Artificial intelligence and its practical application in the manufacturing environment
As the manufacturing industry becomes more competitive, manufacturers must implement sophisticated technologies to improve productivity. Artificial intelligence or AI can be applied to a variety of systems in manufacturing. It can recognize patterns and perform time-consuming and mentally challenging or inhuman tasks. In manufacturing, it is widely applied in the area of constraint-based production planning and closed-loop processing.
AI software uses genetic algorithms to programmatically arrange production plans for the best possible outcome based on a set of constraints predefined by the user. These rules-based programs iterate through thousands of possibilities until the most optimal schedule is found that best meets all criteria.
Another emerging application for AI in a manufacturing environment is in process control, or closed-loop processing. In this setting, the software uses algorithms that analyze which past production runs came closest to a manufacturer’s goals for the current upcoming production run. The software then calculates the best process settings for the current job and either automatically adjusts the production settings or presents staff with a machine setting recipe that they can use to create the best possible run.
This enables increasingly efficient runs to be performed by leveraging information gathered from previous production runs. These recent advances in constraint modeling, scheduling logic, and ease of use have enabled manufacturers to achieve cost savings, reduce inventory, and increase bottom line.
AI – A short history
The concept of artificial intelligence has been around since the 1970s. Originally, the primary goal was for computers to make decisions without human intervention. But it never caught on, in part because system administrators couldn’t figure out how to use all the data. While some could understand the value of the data, it was very difficult to use, even for engineers.
In addition, the challenge of extracting data from the rudimentary databases of three decades ago was significant. Early AI implementations spewed out vast amounts of data, most of which could not be shared or scaled to different business needs.
The resurgence
AI is making a resurgence thanks to a decade-old approach called neural networks. Neural networks are based on the logical connections of the human brain. In computer terms, they are based on mathematical models that collect data based on parameters set by administrators.
Once the network is trained to recognize these parameters, it can make an assessment, draw a conclusion, and take action. A neural network can see connections and trends in vast amounts of data that would be undetectable to humans. This Technology is now used in expert systems for manufacturing engineering.
Practical application in the real world
Some automotive companies use these expert systems for work process management, such as B. Work order routing and production sequencing. For example, Nissan and Toyota model the material flow in the entire production hall, to which a production control system applies rules in order to sequence and coordinate production processes. Many automotive plants use rules-based technologies to optimize the flow of parts through a paint booth based on color and sequence, minimizing spray paint changes. These rule-based systems are able to create realistic production plans that take into account the uncertainties in manufacturing, customer orders, raw materials, logistics and business strategy.
Vendors typically don’t like to refer to their AI-based planning applications as AI because of the stigma associated with the term. Shoppers may be reluctant to spend money on something as ethereal as AI, but are more comfortable with the term “constraint-based planning.”
Constraint-based scheduling requires accurate data
A good constraint-based planning system requires correct work plans that reflect steps in the right order, and good data on whether steps can be parallel or whether they must be sequential. The amount of thorough planning required to implement a successful system is one of its major drawbacks.
If a management team has not defined and established precise work schedules in terms of operational sequence and operational overlap and if they have not correctly identified resource bottlenecks with accurate run and set-up times with a correct set-up matrix, what it ends up with is just a very poor finite schedule that the store cannot produce. Tools like AI should not be viewed as a black box solution, but as a tool that needs accurate input to create an actionable schedule that can be understood by users.
Constraint-based scheduling within an ERP system (Enterprise Resource Planning).
When choosing a solution, you need to pay attention to a number of system requirements. The better an enterprise application integrates different business disciplines, the more powerful it is in terms of providing constraint-based planning. This means that when an application suite offers functionality cobbled together from various products purchased by the manufacturer, it can be more difficult to use that suite to provide good scheduling functionality. This is because a number of business variables residing in non-manufacturing related functions can affect capacity.
Generally, when an ERP package has been configured for constraint-based or finite scheduling, it is routed to a scheduling server that calculates the start and finish times for the operations, taking into account existing orders and capacity. When the production order is executed, the MRP system updates the information about the operations and sends the results back to the company server.
The planning function within an ERP solution should work in a multi-site environment. Suppose you need to calculate a delivery date based on a cross-site, multi-level material and capacity analysis across your supply chain. The system should allow you to plan all locations in your supply chain and the actual work planned for each of these work centers. Manual or automated, you should be able to schedule work and immediately give your client a realistic idea of when the job will be complete.
Other advantages of AI, constraint-based applications
Aside from the immediately obvious capacity management benefits of constraint-based planning, there are a number of less obvious analytical capabilities. The scheduling feature typically allows you to perform predictive analysis of what would happen if specific changes were made to an optimized schedule. So when a plant manager is pressured by a particular account manager to prioritize an order on behalf of a customer, that plant manager can provide excellent data on how many other orders would be delayed as a result. In addition, this functionality can provide predictive analysis of the impact of additional capacity in the plant. This allows manufacturers to see if buying gear is really increasing capacity, or if it’s simply creating a bottleneck further down the manufacturing process.
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