Concerning dynamic adaptability and operational improvement, AI can outperform traditional decision-support technologies. Besides, with high-performance software tools and processing power, AI enables businesses to cost-effectively create and uphold their algorithms and intellectual property in-house that is cheaper, versatile, and more adaptive to continuously changing market conditions and equipment.

FREMONT, CA: At present, Artificial Intelligence (AI) is commonplace. Fitness apps, navigation systems in cars, virtual assistants, weather forecasting, and high-speed stock trading are among existing must-have AI applications. Currently, even manufacturers with heavy assets comprising cement companies are launching pilot projects to decide if and how AI might do good to their operations.

AI offers a less costly option by enabling companies to use their existing software to evaluate the vast amount of data they customarily collect and, at the same time, tailor their decisions. In doing so, they obtain a better understanding of today’s developing technologies and the value they deliver.

Application of AI for Manufacturers with Heavy Assets

For years, businesses have been digitizing their plants with distributed and administrative control systems and, in some cases, superior process controls. While this has significantly enhanced visualizations for operators, many companies with heavy assets have not kept up with the newest advances in decision-support and analytics solutions that apply AI.

Operators still rely on their experience, instinct, and judgment. For instance, present-day downsized teams of control-room operators are anticipated to manually supervise a multitude of signals on various screens and adjust settings as required. At the same time, they ought to troubleshoot and run tests and trials. Therefore, many operators take shortcuts and prioritize pressing activities that do not substantially add value.

The heavy reliance on experience makes it intricate to replace a highly skilled operator at retirement. Since variations in workers’ qualifications can influence not only performance but also profits, AI’s capability to preserve, develop, and standardize knowledge is all the more essential. Additionally, since it can make complex operational set-point decisions on its own, AI reliably delivers predictable and consistent output in markets that have trouble attracting and retaining operator talent.

Concerning dynamic adaptability and operational improvement, AI can outperform traditional decision-support technologies. Besides, with high-performance software tools and processing power, AI enables businesses to cost-effectively create and uphold their algorithms and intellectual property in-house that is cheaper, versatile, and more adaptive to continuously changing market conditions and equipment. Additionally, AI can fully automate complex tasks and offer uninterrupted and precise optimum set points in autopilot mode.