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Enhancing Manufacturing Workflow with AI
The advancements in technology today has provided manufacturers with abundant opportunities to upgrade the existing systems, adapt to the changing manufacturing marketplace, and gain insights into manufacturing operations. With the genesis of artificial intelligence (AI) and machine learning, manufacturers are now embracing the new generation of technology to cope with a competitive marketplace.
Artificial intelligence has the ability to learn, adapt and improve operational efficiency and product quality, with a faster product management. The complex algorithms running in an AI program can analyze product quality goals and then benchmark it to a manufacturing workflow or a company’s internal processes.
Manufacturers often leverage artificial intelligence for production management and Define, Measure, Analyze, Improve and Control (DMAIC) processes that allow interpretation and sharing of production specific details with the customers. The large volumes of data fleeting through manufacturer systems leave production management prone to errors.
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Artificial intelligence coupled with machine learning provides an efficient solution to mitigate product mismanagement by eliminating error and automated inventory stocking. But, a bulk of inventory with sophisticated equipment comes with extensive maintenance and repairs. Artificial intelligence, however, can be coupled with sensors and monitors to accurately predict when the equipment will require maintenance and repairs.
Rather than following a fixed maintenance schedule, manufacturers incorporate AI into the Internet of Things (IoT) to access equipment information across the company and follow a maintenance schedule that is adaptive to the equipment’s needs. From automotive plant equipment to combat machinery, AI can substantially improve maintenance and repair prediction, and also predict component failures with unparalleled accuracy.
Monitoring the effectiveness of all the equipment in the field or a plant can take its toll on the workforce. With automated data gathering predictive analysis, AI delivers an enhanced Overall Equipment Effectiveness (OEE) management and establishes a baseline for the probability of equipment failures.
This probability is then compared with equipment’s real-time operation and is linked to predictive maintenance to improve asset reliability and product quality in a plant. Additionally, the manufacturing systems embedded with artificial intelligence are designed to coordinate and leverage machine learning and predictive analysis to improve plant yield rates both locally and globally.
AI streamlines the manufacturers’ data to provide accurate plant inventory information, delivery dates, sales forecast and the Work in Progress (WIP). This offers manufacturers with valuable insights into the overall status of the plant, possible enhancements, and better decision-making ability to prevail in the competitive manufacturing marketplace.
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