Artificial Intelligence in Manufacturing – Improving the Bottom Line

Artificial intelligence and its practical application in the manufacturing environment

As the manufacturing industry becomes increasingly competitive, manufacturers need to implement sophisticated technology to improve productivity. Artificial intelligence, or AI, can be applied to a variety of systems in manufacturing. You can recognize patterns, as well as perform tasks that are time consuming and mentally challenging or humanly impossible. In manufacturing, it is often applied in the area of ​​constraint-based production scheduling and closed-loop processing.

The AI ​​software uses genetic algorithms to programmatically organize production schedules to obtain the best possible result based on a series of constraints, which are predefined by the user. These rule-based programs loop through thousands of possibilities, until the most optimal program that best meets all the criteria is arrived at.

Another emerging application of AI in a manufacturing environment is process control or closed-loop processing. In this configuration, the software uses algorithms that analyze which previous production runs were closest to meeting the manufacturer’s targets for the current pending production run. The software then calculates the best process setup for the current job and automatically adjusts production settings or presents a machine setup recipe to staff that they can use to create the best possible run.

This enables the execution of progressively more efficient runs by leveraging information gathered from previous production runs. These recent advances in constraint modeling, programming logic, and usability have enabled manufacturers to save costs, reduce inventory, and increase bottom line profits.

AI – A Brief History

The concept of artificial intelligence has been around since the 1970s. Originally, the main goal was for computers to make decisions without human input. But it never caught on, partly because system administrators didn’t know how to make use of all the data. Even if some could understand the value of the data, it was very difficult to use, even for engineers.

On top of that, the challenge of extracting data from the rudimentary databases of three decades ago was significant. Early AI implementations would yield reams of data, most of it unshareable and unsuitable for different business needs.

the resurgence

AI is making a comeback thanks to a ten-year-old approach called neural networks. Neural networks are modeled on the logical associations made by the human brain. In computer language, they are based on mathematical models that accumulate data based on parameters set by administrators.

Once the network is trained to recognize these parameters, it can make an evaluation, come to a conclusion, and take action. A neural network can recognize relationships and detect trends in large amounts of data that would not be apparent to humans. This technology is now being used in expert systems for manufacturing technology.

Practical application in the real world.

Some automotive companies are using these expert systems for work process management, such as work order routing and production sequencing. Nissan and Toyota, for example, are modeling the flow of materials across the entire production floor to which a manufacturing execution system applies rules to sequence and coordinate manufacturing operations. Many auto plants use rule-based technologies to optimize the flow of parts through a paint cell based on colors and sequence, minimizing spray paint changes. These rule-based systems can generate realistic production schedules that take into account the vagaries in manufacturing, customer orders, raw materials, logistics, and business strategies.

Vendors generally do not like to refer to their AI-based programming applications as AI due to the fact that the phrase has a certain stigma attached to it. Buyers may be reluctant to spend money on something as ethereal as AI, but are more comfortable with the term “constraint-based programming.”

Constraint-based programming needs accurate data

A good constraint-based programming system requires correct paths that reflect the steps in the correct order, and good data on whether the steps can be parallel or whether they must be sequential. The amount of painstaking planning that goes into launching a successful system is one of the biggest drawbacks.

If a management team has not defined and locked down precise paths in terms of sequence of operations and overlap of operations, and if they have not correctly identified resource constraints with accurate configuration and execution times with a correct configuration matrix, what ends up being? with is just a very bad finite program that the shop can’t produce. Tools like AI should not be viewed as a black box solution, but as a tool that needs precise inputs to produce a feasible schedule that users can understand.

Constraint-based scheduling within an ERP (enterprise resource planning) system

When selecting a solution, there are a number of system prerequisites to look for. The better an enterprise application integrates multiple business disciplines, the more powerful it will be in terms of constraint-based scheduling delivery. This means that if a suite offers cobbled together functionality from different products the manufacturer has purchased, it may be more difficult to use that suite to provide good programming functionality. This is because a number of business variables residing in non-manufacturing functionality can affect capability.

When an ERP package has been configured for finite or constraint-based scheduling, it is typically routed to a scheduling server that calculates start and finish times for operations based on existing orders and capacity. When the purchase order is executed, the scheduling system updates the information about the operations and sends the results to the company’s server.

The scheduling functionality within an ERP solution should work in a multi-site environment. Let’s say you need to calculate a delivery date based on an analysis of multi-site and multi-tier materials, as well as capacity throughout your entire supply chain. The system should allow you to plan all the sites in your supply chain and the actual work scheduled for each of those work centers. Manually or automatically, you should be able to schedule the work and immediately give your customer a realistic idea of ​​when the order will be completed.

More AI Benefits, Constraint-Based Applications

Aside from the immediately apparent capacity management benefits of constraint-based scheduling, there are a number of less obvious analytical capabilities. Scheduling functionality typically allows you to perform predictive analysis of what would happen if certain changes are made to an optimized schedule. So if a plant manager is pressured by a particular account executive to prioritize an order on behalf of a customer, that plant manager can produce excellent data on how many other orders would be delayed as a result. Additionally, this functionality can provide predictive analytics on the effect of additional capacity on the plant. This allows manufacturers to see if equipment purchases will actually lead to an increase in capacity, or simply result in a bottleneck later in the manufacturing process.