Driving industrial operations on a factory floor with AI-driven technologies has resulted in manufacturers reaping in numerous benefits. Quality control is one area that has been definitely impacted with the deployment of AI models in place replicating humans. Instant defect detection, reduced industrial waste, and top-notch product performance have been some of the driving factors in adopting AI. Instead of relying on age-old manufacturing practices, the industrial units have started relying on smarter approaches employed by AI-powered industrial agents not just for quality but for every other requirement. It doesn’t come as a surprise when knowing about their valuable contribution in designing, manufacturing, and maintaining as well. The future holds to be bright because ready collaborations could be made with other departments like engineering, and logistics. A definitive impact could be made once you hire inventory software development experts from BOSC Tech Labs today with efficiency and affordability.
Is an AI-powered Industrial Agent Just another AI Model?
Absolutely, not! There is a world of difference when comparing an AI industrial agent with another similar AI model. Though the technology involving algorithms and machine-learning programs might be the same, an AI agent for industrial purposes is meant to be different on operational terms. There is every reason for a manufacturer to consider this shift because factory floors have become highly dynamic in nature with increased speed in operations and shorter delivery times. Agile operational environments has become the order of the day surpassing manual capacities paving the way for automated systems. Ever since, the contribution of such agents have been immense, playing a significant role in the success of any manufacturing unit. Given below are a few instances.
Problem-solving
Agentive AIs get to the grass-root levels of any industrial problem in an instance with a shorter response time or almost instantly in most of the cases. As they have been programmed to handle issues arising in such an environment, they make a huge difference when compared with other models. Even mission-critical issues too are addressed as they make use of their analytical capabilities by retrieving data produced by powerful sensors. Another way of looking at problem-solving proactively is implementing predictive maintenance as it saves a lot of production time and improves overall operating efficiency of the systems involved. Decoding how this impacts the internal processes will lead to interesting observations.
- Design – Identify faulty designs and create newer ones from scratch before moving to production.
- Implementation – Alert all the stakeholders about the latest changes for a smoother and faster workflow.
Decision-making
AI industrial agents step away from routine approaches to resolving problems on the floors. For instance, there are several situations during which instant decisions are to be made to ensure uninterrupted operations in a unit. Sudden breakdown in a machine during operation has to be identified and the necessary replacements have to be suggested. The entire process has to be monitored thoroughly to avoid any further lapses. All these activities of which some are sequential and others concurrent in nature have to be executed with perfection, which is a near impossibility with manual involvement alone.
- Insights – Discover the latest updates and implement the suggestions based on the reports.
- Predictions – Implement preventive measures by analyzing the probability of an equipment’s failure.
Managing day-to-day operations by maintaining the machinery in a consistent manner is what the AI-based agentive models are trained for.
Modern factories rely a lot on AutoCAD designs and log reports to perform in a full-fledged way. The entire system may function faultily with the slightest of the errors detected in the design or operation. Though it might not be possible to identify and rectify for humans owing to fatigue or lack of concentration, the same is not the case with industrially-trained AI models.
Major Tools for Predictive Maintenance – The Key for Smoother Operations in Manufacturing
Agentive AI is instrumental in determining when a machine might fail due to the lack of maintenance. Timely suggestions are given based on a detailed analysis made backed by valuable reports generated at regular intervals. Some of the popular tools ideal for predictive maintenance are listed below.
Hitachi Vantara
Estimating when an asset might fail or function to its full capacity will help in maintaining the entire production system successfully. This tool helps in monitoring all the assets at stake without or least human involvement. As a result, the uptime of the entire manufacturing infrastructure is improved drastically. Scheduling new maintenance tasks is one of the unique features that it can offer to the production managers.
Intel IoT
Aiding optimum interconnectivity among all the entities existing in a system is what it is all about – the Internet of Things (IoT). Considering that the concept has been there for decades, it has been empowered with AI giving it renewed and powerful features of communication. By using Intel for IoT, it is possible to administer all the production activities from a closer perspective. Eventually, it is also possible to avoid unintended downtimes, which is a blessing in disguise where production is the crux.
Fero Labs
A clear differentiation made between asset monitoring and predictive maintenance in a factory leads to different scheduling approaches in contrast. The intention is to introduce production companies a wide range of options when it comes to reviving the industrial operations. At Fero, the focus is on optimizing the capacity of machines by defining new standards to them in terms of quality, consistency, and durability.
The list of predictive maintenance tools doesn’t end with these but consists of several others like Fluke Corporation, Cisco IoT, and Stottler Henke to name further. It would be interesting to note that there are numerous changes still to occur with active contributions from them. Irrespective of the tool chosen, the ultimate goal is to make the most of AI in the Manufacturing sector. Across the world, the trends have been significant so far with positive signs such as increased productivity and maximum efficiency.
Use Cases in Manufacturing
1. Optimized Production in Automobile Sector
AI-driven industrial agents are equipped with multiple resources such as reports and insights availed from various sources spanning a long time. Improved decision-making is possible allowing manufacturers to set precise operational settings for a specific production activity on the floor. Eventually, this calculated streamlining of vital operations leads to reduction in overheads.
A reputed automobile manufacturer has employed an AI agent to manage the whole life cycle of operations in one of their units. It all started from the design phase. Based on all the specifications fed to the agent, the best designs have been made available along with pros and cons of each. It has resulted in the manufacturing of cars that are not just sleek in design but also efficient enough in fuel consumption. Satisfied with the output produced, the AI-based agent is being considered for other phases.
2. Automated Operations in Aerospace Segment
Aeronautics depends a lot on 3D designs to ensure the development of any equipment or product is of uncompromising quality. It takes painfully longer durations to evolve at the ideal design if based on manual efforts. The additional burden will be of identifying and rectifying any possible errors in the design. There are instances wherein the design has been approved and moved to production without actually evaluating completely.
Precise operational excellence is the driving force behind the aerospace companies. One of such companies in this space was facing severe complications because of substantial designs. The introduction of an AI-based industrial agent has changed the scenario completely with sophisticated designs developed reducing inventory. As a result, there has been a 50% reduction in production time and cost.
Evaluating the features and benefits of AI agents in manufacturing might display a rosy picture of process improvisation and production optimization. However, the case is not meant to be the same because manufacturers have to undergo a lot of details before deploying such an agent.
3. The Path Ahead
Today’s manufacturers are required to make thoughtful decisions based on enough research and detailed study. Ready deployment of a random AI agent for manufacturing might backfire because of the inconsistency in protocols. The ideal way suggested is to begin slowly and introduce the agent to one particular phase. Under normal conditions, it is suggested that the AI features could be employed for designing a prototype. Based on the subsequent evaluations, the agent can be reintroduced into the system with minor modifications.
Strategic changes can be brought in with the adoption of AI-controlled industrial agents, which is meant to bring down the production costs. Manufacturers need to understand that sustainability is important as adaptation. There are other notable benefits derived later such as avoiding downtimes ensuring that the production doesn’t come to a complete halt. Also, the process of uncovering operational inefficiencies will lead to boosting productivity levels greatly.
Conclusion
AI-powered industrial agents in manufacturing are expected to make breakthrough contributions in an industry where precision and productivity go hand-in-hand. Industrial AI-centric implementations serve as a testimony to the renewed interest of engineering heads for their manufacturing needs. It’s the exclusive benefits brought in by the purpose-built agents that are considered to be the game changers. The inspirational journey of Aveva in adopting AI saving more money for Duke Energy stresses on this importance.
