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Artificial Intelligence AI by Manufacturers

Unlimited computing capacity in the cloud and real-time analytics capabilities enables manufacturers to access new insights and build systems of artificial intelligence (AI) like never before. Forward-thinking manufacturers are looking to use these capabilities to optimize their supply chain and production operations, engage their customers in powerful new ways, transform their services and products, and empower their employees through customer insights.


Elevator manufacturer ThyssenKrupp wanted to gain a competitive edge by focusing on what matters most to its customers in buildings the world over: reliability. By connecting its elevators to the cloud, gathering data from its sensors and systems, and transforming that data into valuable business intelligence, ThyssenKrupp is vastly improving operations, and offering something its competitors do not: predictive and even preemptive maintenance.

The system contains an intelligent information loop: data from elevators is fed into dynamic predictive models, which continually update datasets. Now, the elevators can actually teach technicians how to fix them, with up to 400 error codes possible on any given elevator, which can significantly sharpen efficiency in the field, resulting in dramatically increased elevator uptime.


About 20 years ago, Rolls-Royce went from manufacturing and selling engines to extending comprehensive maintenance services to the airlines that use its engines. The company’s TotalCare® Services employ a “power by the hour” model in which customers pay based on engine flying hours. The responsibility for engine reliability and maintenance rests with Rolls-Royce, which analyzes engine data to manage customers’ engine maintenance and maximize aircraft availability. This model has been highly successful for Rolls-Royce and has created relationships in which airline customers increasingly rely on the company to provide information that optimizes the costs and scheduling related to engine maintenance.

By looking at wider sets of operating data beyond their engines and using machine learning and analytics to spot subtle correlations, Rolls-Royce can optimize their models and provide insight that might improve a flight schedule or a maintenance plan and help reduce disruption for their customers. For example, by understanding the actual condition of a component versus the expected lifetime, Rolls-Royce can help their customer decide if maintenance on an aging component can be deferred to the next scheduled maintenance window versus requiring immediate maintenance. Moving to an approach based on a component’s actual condition could potentially add up to tremendous savings across a fleet by minimizing the disruption and cost of maintenance.


eSmart Systems designed an automated demand response solution that collects data from virtually any type of meter or sensor. It then runs predictive models to forecast potential capacity problems and automatically control load to buildings or other infrastructure to prevent outages. The solution provides a short-term 24-hour forecast, a long-term monthly forecast, and a temperature forecast, and it offers a centralized way to monitor and manage the entire grid. Want to learn more, contact us to speak to one of our AI Experts.

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© 2024 Softura - All Rights Reserved
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