• Written by Sonali Subhadarsini, Product Manager

A shift supervisor checks the maintenance log to see why a production line has been idle for the past hour, only to find that the issue was supposed to have been resolved the day before, and now the entire day's production is at risk of being delayed. This is a common scenario in many production environments, where the lack of automation in key processes can lead to significant delays and losses. One way to address this issue is through AI process automation for production, which involves automating the steps around a decision rather than the decision itself.

Mapping the current process

Before automating any part of the production process, it is essential to map out the current process in detail. This involves identifying all the steps involved in a particular process, from the initial request to the final outcome. For instance, in the case of a production line, this might involve mapping out the steps involved in requesting maintenance, scheduling the maintenance, and completing the maintenance work. The category manager might be responsible for ensuring that the production schedule is aligned with the maintenance schedule, to minimize downtime.

By mapping out the current process, organizations can identify areas where automation can have the most significant impact. This might involve automating routine tasks, such as data entry or report generation, or automating decision-making processes, such as determining when maintenance is required. The goal is to identify areas where automation can streamline the process, reduce delays, and improve overall efficiency.

Handoffs that create delay

One of the primary causes of delay in production environments is the handoff of information or tasks from one person or team to another. For example, when a production line operator identifies an issue with a machine, they may need to notify the maintenance team, who then need to schedule the repair work. This handoff can create a significant delay, especially if the maintenance team is not immediately available or if the issue is not properly communicated.

In many cases, these handoffs can be automated, reducing the delay and improving overall efficiency. For instance, a system can be put in place to automatically notify the maintenance team when a production line operator identifies an issue, or to automatically schedule the repair work. This can help to reduce the delay and ensure that issues are addressed promptly.

Production data dependency

AI process automation for production depends on access to accurate and timely production data. This includes data on output against plan, cycle times, scrap and quality records, and maintenance history. By analyzing this data, organizations can identify areas where automation can have the most significant impact and make informed decisions about where to focus their automation efforts. The production manager might use this data to identify trends and patterns in production, and to make adjustments to the production schedule accordingly.

For example, if the data shows that a particular machine is experiencing a high rate of scrap, the production manager might decide to automate the quality control process for that machine, to reduce the amount of scrap and improve overall quality. Similarly, if the data shows that maintenance is frequently required for a particular machine, the production manager might decide to automate the maintenance scheduling process, to reduce downtime and improve overall efficiency.

Changes to unplanned downtime and stock cover

AI process automation for production can have a significant impact on unplanned downtime and stock cover against the plan. By automating routine tasks and decision-making processes, organizations can reduce the likelihood of unplanned downtime and improve their ability to meet production targets. For instance, a system can be put in place to automatically detect when a machine is experiencing issues and to notify the maintenance team, reducing the likelihood of unplanned downtime.

Similarly, AI process automation can help organizations to improve their stock cover against the plan. By analyzing production data and automating decision-making processes, organizations can ensure that they have the right amount of stock on hand to meet production targets, reducing the likelihood of stockouts or overstocking. This can help to improve overall efficiency and reduce waste.

Measurement and evaluation

The success of AI process automation for production can be measured and evaluated using a variety of metrics, including overall equipment effectiveness (OEE), unplanned downtime hours, and scrap rate. By tracking these metrics, organizations can determine whether their automation efforts are having the desired impact and make adjustments as needed. For example, if the data shows that OEE has improved since automation was implemented, but unplanned downtime hours have not decreased, the organization may need to adjust their automation strategy to focus more on reducing downtime.

For more information on how to implement AI process automation for production, organizations can visit our production page. Additionally, organizations can contact us to discuss their specific needs and determine whether AI process automation is right for them.

Limitations and wrong choices

While AI process automation for production can be a powerful tool for improving efficiency and reducing delays, it is not a solution that is right for every organization. In some cases, the cost and complexity of implementing automation may outweigh the potential benefits, or the organization may not have the necessary data and infrastructure to support automation. In these cases, alternative solutions, such as process re-engineering or staff training, may be more effective.

It is also important to note that AI process automation is not a replacement for human judgment and decision-making. While automation can streamline routine tasks and decision-making processes, it is still important to have human oversight and input to ensure that automation is working effectively and that any issues that arise are addressed promptly. By understanding the limitations and potential wrong choices, organizations can make informed decisions about whether and how to implement AI process automation for production.