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Analyze near real-time operational data; assess asset health

IBM IoT Equipment Advisor augments your asset health monitoring processes with machine learning techniques and cognitive methodologies. This powerful combination provides asset-intensive industries with the capability to analyze near real-time operational data and derive an asset health assessment that can be used to predict future asset performance. Cognitive methods are applied to unstructured data associated with repairs, maintenance, procedures and techniques to offer enhanced insights and recommend optimum repair methods and procedures. It enables equipment manufacturers to detect failure patterns, improve process and determine risk.

Reduce unscheduled maintenance

Harvesting best practices and technical expertise helps maintenance personnel to become better at predicting asset failure and degradation.

Optimize for operational improvement

Constantly evaluate asset operating performance to look for alternate settings or operations to improve asset availability and reduce maintenance costs.

Increase repair efficiency

Increase efficiency of repair up to 10%. Better first-time fix rates and improve time to resolve manufacturing issues by identifying the best methods to address specific maintenance needs.

Diagnose and resolve the problem

Use analytics to better diagnose reasons for asset failure. Intelligent assets generate detailed operational data that can be captured and analyzed to identify the source of the problem.

Key features of IBM IoT Equipment Advisor

  • Gather real-time data from your equipment
  • Diagnose issues with predictive warning of failure
  • Integrate with asset management system; optimize work orders
  • Improve response, lower costs with step-by-step guidance
  • Continuously optimize the use of systems, equipment, people

Security and privacy in the cloud

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