• Written by Muhammed Abdulla NC, CEO

A meeting to discuss the adoption of enterprise AI in manufacturing can be a frustrating experience, as I have learned from sitting through many such discussions. The conversation often starts with enthusiasm for the potential benefits, but quickly becomes mired in concerns about implementation and integration with existing systems. Before long, the meeting devolves into a discussion of why it's just not possible to make the necessary changes, and the idea is shelved. This is a shame, because with the right approach, manufacturing organisations can overcome the obstacles and start to realise the benefits of enterprise AI.

Manufacturing complexities

One of the main challenges in manufacturing is the complexity of the production process. Changeover losses, where production is halted to switch from one product to another, can be a major issue. In addition, multi-plant comparability can be difficult to achieve, as different plants may have different equipment and processes. Furthermore, the ageing asset base of many manufacturing facilities can make it difficult to integrate new technology. These factors can make it harder to adopt new systems, including enterprise AI.

For example, a shift supervisor at a food processing plant may struggle to implement a new quality control system because the existing equipment is not compatible. This can lead to a situation where the supervisor is forced to choose between upgrading the equipment, which may not be feasible, or finding a workaround, which can be time-consuming and prone to error.

Building on existing routines

A more effective approach to adopting enterprise AI in manufacturing is to attach new capability to a routine that already exists. This can help to build trust and familiarity with the new system, and can make it easier to integrate with existing processes. For instance, a category manager at a manufacturing company can use AsscherAi to compare two suppliers, and then use that information to inform their purchasing decisions.

By building on existing routines, manufacturing organisations can start to realise the benefits of enterprise AI without having to make major changes to their operations. This can be a more gradual and sustainable approach, and can help to build a strong foundation for further adoption and development.

Who needs to ask

Most people think that the only people who need to be able to ask questions of an enterprise AI system are those who need the answer. However, this is wrong. In reality, anyone who has a question about the manufacturing process should be able to ask, regardless of their role or level of seniority. This can help to ensure that the system is used to its full potential, and that everyone has access to the information they need to do their job effectively.

For example, a maintenance technician at a manufacturing plant may have a question about the maintenance log, and should be able to ask AsscherAi for the information they need. This can help to improve communication and collaboration across the organisation, and can ensure that everyone is working with the same information.

The first wrong answer

Even with the best systems and processes in place, there will inevitably be times when the enterprise AI system provides a wrong answer. This can be a major setback, and can damage trust in the system. However, it's how the organisation responds to this situation that really matters. By being transparent about the error, and taking steps to correct it and prevent it from happening again, the organisation can start to rebuild trust and demonstrate its commitment to using the system effectively.

For more information on how to implement AsscherAi in your organisation, you can visit our website at www.asscher.ai. Our team is also available to answer any questions you may have, and can be contacted through our contact us page.

When it's the wrong choice

While enterprise AI can be a powerful tool for manufacturing organisations, there are some situations where it may not be the best choice. For example, in situations where the data is highly unstructured or uncertain, other approaches may be more effective. Additionally, in situations where the organisation is not prepared to make the necessary changes to its processes and systems, enterprise AI may not be the right choice.

In these situations, it's often better to take a step back and assess the organisation's readiness for enterprise AI. This can involve evaluating the organisation's data management practices, its ability to integrate new systems, and its willingness to make changes to its processes. By taking a careful and considered approach, manufacturing organisations can ensure that they are using enterprise AI in a way that is effective and sustainable.