How to successfully automate quality control
Performing quality control for an entire product run would be unrealistic with manual inspection. However, this level of quality control is needed if you want to prevent costly recalls, meet strict industry standards, or compete with other brands. Moving away from a quality control strategy that is based on sampling is possible if you successfully automate quality.
Find or create reference data sets
Automated quality systems work by comparing what they measure and observe to existing data sets. Indicators that are relevant to part or product quality need to be identified in order to determine what the quality system will look at.
Once these key quality indicators have been identified, your organization will need access to data sets the quality system will use as references. A data discovery process would help your organization get more visibility into existing data sets and identify the ones that can be used for quality control purposes.
If existing data sets can’t be used as references during the quality control process, new data sets will have to be created.
Define behaviors and quality thresholds
In order to automate quality, you need to automate responses when a flaw is found. Existing flaws should be cataloged and associated with an automated behavior. Acceptable thresholds for each spec quality control machines will measure or observe will have to be determined and probably updated in the future.
One of the most significant challenges your organization will encounter when automating quality consists in determining what the system should do when an unknown flaw is detected. Machines won’t be able to make decisions unless there is reference data available. This is one of the scenarios where human input will be needed.
Design a customized system
Your organization needs a customized system in order to successfully automate quality control. The ideal quality control system depends on what kind of parts or products are manufactured.
“Internet of things” technology could be leveraged to gather data throughout the value chain. Sensors could be added to make sure products are properly stored and transported.
A machine that controls material movements could be added at the end of the supply chain. Some flaws can’t be detected until a part has been manipulated or a product has performed a task. The most relevant movements or tasks will need to be determined, and data will have to be reviewed to keep developing better ways to manipulate products and parts to reveal defects.
A machine vision system could be another crucial element when you successfully automat quality control. These machines can detect surface flaws by comparing images with reference data.
A machine that measures parts and compares these measurements with acceptable thresholds would be needed for anything that requires precise specs.
It is possible to automate quality without eliminating the human element. Combining machines and human inspectors is an approach that makes sense – for instance, if humans would be able to perfect better visual controls or if there is a need to have people present on-site to make decisions when new defects are found.
Redefine the role of quality control engineers
Switching to an automated quality control system makes data available across your entire organization. This data is going to change the way quality control engineers work.
Relying on automation eliminates the need for specialized training and makes quality control more accessible to employees from different processes. It increases visibility and allows decision-makers to react in real time when a new issue is found.
Quality control engineers have to take on a crucial role in successfully automating quality. 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, or even product design.
It is possible to successfully automate quality 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. Your organization will get the most out of its new automated system by redefining the relationship of each process with quality since a centralized quality control system delivers visibility and valuable insights that can benefit different processes.
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