Artificial Intelligence in Manufacturing: Improving Profitability
Artificial Intelligence and its Practical Applications in Manufacturing
Manufacturers must implement sophisticated technology to improve productivity as the industry becomes more competitive. Artificial intelligence, or AI, can be applied to various manufacturing systems. It can recognize patterns and perform time-consuming, mentally challenging, or impossible tasks for humans. It is frequently used in manufacturing for constraint-based production scheduling and closed-loop processing. Obtain the Best information about Cognitive computing services.
AI software employs genetic algorithms to programmatically arrange production schedules for the best possible outcome based on user-defined constraints. These rule-based programs cycle through thousands of possibilities until the most optimal plan meets all criteria.
Another emerging application of AI in manufacturing is process control, also known as closed-loop processing. In this case, the software employs algorithms to determine which previous production runs came closest to meeting a manufacturer’s goals for the upcoming production run. The software then calculates the best process settings for the current job and automatically adjusts production settings or presents staff with a machine-setting recipe to create the best possible run.
This enables the execution of progressively more efficient runs by leveraging data from previous production runs. Recent advances in constraint modeling, scheduling logic, and usability have allowed manufacturers to save money, reduce inventory, and increase profits.
A Brief History of Artificial Intelligence
Artificial intelligence has been discussed since the 1970s. Initially, the primary goal was for computers to make decisions without human intervention. But it never took off, in part because system administrators couldn’t figure out how to use all of the data. Even though some could see the value in the data, it wasn’t easy to use, even for engineers.
Furthermore, the difficulty of extracting data from the primitive databases of three decades ago was significant. Early AI implementations would generate massive amounts of data, most of which were not sharable or adaptable to changing business requirements.
AI is making a comeback, thanks to a ten-year approach known as neural networks. The human brain’s logical associations are used to model neural networks. They are based on mathematical models that collect data based on parameters set by administrators.
Once trained to recognize these parameters, the network can perform an evaluation, reach a conclusion, and take action. A neural network can recognize relationships and detect trends in massive amounts of data that humans would miss. This technology is now being used in manufacturing expert systems.
Some automotive companies use these expert work-process management systems, such as work order routing and production sequencing. Nissan and Toyota, for example, are modeling material flow across the manufacturing floor, which a manufacturing execution system uses to sequence and coordinate manufacturing operations. In addition, many automakers use rules-based technologies to optimize the flow of parts through a paint cell based on color and sequencing, reducing spray-paint changeovers. These rule-based systems can generate realistic production schedules that account for manufacturing variances, customer orders, raw materials, logistics, and business strategies.
Vendors typically avoid referring to their AI-based scheduling applications as AI because the term has a negative connotation. Buyers may hesitate to spend money on something as ethereal as AI, but they are more familiar with “constraint-based scheduling.”
Data accuracy is required for constraint-based scheduling.
A sound constraint-based scheduling system necessitates correct routings that reflect steps in the proper order and accurate data on whether steps can be parallelized or must be sequential. Unfortunately, one of the most significant disadvantages is the extensive planning required for a successful system launch.
If a management team does not define and lock in accurate routings in terms of operation sequence and operation overlap, and if it does not correctly identify resource constraints with exact run and set-up times and a correct set-up matrix, the result is simply a terrible finite schedule that the shop cannot produce. AI should not be viewed as a black-box solution but rather as a tool that requires accurate inputs to have a feasible schedule that users can understand.
Constraint-based scheduling is used in an ERP (enterprise resource planning) system.
Several system prerequisites must be considered when selecting a solution. First, the better an enterprise application integrates different business disciplines, the more powerful it is at delivering constraint-based scheduling. If an application suite offers functionality cobbled together from various products purchased by the manufacturer, it may be more challenging to use that suite to deliver good scheduling functionality. This is because a variety of business variables found in non-manufacturing functionality can have an impact on capacity.
When an ERP package is configured for constraint-based or finite scheduling, it is typically routed to a scheduling server, which computes start and finish times for operations while considering existing orders and capacity. When the shop order is executed, the scheduling system updates the operations information and returns the results to the enterprise server.
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