• Written by Muhammed Abdulla NC, CEO

A quality control dashboard, once hailed as a revolutionary tool, now gathers dust in a corner of the factory floor, its screens dark and uninviting, a testament to the failed promise of AI in quality control, a consequence of automating a process nobody mapped.

Automating a process nobody mapped

The most common mistake when applying AI to quality is automating a process without first understanding its intricacies, a mistake that can lead to a breakdown in the entire system, as I have witnessed in numerous meetings where the focus was on the technology rather than the process itself, and I can confidently say that this approach is wrong because it neglects the human element that is essential to the success of any quality control initiative.

A shift supervisor, for instance, may check the maintenance log to ensure that all equipment is functioning properly, but if the process of checking the log is not clearly defined, automating it can lead to errors and inconsistencies, which is why it is crucial to map out the process before introducing AI, a step that is often overlooked in the rush to adopt new technology, and one that can have serious consequences if not done properly.

Treating inspection records as complete when it is not

Another mistake is treating inspection records as complete and accurate, when in reality they may be incomplete or inconsistent, a problem that can be exacerbated by AI, which can amplify existing errors and lead to a false sense of security, as I have seen in cases where the inspection records were not properly validated before being fed into the AI system.

For example, a quality control manager may use AsscherAi to analyze inspection records, but if the records are not comprehensive, the analysis will be flawed, and the manager may make decisions based on incomplete information, which is why it is essential to ensure that the data is accurate and complete before using AI to analyze it, a step that requires careful planning and attention to detail, and one that is often neglected in the haste to adopt new technology, and for more information on how to ensure data accuracy, you can visit our production page.

Giving the capability only to the analyst who already had it

Some organizations make the mistake of giving the AI capability only to the analyst who already had it, which can limit its potential and prevent others from benefiting from it, a mistake that can lead to a lack of collaboration and a narrow focus, as I have seen in cases where the AI system was only accessible to a select few, and the rest of the team was left out of the loop.

A category manager, for instance, may have access to the AI system, but if the rest of the team does not, they will not be able to contribute their insights and expertise, which can lead to a lack of diversity in the decision-making process, and ultimately, to poor decisions, which is why it is essential to make the AI capability available to all relevant stakeholders, and to provide training and support to ensure that they can use it effectively, and for more information on how to get started with AsscherAi, you can visit our contact us page.

Declaring success against a metric with no baseline

Declaring success against a metric with no baseline is another common mistake, as it can create a false sense of achievement and mask underlying problems, a mistake that can lead to complacency and a lack of continuous improvement, as I have seen in cases where the metric was not properly defined or tracked.

For instance, a quality control team may claim that the AI system has reduced defects by a certain percentage, but if there is no baseline to compare it to, it is impossible to know whether the reduction is significant or not, which is why it is essential to establish a baseline before implementing the AI system, and to track progress over time, which can help to identify areas for improvement and ensure that the system is having the desired impact.

This is a critical step that is often overlooked, and one that can have serious consequences if not done properly, as it can lead to a lack of accountability and a failure to address underlying problems, which is why it is essential to take the time to establish a baseline and track progress over time.

What this does not do

It is also important to note what this does not do, as some organizations may assume that AI can solve all their quality control problems, which is not the case, as AI is only a tool, and it requires careful planning, attention to detail, and a deep understanding of the process and the data, to be effective, and it is not a replacement for human judgment and expertise, but rather a supplement to it.

In some cases, AI may not be the right choice, such as when the process is highly complex or nuanced, or when the data is incomplete or inconsistent, in which case, other approaches may be more effective, such as manual inspection or statistical process control, which can provide more accurate and reliable results, and it is essential to carefully consider the limitations and potential biases of AI before implementing it, and to ensure that it is used in a way that complements and enhances human capabilities, rather than replacing them.

Ultimately, the success of AI in quality control depends on a deep understanding of the process, the data, and the limitations of the technology, as well as a commitment to continuous improvement and a willingness to adapt and evolve, which is why it is essential to approach AI with a critical and nuanced perspective, and to carefully consider the potential risks and benefits before implementing it.