• Written by Sonali Subhadarsini, Product Manager

A category manager checking the latest shipment records from a supplier notices a discrepancy in the lead times, which could impact the entire supply chain, and wonders how to ensure that the data is accurate and up-to-date, and that the right people have access to it, which is where enterprise AI governance for supply chain comes in.

Enforcing permission at retrieval

The traditional approach to controlling access to sensitive data has been to instruct the model on what it can and cannot do, but this approach has limitations, as it relies on the model being correctly configured and updated, which can be time-consuming and prone to errors, whereas enforcing permission at retrieval means that the system checks the user's permissions every time they try to access the data, which provides an additional layer of security and ensures that the data is only accessible to those who need it.

This approach is particularly important in a supply chain context, where different teams and stakeholders need to access different types of data, and where the wrong data in the wrong hands could have serious consequences, such as a shift supervisor trying to access confidential supplier information, or a procurement manager trying to view sensitive inventory data.

Auditability

Auditability is critical in a supply chain context, as it allows organisations to track what data has been accessed, by whom, and when, which is essential for maintaining transparency and accountability, and for identifying potential security breaches or data leaks, and it also allows organisations to see what questions have been asked, and what answers have been returned, which can help to identify potential issues or biases in the data.

For example, if a category manager notices that a particular supplier's data is being accessed frequently, they may want to investigate further to see who is accessing the data and why, which could help to identify potential security risks or data leaks, and auditability provides a clear record of all data access and usage, which can be used to inform decisions and improve the overall security and integrity of the supply chain.

Supply chain data

The effectiveness of enterprise AI governance for supply chain depends on the quality and accuracy of the underlying data, which includes purchase and delivery history, lead times, inventory by location, and demand signal, as this data provides the foundation for the AI system to make informed decisions and provide accurate answers, and any errors or inaccuracies in the data can have serious consequences, such as incorrect inventory levels or misplaced shipments.

For instance, if the data on lead times is incorrect, the AI system may provide answers that are based on flawed assumptions, which could lead to delays or stockouts, and it is therefore essential to ensure that the data is accurate, up-to-date, and properly maintained, which can be a challenge in a complex supply chain with multiple stakeholders and data sources, and organisations may need to invest in data management and integration tools to ensure that the data is consistent and reliable.

What it changes

Enterprise AI governance for supply chain can help to identify and mitigate risks that may be hidden behind a stable average, such as lead time variance, which can have a significant impact on the supply chain, and it can also help to identify inventory that is being held against the wrong risk, which can help to reduce waste and improve efficiency, and by providing a more nuanced understanding of the supply chain, organisations can make more informed decisions and improve their overall performance.

For example, a shift supervisor may notice that a particular shipment is consistently late, but the average lead time for the supplier is still within the acceptable range, which could mask the issue, and enterprise AI governance for supply chain can help to identify this variance and provide a more accurate picture of the supply chain, which can inform decisions and improve the overall efficiency of the operation, and organisations can also use this information to negotiate with suppliers or adjust their inventory levels accordingly.

Measuring success

The success of enterprise AI governance for supply chain can be measured using a range of metrics, including lead time variance, fill rate, and inventory turns, which provide a clear indication of the effectiveness of the system and the overall performance of the supply chain, and by tracking these metrics, organisations can identify areas for improvement and make data-driven decisions to optimise their supply chain, and operations teams can use this information to adjust their processes and improve their overall efficiency.

For instance, if the lead time variance is high, it may indicate that there are issues with the supply chain that need to be addressed, such as delays or stockouts, and organisations can use this information to investigate further and identify the root cause of the issue, and by using these metrics, organisations can ensure that their supply chain is running smoothly and efficiently, and that they are getting the most out of their enterprise AI governance system.

Limitations

While enterprise AI governance for supply chain can be a powerful tool for improving the efficiency and effectiveness of the supply chain, it is not a silver bullet, and it has its limitations, and organisations should be aware of these limitations when implementing the system, and it is also important to note that the system is only as good as the data that it is based on, and if the data is inaccurate or incomplete, the system will not be able to provide accurate answers, and organisations may need to invest in data management and integration tools to ensure that the data is consistent and reliable, and for more information on how to implement enterprise AI governance for supply chain, organisations can contact us to discuss their specific needs and requirements.