5 examples of the power of AI in manufacturing optimization

The benefits they’ve found from automation include a reduction in operational costs by up to 40%; an increase in the manufacturer’s control over processes; improved employee performance; and significantly lower downtime. Predictive maintenance analyzes data from connected equipment and production equipment to determine when maintenance is needed. Using predictive maintenance technology helps businesses lower maintenance costs and avoid unexpected production downtime. While manufacturing companies use cobots on the front lines of production, robotic process automation (RPA) software is more useful in the back office. RPA software is capable of handling high-volume or repetitious tasks, transferring data across systems, queries, calculations and record maintenance. Even in the face of ongoing change, AI can significantly help keep your manufacturing business running.

AspenTech research shows that 83% of large industrial companies believe that AI can produce better results. It also suggests that domain expertise is core for adopting AI models into the manufacturing industry. AI in manufacturing is, adding advanced technology to the current manufacturing process.

How to Mitigate the Risks of AI

Data-intensive tasks historical data sets can be involved in process optimization. It is difficult to determine which process variables result in the best product quality. Numerous Designs of Experiments are often conducted by manufacturing and quality experts to optimize process parameters, but they are frequently expensive and time-consuming. A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended. Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their digital transformation.

Besides, adopting AI in an IoT-based infrastructure improves visibility into energy assets and enables predictive maintenance. This allows energy companies to avoid unexpected downtimes, maintenance costs, and outages. For example, some startups offer workflow optimization solutions that promptly identify delays and failures in pick-and-place machines that affect delivery times. Such solutions enable logistics companies to avoid package mishandling and maltreatment, improve inventory management, and enhance warehouse efficiency. AI in logistics, thus, ensures on-time delivery of packages and boosts revenues. Signatrix is a German startup that provides in-store visual intelligence.

Machine Learning, Neural Networks, and Deep Learning

Using the AI, the manufacturers can answer the “what if” question in no time – all they need is an extensive, quality dataset. Once it occurs, the manufacturing capacities of the factory shrink or even drop to zero, causing financial damage. Even the shortest production stoppage may result in lowered quality, making the first batch of the product unsuitable for the market. To improve the current repeatable batch production processes, a producer of pharmaceuticals approached us to implement AI models and utilize predictive modeling. IBM functions through five key segments, namely, Cognitive Solutions, Technology Services & Cloud Platforms, Global Business Services, Systems, and Global Financing. The company manufactures and sells hardware & software and delivers numerous hosting and consulting services from mainframe processors to nanotechnology domains.

LinePulse provides these capabilities for automotive manufacturers, and displays all relevant manufacturing quality data on a centralized dashboard. It enables them to increase visibility into operations and use advanced analytics to drive predictive maintenance. Consequently, manufacturing companies improve production efficiency, reduce waste, decrease labor costs, and save operational costs. One of the biggest challenges in implementing Artificial Intelligence (AI) applications in manufacturing is determining how to deploy and use it most effectively.

Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding. Starting from automation to decision making, this company helps multinational companies. If any production facility plans to work continue round the clock, they need to create different work shifts. It will result in a large amount of reduction in regular maintenance efforts, annual maintenance costs, and part maintenance.

Additionally, AI-driven technologies are capable of detecting potential inefficiencies in equipment or processes before they become a problem. In this article, we will explore some examples of how AI is being utilized within the manufacturing industry to drive cost savings and improved profitability. Artificial intelligence has had a profound impact on the manufacturing industry. AI technology can be used to automate repetitive tasks, freeing up time and labor costs for manufacturers. Additionally, automated processes utilizing AI allow machines to quickly identify product defects without waiting for manual inspections, improving quality control while also reducing waste.

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