• Written by Sonali Subhadarsini, Product Manager

This week, a shift supervisor at a manufacturing plant needs to decide whether to authorise overtime for a team working on a critical project, and they currently base this decision on their own experience and manual checks of the maintenance log and project status reports. However, as the plant starts to use AsscherAi to answer questions about its own data in real time, the supervisor will be able to make more informed decisions, but only if the system is governed correctly.

Enforcing permission at retrieval

The traditional approach to controlling access to sensitive data is to instruct the model on what data it can and cannot access, but this approach can be flawed, as it relies on the model being correctly configured and updated. A more effective approach is to enforce permission at the point of retrieval, so that even if the model is incorrectly configured, the data will still be protected. This is particularly important in an operations context, where sensitive data about projects, budgets, and suppliers may be stored.

For example, a category manager may need to compare the performance of two suppliers, but they should only be able to access data that is relevant to their role and responsibilities. By enforcing permission at retrieval, the organisation can ensure that sensitive data is only accessed by authorised personnel, and that the risk of data breaches or misuse is minimised.

Auditability

Auditability is critical in an operations context, as it allows organisations to track what questions are being asked, what data is being returned, and on what data the answers are based. This is essential for ensuring that the system is being used correctly and that any errors or anomalies are quickly identified and corrected. For instance, if a project manager asks about the status of a project, the organisation should be able to track what data was used to answer that question, and whether the answer was accurate.

By maintaining a record of all questions and answers, organisations can also identify trends and patterns in the data, and make more informed decisions about their operations. This can be particularly useful in identifying potential issues or risks, and taking proactive steps to mitigate them.

Operations data

The effectiveness of AsscherAi in an operations context depends on the quality and accuracy of the underlying data. This includes process and exception records, project status, budget consumption, and supplier performance data. If this data is incomplete, inaccurate, or outdated, the answers provided by the system will be flawed, and may lead to incorrect decisions being made.

For example, a project manager may ask about the progress of a project, but if the project status data is not up to date, the answer will be incorrect. Similarly, if a category manager asks about the performance of a supplier, but the supplier performance data is incomplete, the answer will be flawed. Organisations must therefore ensure that their operations data is accurate, complete, and up to date, in order to get the most out of AsscherAi.

What it changes

AsscherAi can help organisations to identify anomalies and trends in their operations data that may not be visible through traditional reporting methods. For example, a project may be consuming budget at a faster rate than progress is being made, but this may only be visible when the project status and budget consumption data are combined. Similarly, a supplier may be consistently delivering late, but this may only be visible when the supplier performance data is analysed over time.

By providing real-time answers to questions about their operations data, AsscherAi can help organisations to identify and address these issues quickly, and to make more informed decisions about their operations. This can be particularly useful in an operations context, where timely and informed decision-making is critical to success.

Measuring success

The success of AsscherAi in an operations context can be measured in a variety of ways, including on-time delivery, exception rate, and expediting cost. For example, if a project is consistently delivered on time, and the exception rate is low, this may indicate that the system is working effectively. Similarly, if the expediting cost is reduced, this may indicate that the system is helping to identify and address issues quickly.

By tracking these metrics, organisations can get a clear picture of the effectiveness of AsscherAi in their operations context, and make adjustments as needed to optimise the system. This may involve refining the underlying data, adjusting the permissions and access controls, or providing additional training to users.

Limitations

While AsscherAi can be a powerful tool for organisations, it is not a panacea for all operations issues. There may be cases where the system is not the best fit, or where additional support or guidance is needed. For example, if an organisation is struggling with data quality or governance issues, AsscherAi may not be able to provide accurate or reliable answers. In these cases, it may be necessary to address the underlying issues before implementing the system.

If you are considering implementing AsscherAi in your organisation, but are unsure about how to get started or whether the system is right for you, you can contact us for more information and guidance. We can help you to assess your operations data and governance, and provide recommendations on how to get the most out of the system.