• Written by Sonali Subhadarsini, Product Manager

In a manufacturing environment, a dashboard that nobody opens is a common sight, often a relic of a well-intentioned project that failed to account for the complexities of the production floor, where the shift supervisor and category manager have to make decisions quickly, and the data they need is not always readily available, which is why enterprise AI governance in manufacturing is crucial to ensure that the system is used effectively.

Manufacturing complexities

The nature of manufacturing makes it harder to implement and maintain a system like AsscherAi, as the production schedule dictates the workflow, and any downtime or changeover losses can have significant consequences, which is why the system needs to be able to handle the nuances of discrete and process plants, and the ageing asset base that often comes with them, where the maintenance log is a critical component of the decision-making process.

For instance, in a multi-plant setup, comparability of data becomes a challenge, as different plants may have different systems, processes, and even definitions of key metrics, which can make it difficult to get a clear picture of the overall performance, and this is where AsscherAi can help, by providing a unified view of the data, but it requires careful planning and governance to ensure that the system is used effectively.

Permission and access control

One of the key aspects of governance is enforcing permission and access control, which is often done by instructing the model on what data to retrieve and what actions to take, but this approach can be limiting, as it does not account for the dynamic nature of the manufacturing environment, where roles and responsibilities can change quickly, and the system needs to be able to adapt to these changes, which is why it is better to enforce permission at retrieval, rather than by instructing the model.

This approach allows for more flexibility and ensures that the right people have access to the right data, at the right time, and it also reduces the risk of data breaches, as the system is designed to only retrieve the data that is necessary for a specific task, and nothing more, which is a critical consideration in a manufacturing environment, where data security is paramount, and more information on this topic can be found on our website.

Auditability and transparency

Auditability and transparency are critical components of governance, as they provide a clear record of what was asked, what was returned, and on what data, which is essential for tracking changes, identifying errors, and ensuring compliance with regulatory requirements, and this is where AsscherAi can provide significant value, by providing a clear and transparent record of all interactions with the system.

For example, the category manager can use AsscherAi to compare two suppliers, and the system will provide a clear record of the data that was used to make the comparison, and the results that were returned, which can be used to inform future decisions, and ensure that the procurement process is fair and transparent, and our team can provide more information on how to implement this in your organisation.

Routing to a human

There are certain categories of questions that should always be routed to a human, by design, as they require a level of judgment, expertise, or nuance that is beyond the capabilities of the system, and these categories can vary depending on the specific use case, and the requirements of the organisation, but they may include things like procurement decisions, or maintenance scheduling, where the shift supervisor needs to make a decision quickly, and the system needs to be able to provide the necessary information.

For instance, in a discrete manufacturing environment, the system may be able to provide real-time data on production levels, and inventory, but it may not be able to provide the same level of insight into the nuances of the production process, or the subtleties of the supply chain, which is where the expertise of the category manager comes in, and the system needs to be designed to route these types of questions to a human, where they can be evaluated and acted upon accordingly.

Limitations and wrong choices

While AsscherAi can provide significant value in a manufacturing environment, it is not a panacea, and there are certain situations where it may not be the right choice, such as in situations where the data is highly unstructured, or where the decision-making process requires a high degree of creativity, or outside-the-box thinking, and in these situations, other approaches may be more effective, such as traditional business intelligence tools, or more specialized systems that are designed to handle specific types of data, or decision-making processes.

In these situations, it is better to carefully evaluate the requirements of the organisation, and the capabilities of the system, before making a decision, and to consider factors such as the complexity of the data, the nature of the decision-making process, and the level of expertise required, and to use

• traditional business intelligence tools
• more specialized systems