WHICH BUSINESS CASE IS BETTER SOLVED BY ARTIFICIAL INTELLIGENCE?

Which Business Case Is Better Solved By Artificial Intelligence?

Which Business Case Is Better Solved By Artificial Intelligence?

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Business Case Better Solved by Artificial Intelligence: Predictive Maintenance in Manufacturing

Overview:


Predictive maintenance involves the use of AI to monitor the condition of machinery and equipment in real-time to predict potential failures before they happen, allowing businesses to schedule maintenance proactively.

Why AI is a Better Solution:



  1. Data-Driven Insights: AI can analyze vast amounts of data from sensors on machinery, including vibration, temperature, pressure, and other metrics. Machine learning models can detect patterns and anomalies that would be too complex for humans to identify, enabling accurate predictions of when maintenance is needed.

  2. Cost Savings: By preventing unexpected breakdowns, AI helps reduce downtime and the costs associated with emergency repairs. It also helps optimize maintenance schedules, reducing unnecessary routine checks, and focusing efforts only where and when they're truly needed.

  3. Increased Equipment Lifespan: AI-driven maintenance schedules ensure that equipment is maintained at optimal intervals, preventing overuse and underuse, which can both lead to faster wear and tear. This increases the overall lifespan of expensive machinery.

  4. Real-Time Monitoring and Efficiency: AI systems can continuously monitor machinery in real time, whereas traditional maintenance might involve periodic inspections that can miss early signs of wear. AI offers immediate detection and alerts to operators, ensuring a faster response.

  5. Scalability: AI systems can be scaled across large manufacturing plants with hundreds or thousands of machines, efficiently handling maintenance at scale. In contrast, manual monitoring and maintenance would require large teams, making it less efficient.


Example:


General Electric (GE) uses AI in their industrial products to provide predictive maintenance to clients in sectors like aviation and energy, helping them save millions by reducing unplanned downtime.

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