data sets it will use as a reference, and which inspection techniques will be used to measure quality across your unique value chain. While refinements will be needed over time, your business will be well on its way to delivering consistent, high-quality products to your customers and maintaining a competitive edge in a worldwide marketplace. Need an automated enterprise quality management software (EQMS)? IQS offers an automated EQMS solution for your quality management needs. Contact IQS today to request a demo or learn more about your enterprise quality management solutions.