Troubleshooting QC: Five Common Issues with Quality Control You Can’t Afford to Miss (and How to Fix Them)

By Kaitlin Lee

An important part of product development and manufacturing is quality control (QC). When done poorly, QC can lead to major headaches, and can also cost companies millions of dollars. Understanding the most common QC issues can help companies catch problems early and improve overall product reliability.

But firstly, what is QC? 

Quality control (QC) is a system that checks that manufactured products are created and maintained to a high standard. It involves testing different units and determining if they meet specifications for the final product. 

Without effective QC, creating a product can be costly, time-consuming, and even unsafe, depending on what companies are manufacturing. 

How might QC differ among different industries? 

QC systems differ depending on the industry, and there might be different processes and regulations. Here are some examples: 

• Food and drug manufacturing: ensures that products will not make consumers sick. 

• Automobile manufacturing: ensures that parts meet specific standards and tolerances.

• Electronics manufacturing: uses meters that measure the flow of electricity and stress-testing. 

What are five common issues that can come up in QC? 

1. Limited Product Knowledge among Workers:

   1. What it is: Most of the time, only C-suite executives and product developers are the only groups that really understand the product and how it works. Other workers’ interactions with the product come from minimal interaction with the parts or materials, which means that some workers may not know what the product even looks like, let alone know how it works. For certain projects, there may be confidentiality concerns, which means that not everyone can be granted access to the entirety of a process or project. However, for certain modules or parts of the process, that information can fully be given to other workers who may not be executives. 

   2. How to fix: Create an in-depth tutorial about your product, or about a certain module without breaking confidentiality. You can work with a technical communicator or a training specialist to create this course. Either way, it can simplify the product and make it easier for every worker to understand what it’s about. 

2. Lack of Proper Product Documentation

   1. What it is: Throughout the development process, a lot can change about the product, without any key documentation. A product manager may say certain things about a product, while engineers may push for other improvements – without documenting. Additionally, documentation can provide accurate reports, and help avoid mistakes in the future. Without proper documentation, products can have varying quality or manufacturing issues, and can also decrease trust for consumers.

   2. How to fix: To mitigate issues with documentation, make sure to write down every product detail and update throughout the product. This can include new updates, decisions, and issues that come up. This constant documentation can keep everyone on board with every update. 

3. Inconsistent or Incomplete Product Knowledge or Testing

   1. What it is: Many QC issues come from poor testing procedures or environments. Some of these include: not testing all components; testing outgoing products without determining that they meet specifications; sending out products without properly testing them; and failing to spot-check for errors. 

   2. How to fix it: Many of these issues can be solved with a simple quality audit. Additionally, while having a checklist seems like a no-brainer, too many don’t remember to make one, and instead rely on memory. Sharing a final checklist with the entire group can also help everyone’s individual work be aligned, and mitigates overlooking important details. Additionally, make sure that everyone’s checklists are aligned with each other. You can use a project management website also to help coordinate checklists, like Trello or Monday. 

4. Incorrect Interpretation of Test Results

   1. What it is: When testing your product, it’s important that you have a procedure and subject matter experts that can adequately assess the quality of the product. However, poor structure – i.e. issues with preparing, conducting, and concluding research – can lead to the incorrect interpretation of data. Additionally, if not done well, it can expose you to accidentally violating government rules and regulations. 

   2. How to fix it: Keep in mind which stages are most vulnerable for human error, and at each stage, remember to keep in mind which government regulations you need to align with. 

5. Difficulty managing suppliers 

   1. What it is: For all of your parts and materials, you’re most likely relying on multiple suppliers. However, quality can be greatly compromised if there is less transparency, and if you’re not aware what steps your suppliers and sub-suppliers are taking for QC. 

   2. How to fix it: You can always try to get in touch with all of your suppliers. Some sub-suppliers may be harder to reach.

How is machine learning changing the QC game? 

Machine learning is fundamentally transforming QC procedures across industries by creating smarter, faster, and more reliable processes. Often times, traditional QC methods can be time consuming and prone to human error. But here are some ways machine learning can address the challenges above with several key advancements. 

1. Limited Product Knowledge Among Workers

   1. How machine learning can help: Machine learning-powered systems can deliver personalized learning nodes or modules, based on an employee’s role, previous performance, and knowledge gaps. 

2. Lack of Proper Product Documentation 

   1. How machine learning can help: ML tools can automatically extract and log key product updates from meeting notes, emails, or engineering documents using NLP. For example, auto-tagging systems can detect and flag undocumented changes or inconsistencies in specs. ML-enabled document management systems can also predict gaps in documentation and suggest updates proactively.

3. Inconsistent or Incomplete Product Knowledge or Testing

   1. How machine learning can help: Machine learning programs can run predictive analytics to detect patterns of failure and predict future risk areas while testing. Additionally, computer vision machine learning tools can pinpoint defects, standardize test outputs, and flag inconsistencies before they reach human eyes. 

4. Incorrect Interpretation of Test Results

   1. How machine learning can help: ML algorithms can interpret complex test data more accurately than humans by learning from vast datasets and detecting anomalies that may go unnoticed. Explainable AI (XAI) tools can justify decisions, helping professionals and regulators understand how test results were derived, helping improve transparency. 

5. Difficulty managing suppliers

   1. How machine learning can help: ML algorithms can assess possible supplier risk by looking at historical performance data, delivery times, and defect rates. They can also use data from geopolitical events, economic conditions, or other events to predict when suppliers might be slower. 

In short: 

QC is a key aspect of most businesses, but it also can be a challenge. Keeping in mind these common issues will streamline your process, and also keep you ahead of the curve.  

Bibliography

1. “5 Common Quality Control Issues (and How to Fix).” 5 Common Quality Control Issues (And How to Fix), www.factoredquality.com/resource/5-common-quality-control-issues-and-how-to-prevent-them. Accessed 9 July 2025. 

2. Hayes, Adam. “Quality Control (QC): What It Is, How It Works, and QC Careers.” Investopedia, Investopedia, www.investopedia.com/terms/q/quality-control.asp. Accessed 9 July 2025. 

3. “How to Resolve the 10 Most Common Quality Control Issues: SC Training.” How to Resolve the 10 Most Common Quality Control Issues | SC Training, 18 Sept. 2024, training.safetyculture.com/blog/quality-control-issues/.