The Role of Artificial Intelligence in Process Manufacturing
Introduction
Process manufacturing companies are often faced with complex operational processes that require close monitoring and control to maintain optimal performance. This is where artificial intelligence (AI) comes in. AI can help with the optimization of processes and the reduction of operational costs, among other things. In this post, we'll explore how AI can be used in process manufacturing, including the theory of constraints, and why it is important for CIOs and VPs of IT to continue fine-tuning this technology.
What is Process Manufacturing?
Process manufacturing refers to the production of goods by combining ingredients through a series of process steps. It is used primarily in the chemical, food and beverage, pharmaceutical, and biotechnology industries. Unlike discrete manufacturing where a product is made by assembling individual parts or components, process manufacturing involves continuous processes, where the product is made by mixing and transforming raw materials. Examples of process manufacturing are plastic or aluminum extraction, fertilizer cooking, or roof shingle manufacturing. Each of these processes involves a number of variables that drive the quality and efficacy of the end product.
The Theory of Constraints
One of the leading theories in manufacturing optimization is Eli Goldratt’s theory of constraints (more about it here). In process manufacturing, the theory of constraints (TOC) plays a critical role in identifying bottlenecks that impede efficiency and productivity. The TOC is a methodology that aims to improve overall system performance by identifying and addressing constraints that limit throughput. With the growth of real-time sensor data, AI can be used to support the TOC by capturing, analyzing, and visualizing data enabling employees to react faster to anomalies. The premise of TOC is to optimize the entire process and focus on the most expensive steps and optimize for those even if it means un-optimizing other areas. This is a perfect use case for a machine learning model that can map and monitor a large number of variables and identify process steps that human operators might otherwise ignore.
The Advantages of AI in Process Manufacturing
One of the key advantages of AI is its ability to optimize production processes. By analyzing sensor data in real-time, AI can identify performance issues and recommend corrective actions to minimize downtime and maximize productivity. Additionally, AI can improve product quality by detecting errors before they occur, reducing the number of rejected batches, and minimizing waste.
For instance, in plastic extrusion, the ML model can monitor a multitude of variables such as screw temperature, speed of the line, raw material metadata, and even the human aspect of who is running the line. With all the data, the model can predict potential issues with quality, the likelihood of breakage, bubbling, etc. Armed with predicted outcomes, the manufacturing team can optimize the line variables to improve the quality and throughput. A Finnish company Quva has been leading the way here.
In a fertilizer plant, the most expensive part of the end product is the compounds (Ammonia, Urea, Phosphorus, etc.) in the recipe while the granule filler is relatively speaking lower cost. Optimizing the process recipe based on chemical metadata of the compounds (e.g. source country & plant) the ML model can predict the right speeds and volume to optimize the end product based on the characteristics of the raw materials.
Another example of AI/ML helping manufacturing is computer vision. This is even used to verify the consistency of pizza pies in certain chains! In essence, the process includes one or several cameras that constantly image the product moving through the manufacturing process and it’s comparing the input to the machine learning model that has been trained on known defects. Once the AI system identifies one of the known defects, it can signal the process control and potentially stop the line or adjust process variables to mitigate the identified defect. This approach can save a tremendous amount of time but also allow critical human resources to focus on the essentials rather than watching the line or taking batch quality assurance (QA) samples.
AI can also help reduce operational costs by optimizing machine utilization and reducing energy consumption. By analyzing operational data, AI can identify areas where energy consumption can be reduced without affecting production output. This can help manufacturers realize significant cost savings.
Conclusion
As companies continue to look for ways to improve their operational processes, AI is becoming increasingly important. In process manufacturing, AI can help optimize production processes, reduce operational costs, and improve product quality. Its ability to support the theory of constraints is particularly valuable, as it can help manufacturers avoid potential bottlenecks and improve overall system performance. For CIOs and VPs of IT, it's essential to start considering AI as a tool for driving operational efficiency and maintaining a competitive edge.
