The Asset Guardian EAM (TAG)
The Asset Guardian (TAG) Mobi is the chosen preventive maintenance and asset management solution (EAM) for Microsoft Dynamics 365 Business Central, designed to deliver reliable manufacturing asset solutions that reduce risk and downtime.
TAG Mobi prevent downtime, maximize asset performance, and accelerate onboarding and training with the support of AI tools and intuitive dashboards.
No silos. No extra software. Just smooth integration and quick adoption—so maintenance teams can work faster, and managers get the data they need to make decisions.
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TRACTIAN
Tractian serves as the Industrial Copilot focused on enhancing maintenance and reliability by integrating both hardware and software to oversee asset performance, streamline industrial operations, and execute predictive maintenance approaches. The platform, powered by AI, enables companies to avert unexpected equipment failures and improve production efficiency. Headquartered in Atlanta, GA, Tractian also has a global footprint with branches in Mexico City and Sao Paulo, thereby expanding its reach. For more information, you can visit their website at tractian.com, where additional resources and details about their offerings are available.
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UpKeep
UpKeep is a mobile-centric application designed for the maintenance of facilities and equipment, relied upon by numerous major corporations globally. This software empowers asset and facility management teams to enhance data accuracy and collaborate effectively, leading to increased productivity levels. It offers features that enable users to generate work orders while on the move, maintain oversight of ongoing and future work orders, and assess the condition of various locations. Additionally, UpKeep's user-friendly interface facilitates quick access to essential information, further streamlining maintenance processes.
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Aspen Mtell
Recognizing patterns in operational data is essential for anticipating deterioration and potential malfunctions well in advance. By implementing precise failure pattern identification, organizations can significantly reduce the occurrence of false positives that are often problematic in model-based methodologies. The use of advanced machine learning approaches enables a rapid differentiation between typical and atypical behaviors, which can lead to the activation of equipment protection measures within weeks rather than months. Additionally, the collaboration between Aspen Mtell and Aspen Cloud Connect™ allows for seamless access to devices operating under OPC UA, further enhancing analytical capabilities. This integration serves as a crucial line of defense against asset degradation by identifying early indicators of failure through operational data analysis. Moreover, incorporating AI-driven agent development improves current maintenance practices, enabling swift deployment of autonomous agents across multiple locations or even throughout an entire organization. With the focus on accurate failure pattern detection, businesses can greatly minimize the frequency of false positives often seen in conventional model-based approaches. By utilizing streamlined machine learning techniques, companies can quickly identify and respond to both standard and irregular activities, ensuring robust protection for their equipment and optimizing operational efficiency. This proactive approach ultimately fosters resilience and reliability in asset management strategies.
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