• Written by Sonali Subhadarsini, Product Manager

On the factory floor, the shift supervisor checks the production schedule for the day, taking into account the changeover times between different product lines, and wonders how to reduce the losses that come with switching between them. This is a common challenge in manufacturing, where the pursuit of efficiency is constant, and the introduction of AI process automation in manufacturing is often seen as a potential solution.

Manufacturing complexities

Manufacturing, particularly in discrete and process plants, presents a unique set of challenges that can make AI process automation more difficult to implement. Changeover losses, for instance, can be significant, as the time and resources spent on switching between different product lines can add up quickly. Additionally, multi-plant comparability can be an issue, as different plants may have different processes, equipment, and systems, making it harder to compare and contrast their performance. Furthermore, the ageing asset base of many manufacturing facilities can also pose a challenge, as older equipment may not be compatible with newer automation systems.

The category manager, responsible for sourcing and procurement, may also face difficulties in comparing the performance of different suppliers, as the data may be scattered across different systems and plants. This can make it harder to identify areas for improvement and optimize the supply chain. The production planner, on the other hand, may struggle to balance the production schedule with the available resources, taking into account the changeover times and the capacity of the equipment.

Mapping the current process

Before automating any part of the process, it is essential to map out the current workflow, including all the steps, handoffs, and decision points. This involves observing the production process, talking to the people involved, and documenting the existing procedures. The maintenance manager, for example, may have a set of procedures for performing routine maintenance tasks, while the quality control team may have a separate set of procedures for inspecting and testing the products.

By mapping out the current process, manufacturers can identify areas where automation can add the most value, and where the introduction of AI process automation in manufacturing can help to reduce delays, improve efficiency, and increase productivity. This can also help to identify potential bottlenecks and areas where the process can be streamlined or optimized. For more information on how to get started with AI process automation in manufacturing, visit our website.

Handoffs and delays

One of the main causes of delay in manufacturing is the handoff between different teams, departments, or systems. When the production team finishes a batch of products, they may need to hand off the finished goods to the packaging team, who then need to hand off the packaged products to the shipping team. Each of these handoffs can introduce delays, as the teams may have different schedules, priorities, and systems.

The logistics coordinator, responsible for managing the flow of goods, may need to coordinate with the production team, the packaging team, and the shipping team to ensure that the products are delivered on time. This can involve a lot of communication, coordination, and planning, which can be time-consuming and prone to errors. By automating some of these handoffs, manufacturers can reduce the delays and improve the overall efficiency of the process.

Exception handling

Exception handling is a critical aspect of AI process automation in manufacturing, as it involves dealing with unexpected events, errors, or deviations from the normal process. When an exception occurs, the system may need to alert the relevant teams, trigger a set of predefined procedures, or even stop the production process altogether. The quality control team, for example, may need to investigate a batch of defective products, identify the root cause of the problem, and implement corrective actions to prevent it from happening again.

Exception handling can be complex and time-consuming, as it requires a deep understanding of the process, the equipment, and the systems involved. However, by automating some of these exception handling procedures, manufacturers can reduce the downtime, improve the quality of the products, and increase the overall efficiency of the process. If you have any questions about how to implement AI process automation in manufacturing, please contact us for more information.

Limitations and wrong choices

While AI process automation in manufacturing can be a powerful tool for improving efficiency and productivity, it is not a silver bullet, and there are situations where it may not be the best choice. For instance, in situations where the process is highly variable, or where the equipment is highly customized, automation may not be feasible or cost-effective. In such cases, manufacturers may need to rely on other strategies, such as process optimization, lean manufacturing, or total productive maintenance.

Additionally, AI process automation in manufacturing may not be suitable for situations where the process involves a high degree of human judgment, creativity, or problem-solving. In such cases, the introduction of automation may actually decrease productivity, or lead to a decrease in product quality. Manufacturers need to carefully evaluate their processes, and determine where automation can add the most value, before investing in AI process automation in manufacturing.