Artificial Intelligence in Manufacturing – Improving the Bottom Line

Artificial Intelligence in Manufacturing – Improving the Bottom Line

Artificial intelligence and its practical application in the production environment

As the manufacturing industry becomes increasingly competitive, manufacturers must implement sophisticated technologies to improve productivity. Artificial intelligence or AI can be applied to various systems in manufacturing. It can recognize patterns as well as perform time-consuming and mentally challenging or human-impossible tasks. In manufacturing, it is often applied in the areas of constraint-based production planning and closed-loop processing.

AI software uses genetic algorithms to programmatically organize production schedules for the best possible outcome based on a number of constraints that are predefined by the user. These rule-based programs go through thousands of possibilities until the most optimal schedule that best meets all criteria is arrived at.

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

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

AI – Brief history

The concept of artificial intelligence has been around since the 1970s. Initially, the main goal was for computers to make decisions without the involvement of humans. But it never caught on, in part because system administrators couldn’t figure out how to use 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 implementations of AI would throw out reams of data, much of which was not sharable or adaptable to different business needs.

The Revival

AI is making a resurgence, thanks to a decade-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 assessment, come to a conclusion and take action. A neural network can recognize connections and spot trends in vast amounts of data that would not be obvious to humans. This technology is now used in expert systems for manufacturing technology.

Practical application in the real world

Some automotive companies use these expert systems to manage work processes, such as work order routing and production sequencing. Nissan and Toyota, for example, model the material flow throughout the production floor, to which the production execution system applies rules in the sequencing and coordination of production operations. Many automotive plants use rule-based technologies to optimize the flow of parts through a paint cell based on colors and consistency, thereby minimizing spray paint changes. These rule-based systems are able to generate realistic production schedules that account for the vagaries of production, customer orders, raw materials, logistics, and business strategies.

Vendors generally don’t like to refer to their AI-based scheduling 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-sounding as AI, but are more comfortable with the term “constraint-based planning.”

Constraint-based scheduling needs accurate data

A good constraint-based planning system requires correct routes that reflect the steps in the correct order, and good information about whether the steps can be parallel or whether they must be sequential. The amount of extensive planning required to launch a successful system is one of the biggest drawbacks.

If the management team has not defined and locked down accurate routes in terms of sequence of operations and overlapping of operations and if they have not properly identified resource constraints with accurate lead times and setup with a proper setup matrix, what will result with is simply very bad a final schedule that the shop cannot produce. Tools like AI should not be seen as a black box solution, but rather a tool that needs accurate data to create a feasible schedule that can be understood by users.

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

When choosing a solution, there are a number of system prerequisites that you should look for. The better an enterprise application integrates different business disciplines, the more powerful it will be in providing constraint-based planning. This means that if an application package offers functionality bundled from different products purchased from the manufacturer, it may be more difficult to use that package to provide good scheduling functionality. This is because a number of business variables that reside in non-manufacturing functionality can affect capacity.

When an ERP package is configured for constraint-based or constrained scheduling, it typically targets a scheduling server that calculates start and finish times for operations, taking into account existing orders and capacity. When the store order is fulfilled, the scheduling system updates the information about the operations and sends the results back to the corporate server.

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

More benefits of AI, constraint-based applications

Besides the immediately obvious capacity management benefits of constraint-based scheduling, there are a number of less obvious analytical possibilities. Scheduling functionality typically allows you to conduct predictive analyzes of what would happen if certain changes were made to an optimized schedule. So if an enterprise manager is pressured by a particular account executive to prioritize an order on behalf of a customer, that enterprise manager can create excellent data on how many other orders would be late as a result. In addition, this functionality can provide predictive analytics on the effect of added capacity in the plant. This allows manufacturers to see if equipment purchases will actually provide an increase in capacity or simply create a bottleneck further down the production process.

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