Gone are the days of hand-written tables. Production plans have now gone digital and are being managed through PC applications and specialized systems. Because of this, AI-enhanced production control systems are in demand. So, what will AI bring to production management? In this blog learn why organizations are introducing AI in production management, the benefits that come from it, and examples of it in use.
Why the introduction of AI in production management is gaining attention?
Why is AI technology being used more in production management? Let’s consider the relationship with AI while looking at the purpose of production control.
Purpose of production control
Production control oversees production activities for cost efficiency and quality assurance. It also manages optimizing the “making” and “selling” of products, which is crucial in the manufacturing industry.
For more, read our blog about production control, manufacturing control, and quality control.
Differences in Production Management, Manufacturing Management, and Quality Control | Stratus
Organizations manage a variety of production operations, including raw materials procurement, inventory management, delivery information, and transportation methods. The scope of this management is wide, and the work is extremely complex.
Production management systems are helping companies carry out these complex operations.
Read more about the functions and benefits of production management systems in our blog post below.
Functions and Benefits of the Production Control System | Stratus
Advantages of using AI for production management
AI is now being used in production management operations. Before AI, there were many problems in production management that were difficult to solve, even after introducing a production management system. To balance the heavy workloads of each department, organizations must divide personnel and coordinate effectively between departments.
In addition, raw materials, sub-materials, work-in-progress, and outsourced parts are also subject to management by production control. It’s important to note that there is always the risk of omissions in arrangements and incorrect orders.
Depending on the industry, it is not uncommon for demand forecasts and defect rate assumptions to be completely unpredictable or to be made based on experience and intuition.
Production management tasks were heavily reliant on the skill of the person in charge. Personalization often caused harm to the work content. Using AI in production management attracts attention to resolve these issues. Better control and forecast accuracy will enhance production management operations. For this reason, AI is being implemented to improve production accuracy.
So, what are the benefits of using AI for production management? Let’s take a further look at the details.
Elimination of human resource shortages and dependence on individual skills
Production control operations often depend on the abilities of the person in charge, which can increase workloads. The shortage of human resources, which many companies in the manufacturing industry are suffering from, is also spurring an increase in the workload of production managers.
When the production management system is AI-powered, it can reduce the amount of manual labor needed from the manager. The use of AI helps eliminate the dependence on individual skills and reduce the burden on each person, eliminating the shortage of human resources.
Demand forecast from huge information processing
Producing production plans from demand forecasts is also an important production management task. A very high level of skill is required to make predictions based on both the experience and intuition of the person in charge based and, on the information accumulated so far.
By introducing AI, it’s possible to process vast amounts of information and data in a short period of time, and it will also be possible to create patterns for parts that previously relied on intuition without clear grounds. More information can be used for demand forecasting, and AI can be trained to further improve forecast accuracy.
Respond to needs through flexible and rapid course correction
The era of mass production of a limited number of models is over, and market needs are diversifying so that only high-mix low-volume production can meet them.
However, there are limits to resources such as personnel, location, and equipment in factories that carry out production. It is not realistic to prepare individual production lines for a wide variety of products. For this reason, it’s necessary to conduct production activities while switching the types of products to be manufactured through a detailed production plan and a flexible production system. In response to ever-changing needs, organizations must revise the production plan each time.
With the introduction of AI, the burden of correcting the course of production planning can be reduced and the accuracy improved. As needs change at an accelerating pace, the importance of AI is also increasing in order to keep pace.
Accurate and real-time information sharing
The information that should be shared across other departments is constantly changing due to changes in needs and continuous course corrections in production management operations.
As manufacturers continue to improve production efficiency, delays in information sharing in processes that require manufacturing cycle management in units of 0.1 seconds lead to the risk of creating significant waste. Therefore, many production control systems are equipped with information and data sharing functions.
Introducing AI dramatically increases the amount of information and data that can be processed. Along with this, the importance of real-time information sharing will increase.
How to use AI in your company’s production management
To utilize AI in production management, it is necessary to introduce a production management system that incorporates AI.
If you have not introduced a production control system at present, it’s important to consider implementing one. Also, if you have already introduced a production management system and do not plan to use AI in that system, you should consider switching systems.
There are several decisions that must be made to implement the new system:
- Development method
Buy a packaged system or develop a new one
- Introduction posture
Is it managed on your own computer or server, or is it managed in the cloud?
- How to switch
How to switch from existing system
You can read more about these options and how to implement them in our blog linked below.
Functions and Benefits of the Production Control System | Stratus
When introducing a new AI production management system, it is necessary to develop a function to collect data to improve efficiencies. To make good use of AI, data integrity and accuracy is essential.
It is also important to consider the sensors required to collect highly accurate data, the types of data required, and mechanisms – such as an Edge Computing platform – that collects and analyzes data in real time.
Examples of production management using AI
There are a few cases where production management operations have started with partial AI. For example, the use of AI is progressing in the following operations:
AI for production planning
Drafting a production plan can be one of the larger work loads in production management. Although conventional production management systems can organize, link, share, and visualize information, the final production planning had to be done manually.
For example, a major food company has introduced an AI production management system and leverages AI to formulate production plans. A mechanism that allows AI to learn the production planning by production management experts calculates an optimized production plan for this company.
With AI learning, it is now possible to create production plans with a high degree of accuracy that surpasses human planning.
Optimize production and inventory with AI
Many production managers tend to keep a large amount of inventory to avoid risks such as product shortages and material shortages. This risk-averse mentality is also a factor that gradually increases costs.
One company introduced an AI-powered production management system succeeded in reducing surplus inventory caused by human psychology. AI finds the optimal inventory amount, making it possible to determine the optimal production quantity and production timing.
Demand forecasts and plan changes in response to changes in the environment
Beverage sales fluctuate depending on the weather, making it difficult to forecast demand. This beverage manufacturer uses AI to streamline production management.
In the past, demand forecasting, which is complicated due external factors – including weather – could not be handled by machines and was done by human judgment. However, the introduction of AI has made this possible, allowing the system to create production plans that reflect external factors, including climate.
Quality inspection by AI improves quality level
In the inspection process, there is almost always work that must be done manually. The challenge is that it is difficult for inspectors to perform inspections with exactly the same standards. For example, in visual inspections, there is room for the subjectivity and it’s impossible to completely eliminate discrepancies in judgment. In addition, defective products may flow out due to oversight.
There is a successful use case of introducing AI-based quality control into the inspection process. The image captured by the camera device is compared with the acceptance criteria data accumulated and AI is used to automatically judge. This makes it possible to make judgments with an accuracy that far exceeds that of human inspection.
The progress of AI in production management
In this blog, we introduced what kind of changes can be created by incorporating AI into production management, and how production management operations can be made more efficient.
Production management is very complex as it manages everything related to production activities. A large amount of information must be handled, and there is a limit to the ability of workers to handle high-mix low-volume production. Therefore, attention is focused on systems that combine AI with production management systems so that machines can find optimal solutions from a vast amount of information.