Artificial Intelligence In Manufacturing – Improving The Bottom Line




Basic


Artificial Intelligence and it’s functional Application in the Manufacturing ecosystem

As the manufacturing industry becomes increasingly competitive, manufacturers need to implement complex technology to enhance productivity. Artificial intelligence, or AI, can be applied to a variety of systems in manufacturing. It can recognize patterns, plus perform time consuming and mentally challenging or humanly impossible responsibilities. In manufacturing, it is often applied in the area of restriction based production scheduling and closed loop processing.

AI software uses genetic algorithms to programatically position production schedules for the best possible outcome based on a number of constraints, which are pre-defined by the user. These rule-based programs cycle by thousands of possibilities, until the most optimal schedule is arrived at which best meets all criteria.

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

This allows for the execution of progressively more efficient runs by leveraging information collected from past production runs. These recent advances in restriction modeling, scheduling logic, and usability have allowed manufacturers to reap cost savings, reduce inventory and increase bottom line profits.


AI – A fleeting history


The concept of artificial intelligence has been around since the 1970s. Originally, the dominant goal was for computers to make decisions without any input from humans. But it never caught on, partly because system administrators couldn’t figure out how to make use of all the data. already if some could comprehend the value in the data, it was very hard to use, already for engineers.

On top of that, the challenge of extracting data from the rudimentary databases of three decades ago was meaningful. Early AI implementations would spit out reams of data, most of which wasn’t sharable or adaptive to different business needs.


The resurgence


AI is having resurgence, courtesy of a ten-year approach called neural networks. Neural networks are modeled on the logical associations made by the human brain. In computer-speak, they’re based on mathematical models that build up data based on parameters set by administrators.

Once the network is trained to recognize these parameters, it can make an evaluation, reach a conclusion and take action. A neural network can recognize relationships and identify trends in huge amounts of data that wouldn’t be apparent to humans. This technology is now being used in expert systems for manufacturing technology.


functional application in the real world


Some automotive companies are using these expert systems for work course of action management such as work order routing and production sequencing. Nissan and Toyota, for example, are modeling material flow throughout the production floor that a manufacturing execution system applies rules to in sequencing and coordinating manufacturing operations. Many automotive plants use rules-based technologies to optimize the flow of parts by a paint cell based on colors and sequencing, consequently minimizing spray-paint changeovers. These rules-based systems are able to generate realistic production schedules which explain the vagaries in manufacturing, customer orders, raw materials, logistics and business strategies.

Vendors typically don’t like to refer to their AI based scheduling applications as AI due to the fact that the phrase has some stigma associated with it. Buyers are perhaps reluctant to use money on something as ethereal sounding as AI but are more comfortable with the term “restriction based scheduling”.


restriction-based scheduling needs accurate data


A good restriction-based scheduling system requires correct routings that mirror steps in the right order, and good data on whether steps can be similar or whether they need to be sequential. The amount of thorough planning that is required for a successful system to be launched is one of the largest drawbacks.

If a management team has not defined and locked in accurate routings in terms of operation ordern and operation overlap, and if it has not correctly identified resource constraints with accurate run and set-up times with a correct set-up matrix, what it winds up with is just a very bad finite schedule that the shop cannot produce. Tools like AI should not be thought of as a black box solution, but rather as a tool that needs accurate inputs in order to produce a possible schedule that can be understood by the users.


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


In selecting a solution, there are a number of system prerequisites that you need to look for. The better an enterprise application integrates various business disciplines, the more powerful it will be in terms of delivering restriction based scheduling. This method that if an application suite offers functionality cobbled together from different products the manufacturer has purchased, it may be harder to use that suite to deliver good scheduling functionality. This is because a number of business variables that reside in non-manufacturing functionality can affect capacity.

When an ERP package has been configured for restriction based or finite scheduling, it is generally routed to a scheduling server which calculates start and finish times for the operations with consideration to existing orders and capacity. When the shop order is executed, the scheduling system updates the information regarding operations and sends the results back to the enterprise server.

Scheduling functionality within an ERP solution ought to work in a multiple-site ecosystem. Let’s say you need to calculate a delivery date based on a multi-site, multilevel examination of material in addition as capacity throughout your whole supply chain. The system should allow you to plan given 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 work and closest give your customer a realistic idea of when the order will be completed.


More benefits of AI, restriction based applications


except the closest apparent capacity management benefits of restriction based scheduling, there are a number of less obvious analytical capabilities. Scheduling functionality typically allows you to conduct predictive analyses 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 late as a consequence. Furthermore, this functionality can provide predictive analyses on the effect of additional capacity in the plant. This enables manufacturers to see if equipment purchases will truly deliver an increase in capacity, or if it will simply consequence in a bottleneck further downstream in the manufacturing course of action.




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