Adoption and change for quality
Enterprise AI adoption for quality drives change by integrating into existing routines, such as daily meetings where quality metrics are reviewed, allowing users to ask questions and get answers in a familiar context, making it more likely to be adopted and improving quality management.
A quality manager stares at a screen displaying a dashboard of metrics, wondering why the new enterprise AI system for quality is not being used to its full potential, despite its ability to answer complex questions about the organisation's own data in real time.
The issue lies in the fact that new capabilities are often expected to be used in isolation, rather than being integrated into existing routines and workflows. This is a mistake, as it is the attachment of new capability to a routine that already exists that drives adoption and change for quality.
Most people get wrong the idea that simply providing a new tool or system is enough to drive change, but this is wrong. The reality is that change only happens when new capabilities are embedded into the daily routines of the people who need them.
Attaching new capability to existing routines
For a new system to be adopted, it needs to be attached to a routine that already exists, such as a daily or weekly meeting where quality metrics are reviewed. This allows the new capability to be used in a context that is familiar to the users, making it more likely to be adopted.
A shift supervisor, for example, can use the enterprise AI system to answer questions about the maintenance log, and then use that information to inform decisions about which machines to prioritise for maintenance. This integration into an existing routine makes the new capability more useful and more likely to be used.
Who needs to be able to ask, not just who needs the answer
Another common mistake is to focus only on who needs the answer, rather than who needs to be able to ask the question. In the case of quality, this means that not just quality managers, but also production staff and supervisors need to be able to ask questions and get answers from the system.
This is because quality is not just the responsibility of the quality department, but is a shared responsibility across the organisation. By giving more people the ability to ask questions and get answers, the organisation can tap into a wider range of knowledge and expertise, and make better decisions.
The quality data this depends on
The ability to answer questions about quality depends on having high-quality data, including inspection records, non-conformance reports, batch genealogy, and customer complaints. This data needs to be accurate, complete, and up-to-date, in order to provide reliable answers to questions.
For example, a category manager comparing two suppliers needs to be able to access data on the quality of the components they provide, in order to make an informed decision. This data can be accessed through the production system, and used to answer questions about the quality of the components.
What it changes about defects and CAPA records
The use of enterprise AI for quality can change the way that defects are traced and CAPA records are managed. By providing real-time answers to questions about quality, the system can help to identify defects earlier, and trace them back to their root cause more quickly.
This can also help to reduce the number of CAPA records that are written but never read, by providing a more effective way of identifying and addressing quality issues. For example, a quality manager can use the system to identify a trend of defects in a particular batch, and then use that information to inform a CAPA record.
Measured with key metrics
The effectiveness of the enterprise AI system for quality can be measured using key metrics such as first pass yield, defects per unit, and time to root cause. These metrics provide a clear indication of whether the system is having a positive impact on quality, and can be used to identify areas for improvement.
By tracking these metrics over time, the organisation can see whether the use of the enterprise AI system is leading to improvements in quality, and make adjustments as needed. For more information on how to get started with using the system, contact us at contact-us.
What this does not do
The enterprise AI system for quality is not a replacement for human judgement and expertise. It is a tool that provides answers to questions, but it is up to the users to interpret those answers and make decisions based on them.
It is also not a solution for organisations that do not have a strong foundation in quality management. The system relies on high-quality data and a well-defined quality management process, in order to provide accurate and reliable answers to questions.
Frequently asked questions
How can enterprise AI improve quality management in our organisation?
Enterprise AI can improve quality management by providing real-time answers to complex questions and integrating into existing routines, making it easier to identify and address quality issues.
What data is required for effective enterprise AI quality management?
High-quality data, including inspection records, non-conformance reports, and customer complaints, is necessary for effective enterprise AI quality management, providing reliable answers to questions.
Can enterprise AI replace human judgement and expertise in quality management?
No, enterprise AI is a tool that provides answers to questions, but human judgement and expertise are still necessary to interpret those answers and make decisions based on them.
How can we measure the effectiveness of enterprise AI for quality management?
The effectiveness of enterprise AI for quality management can be measured using key metrics such as first pass yield, defects per unit, and time to root cause, providing a clear indication of improvement.