• Written by Sonali Subhadarsini, Product Manager

When a category manager at a large retailer tries to query the performance of different carriers using AsscherAi, they often find that the results are inconsistent, and the system struggles to account for the variability in carrier performance, dwell and detention times, and exception handling at scale, which can lead to frustration and a lack of trust in the system. This is because logistics, by its very nature, is a complex and dynamic field, with many moving parts and variables that can affect the outcome of a query. As a result, the system's ability to provide accurate and reliable results is compromised, and the category manager is left to wonder what went wrong.

Logistics complexity

The logistics field is inherently complex, with many factors that can affect the performance of carriers, including traffic, weather, and road conditions, which can cause delays and disruptions to the supply chain. Additionally, the variability in dwell and detention times, which refer to the time a carrier spends waiting to load or unload cargo, can have a significant impact on the overall efficiency of the logistics operation. Exception handling, which refers to the process of dealing with unexpected events or disruptions, is also a critical aspect of logistics, and one that can be particularly challenging to manage at scale. When a system like AsscherAi is used to query logistics data, it must be able to account for these complexities and variables in order to provide accurate and reliable results.

A shift supervisor at a warehouse, for example, may use AsscherAi to query the status of a particular shipment, only to find that the system is unable to provide a clear answer due to the complexity of the logistics operation. This can lead to frustration and a lack of trust in the system, and highlights the need for more sophisticated governance and risk management controls to be put in place.

Enforcing permission at retrieval

Rather than relying on the model to enforce permission controls, it is more effective to enforce permission at the point of retrieval, when the user is actually querying the data. This approach ensures that the user only has access to the data that they are authorized to see, and prevents unauthorized access to sensitive information. By enforcing permission at retrieval, organizations can ensure that their data is protected and that only authorized users have access to it.

This approach is particularly important in logistics, where data may be sensitive or confidential, and where unauthorized access could have serious consequences. By using AsscherAi to enforce permission at retrieval, organizations can ensure that their data is protected and that only authorized users have access to it, which can help to build trust and confidence in the system.

Auditability

Auditability is a critical aspect of governance and risk management in logistics, as it allows organizations to track and monitor all queries and interactions with the system. This includes tracking what was asked, what was returned, and on what data, which can help to identify any errors or inconsistencies in the system. By maintaining a clear audit trail, organizations can ensure that they are able to track and monitor all activity on the system, and can identify any potential issues or risks.

For example, a category manager may use AsscherAi to query the performance of different carriers, and may want to track the results of that query to ensure that they are accurate and reliable. By maintaining an audit trail, the category manager can track what was asked, what was returned, and on what data, which can help to build trust and confidence in the system. To learn more about how AsscherAi can help with auditability, visit our website for more information.

Routing to a human

There are certain categories of queries that should always be routed to a human, rather than being handled by the system. These include queries that are sensitive or confidential, or that require a high degree of judgment or expertise. By routing these queries to a human, organizations can ensure that they are handled correctly and that the user receives the support and guidance they need. This approach can help to build trust and confidence in the system, and can ensure that users are able to get the most out of AsscherAi.

For example, a shift supervisor may use AsscherAi to query the status of a particular shipment, but may need to speak to a human to get more information or to resolve any issues that may have arisen. By routing this query to a human, the shift supervisor can get the support and guidance they need, and can ensure that the issue is resolved quickly and efficiently. If you have any questions about how AsscherAi can help with routing queries to a human, please contact us for more information.

Limitations of the system

While AsscherAi can be a powerful tool for querying logistics data, it is not a panacea for all logistics-related issues. There are certain limitations and constraints that must be taken into account when using the system, including the quality and accuracy of the data, and the complexity of the logistics operation. Additionally, AsscherAi is not a replacement for human judgment and expertise, but rather a tool that can be used to support and augment human decision-making.

By understanding the limitations and constraints of AsscherAi, organizations can ensure that they are using the system effectively and efficiently, and can avoid any potential pitfalls or risks. This may involve using the system in conjunction with other tools and technologies, or providing additional training and support to users to ensure that they are able to get the most out of the system.