When it comes to manufacturing, there are numerous challenges the industry must face, including errors on production lines. However, these issues can be mitigated with sophisticated error reporting software, which can be further enhanced through artificial intelligence.
In this article, we explore AI-driven error reporting software and why you should implement it in your manufacturing business. Keep reading to learn more.
What Is Artificial Intelligence?
As the name suggests, artificial intelligence (AI) is where computers and software are able to function “intelligently,” meaning autonomously or without significant human input. In other words, with artificial intelligence, computers are built to think and make decisions like humans.
While there are many types of AI, perhaps the most notable is machine learning in which AI algorithms are fed information and learn new skills or capabilities by themselves. Some other popular forms of artificial intelligence you will likely have used yourself include Apple’s Siri, Amazon’s Alexa, the technology behind Tesla’s self-driving cars, and ChatGPT.
How Can AI Enhance Error Reporting in Manufacturing?
Clearly, AI has many diverse applications. So, let’s now dive deeper into how artificial intelligence technology can benefit your own manufacturing business, especially in terms of error reporting.
Data Analysis and Processing
As errors occur, this information can be fed into an AI model, which will learn from past and ongoing production line issues. With this data in mind, artificial intelligence models can then analyze and process your data, highlighting patterns within errors and providing data interpretations. This means that, if the same error keeps happening, AI can analyze the available information surrounding the recurring issue and provide you with potential causes and probable remedies.
Efficiency Recommendations
Using the data mentioned above, as well as general statistics about your manufacturing process, sales, faulty items, returns, and due dates, AI models can provide your business with effective efficiency recommendations. For example, if it finds that one part of the production process is disproportionately slow compared to others, AI can highlight this and offer in-depth data to help inform your decisions in improving this area of manufacturing.
Predict Production-Line Failures
By analyzing historical data, AI algorithms can also predict future production line failures. If your production line encounters issues after producing an average of 200 products, for instance, it may identify this and highlight the issue within a margin of error. This could otherwise be missed by human analysis due to outliers in datasets–sometimes errors might occur at 203 products, or 220, or 196, but AI can hone in on the most likely time for an error to arise. Once errors are predicted, you and your team can work toward remedying them before they ever occur.
Automated Real-Time Reporting
If you’ve used ChatGPT, then you probably already know how well artificial intelligence is at mimicking human language and generating legible, professional text. Thanks to these capabilities, AI can be leveraged to generate manufacturing reports using the real-time data it collects. The technology can also produce daily, weekly, or monthly reports automatically, meaning your team can focus more on analyzing and interpreting reports, as there’s no need to spend time creating them.
Technology Used in AI Error Reporting Software
It’s clear that AI has a powerful place in manufacturing error reporting software. So, let’s discuss what technology underpins artificial intelligence, giving you a better understanding of what you’re implementing into your business.
Large Language Models
A Large Language Model (LLM) is a form of predictive artificial intelligence powered by large swathes of human and narrative text. Essentially, LLMs analyze natural language and produce text based on the probability of its relevance to its user input, contextual understanding, and fundamental language rules.
ChatGPT, for example, is a large language model that’s capable of holding a human-like conversation with users, providing responses seemingly with a contextual understanding and presence of mind. This is done by simply predicting the most likely relevant response to user input based on its training data and historical conversations with the user.
Neural Networks
Neural networks are a form of artificial intelligence that attempt to mimic the way human brains learn and process information. They are part of machine learning, a subset of AI in which computer algorithms are designed to learn autonomously from their training data. Neural networks are the heart of AI deep learning, where it’s possible for machines to grasp and process complex concepts and data that are crucial to maintaining consistent manufacturing error reporting.
Challenges With AI in Manufacturing Error Reporting
While AI enables more proactive error detection in manufacturing, integrating the technology poses some key challenges in terms of data, ethics and model risks.
Data Availability
AI models require substantial training data to accurately learn to identify errors and anomalies; however, many manufacturers lack sufficiently large historical defect datasets. Although data may be stored on legacy systems and transferred, in some cases, it’s never digitally recorded.
Carefully documenting and consolidating defect data is essential but difficult preparatory work. As a result, data pipelines must be built to ingest and preprocess data from various equipment, inspections, and logs. Ongoing capture processes must also be instituted to provide AI with sufficient high-quality data.
Privacy Concerns
Collecting shop floor data at scale may raise privacy questions regarding surveillance. Workers rightfully have concerns about excessive tracking given the personal information that could be tracked as a result of AI. This means transparency and consent are vital.
Because AI models should analyze patterns in the collective data rather than monitoring individuals, appropriate access controls and aggregation must be implemented so that data is anonymized and minimized to what is essential for defect detection. Communication, education and collaboration with workers will ultimately help align privacy preserving practices.
Hallucinations
Due to their statistical nature, AI models carry risks of hallucinating false insights that don’t correspond to reality. In manufacturing, this could lead to phantom defect detections, false alarms, and overreactions to normal variances.
While safeguards like confidence thresholds on predictions, ongoing model validation, and human oversight checks help mitigate these risks, critical decisions shouldn’t be fully automated without human involvement.
How to Smoothly Implement AI Error Reporting in Your Manufacturing Business
When it comes to implementing new technology, careful planning and engagement are required in order to ensure smooth adoption.
Provide Employee Training
To build support rather than fear about AI, you should provide extensive training to workers on how the AI models function and impact their roles. For this, you’ll want to be transparent about what data is collected, how it is used, and who can access it. You should also highlight tangible benefits like increased operational safety and proactive corrections that ease day-to-day tasks.
Outline AI Code of Conduct
Drafting an AI code of ethics to guide development, deployment and monitoring in line with organizational values is good practice. This helps ensure the AI meets expected standards around transparency, bias avoidance, human oversight, privacy preservation and error correction. It also signals priorities to employees like fair and safe AI use.
Monitor Results and Feedback
You should actively monitor AI performance indicators, such as accuracy, false positives/negatives and overall defect detection rates, and watch for unintended consequences or output drift from operational reality that could require retraining. Additionally, you should apply rigorous statistical validation and gather regular employee feedback through surveys, interviews and focus groups.
Robust AI Manufacturing Error Reporting Software From Idea Maker
If you’re for AI manufacturing error reporting software for your business, you’re in the right place. At Idea Maker, we have a dedicated team of AI and software development experts ready to improve your business’s efficiency. Schedule a free consultation with us today to learn more.
