• Written by Muhammed Abdulla NC, CEO

In manufacturing, AI is often touted as a revolution that will transform the entire operation, from the factory floor to the executive suite. However, in practice, AI has largely landed in the office functions, such as sales and marketing, where the output is text, rather than in the physical work of production. According to the US Census Bureau's Business Trends and Outlook Survey, 14.3% of US businesses used AI in sales and marketing in the previous six months, compared to 1.7% in production of goods. This disparity is not surprising, given that AI is well-suited to tasks that involve analyzing and generating text, such as responding to customer inquiries or generating reports.

Where AI has actually landed in manufacturing so far

AI has been most successful in manufacturing in areas such as procurement, where it can be used to analyze supplier data and generate reports. For example, a category manager can use AI to compare the prices and delivery times of different suppliers, and generate a report that summarizes the findings. This can save time and improve the accuracy of the procurement process. However, AI has not yet had a significant impact on the physical work of production, such as machining or assembly.

In fact, according to the US Census Bureau's Business Trends and Outlook Survey, only 2.2% of US manufacturers used AI in production of goods, compared to 12.4% who used AI in sales and marketing. This suggests that AI is still largely being used in the office functions of manufacturing, rather than on the factory floor.

Changeover losses

One area where AI can have a significant impact in manufacturing is in reducing changeover losses. Changeover losses occur when a machine or production line is shut down for maintenance or to switch to a different product. By using AI to analyze data from the ERP, MES, plant historian, and maintenance system, manufacturers can quickly identify the causes of changeover losses and take steps to reduce them. For example, a shift supervisor can use AI to ask the data a direct question, such as "what is the average downtime for this machine?", and get an answer in real-time.

This can help manufacturers to reduce downtime and increase productivity. By using AI to analyze data and generate reports, manufacturers can identify areas for improvement and take steps to address them. This can lead to significant cost savings and improved efficiency.

Multi-plant comparability

Another area where AI can have a significant impact in manufacturing is in multi-plant comparability. Many manufacturers have multiple plants, each with its own systems and processes. By using AI to analyze data from each plant, manufacturers can compare performance and identify areas for improvement. For example, a plant manager can use AI to compare the production rates of different plants, and identify areas where one plant is outperforming another.

This can help manufacturers to identify best practices and implement them across all plants. By using AI to analyze data and generate reports, manufacturers can compare performance and identify areas for improvement. This can lead to significant cost savings and improved efficiency. To learn more about how AI can be used in manufacturing, contact us today.

Ageing asset base

However, one area where AI is not yet having a significant impact in manufacturing is in the ageing asset base. Many manufacturers have older equipment and systems that are not well-suited to AI. In order to use AI effectively, manufacturers need to have a robust data infrastructure in place, including sensors, data storage, and analytics software. Without this infrastructure, AI can actually add noise and complexity to the system, rather than improving it.

For example, if a manufacturer has an older machine that is not equipped with sensors, it may not be possible to collect the data needed to use AI effectively. In this case, the manufacturer may need to invest in new equipment or upgrade their existing systems before they can use AI. To learn more about how to implement AI in manufacturing, visit our production page.

Why most adopters have not changed anything underneath

According to the US Census Bureau's Business Trends and Outlook Survey, 64.3% of US businesses that have adopted AI have made no changes to their underlying systems or processes. This suggests that many manufacturers are simply layering AI on top of their existing systems, rather than using it to transform their operations. However, this approach can limit the benefits of AI, as it is not being used to address the underlying inefficiencies and complexities of the system.

For example, if a manufacturer is using AI to analyze data from their ERP system, but the ERP system itself is outdated and inefficient, the benefits of AI will be limited. In order to get the most out of AI, manufacturers need to be willing to make changes to their underlying systems and processes. To learn more about how to get the most out of AI in manufacturing, read our blog.

What a sensible first quarter looks like for a manufacturing business

A sensible first quarter for a manufacturing business that is implementing AI might involve focusing on a single question, such as "what is the average downtime for this machine?", and a single system, such as the ERP system. By focusing on a specific question and system, the manufacturer can quickly see the benefits of AI and build momentum for further implementation. The manufacturer can also start with a single team, such as the maintenance team, and expand from there.

This approach can help manufacturers to avoid the pitfalls of trying to implement AI too broadly, and to focus on the areas where it can have the most impact. By starting small and building from there, manufacturers can ensure that they are getting the most out of their AI investment. According to the US Census Bureau's Business Trends and Outlook Survey, only 6.8% of US businesses that have adopted AI have changed how they collect or manage data, which suggests that many manufacturers are not yet using AI to its full potential.

What this does not do

AI is not a magic bullet that can solve all of the problems of a manufacturing business. It is a tool that can be used to analyze data and generate reports, but it is not a replacement for human judgment and expertise. In order to get the most out of AI, manufacturers need to have a clear understanding of what it can and cannot do, and to use it in conjunction with other tools and techniques. For example, AI can be used to analyze data from the plant historian, but it cannot replace the expertise of a experienced plant manager.

Additionally, AI is not suitable for all types of manufacturing businesses. For example, businesses that have highly variable or custom production processes may not be well-suited to AI, as it is typically most effective in repetitive and routine tasks. In these cases, other tools and techniques, such as lean manufacturing or total productive maintenance, may be more effective. By understanding the limitations of AI, manufacturers can ensure that they are using it in the most effective way possible.