• Written by Muhammed Abdulla NC, CEO

When a production line grinds to a halt because the maintenance team is waiting for a parts supplier to confirm a shipment, and that confirmation is stuck in an email inbox, it becomes clear that the right data was not in the right place at the right time. This is a common problem that can be avoided by having a solid AI checklist in place for production, ensuring all necessary systems are connected and data is flowing freely. A production AI system relies on access to various data sources, such as inventory management, supply chain tracking, and equipment monitoring, to provide accurate and timely answers to critical questions.

Connected Data Sources

A production AI system must be able to draw from multiple data sources to provide a complete picture of the production process. This includes data from machines on the factory floor, inventory management systems, and supply chain tracking. Without this connected data, the AI system is unable to provide accurate answers, and production teams are left waiting for information that never arrives. For instance, a shift supervisor checks the maintenance log to see when the next equipment inspection is due, but the log is not updated in real-time, causing delays and potential safety hazards.

The lack of connected data sources can have serious consequences, including delayed production, increased costs, and decreased quality. By ensuring that all necessary data sources are connected and accessible, production teams can avoid these pitfalls and keep the production line moving smoothly. This can be achieved by integrating systems such as enterprise resource planning (ERP) and manufacturing execution systems (MES) with the AI platform, allowing for real-time data exchange and analysis.

Agreed Definitions

Most people get wrong the idea that definitions can be agreed upon verbally or through informal means, but this is wrong. Definitions must be agreed upon in writing, with clear and concise language, to avoid misunderstandings and errors. This is particularly important in a production environment, where small mistakes can have significant consequences. By having written definitions, production teams can ensure that everyone is on the same page, and the AI system can provide accurate and consistent answers.

For example, the term "production ready" may have different meanings to different teams, but by defining it in writing, everyone can understand what it entails. This helps to avoid confusion and ensures that the AI system is providing answers based on a shared understanding of key terms. The category manager compares two suppliers based on their ability to meet the defined criteria, ensuring that the chosen supplier can deliver the required materials on time.

Permissions and Access

Permissions and access controls are often decided on an ad-hoc basis, but this can lead to security risks and data breaches. By deciding on permissions and access before the AI system is implemented, production teams can ensure that sensitive data is protected and only accessible to authorized personnel. This includes determining who can access the AI system, what data they can view, and what actions they can take.

By having clear permissions and access controls in place, production teams can prevent unauthorized access to sensitive data and ensure that the AI system is used responsibly. This is particularly important in a production environment, where data breaches can have serious consequences, including financial losses and damage to reputation. The production manager can review production schedules and adjust access controls accordingly, ensuring that only authorized personnel can view or modify sensitive data.

A First Question Worth Answering

A common mistake is to implement an AI system without a clear understanding of what questions it should be answering. By identifying a first question worth answering, production teams can ensure that the AI system is providing value from the outset. This question should be one that production teams already wait on, such as "what is the current inventory level of a specific part?" or "when is the next maintenance inspection due?"

For instance, the logistics team may be waiting on confirmation of a shipment from a supplier, and the AI system can provide real-time updates on the shipment status. By answering this question, the AI system can provide immediate value to the production team, and help to improve efficiency and reduce delays. The team can then build on this success, identifying additional questions that the AI system can answer to further improve production processes.

Limitations and Wrong Choices

This AI checklist for production is not a one-size-fits-all solution, and there are times when it may not be the right choice. For example, if the production process is highly variable or unpredictable, an AI system may not be able to provide accurate answers. In such cases, other solutions, such as manual data collection or statistical process control, may be more suitable. Additionally, if the production team is not willing or able to invest time and resources into implementing and maintaining an AI system, it may not be the right choice.

It is also important to note that an AI system is not a replacement for human judgment and expertise. While it can provide valuable insights and answers, it should be used in conjunction with human oversight and decision-making. If you are unsure about whether an AI system is right for your production team, you can contact us to discuss your specific needs and challenges. By understanding the limitations and potential wrong choices, production teams can make informed decisions about whether and how to implement an AI system.