Skytree for Asset Intensive Industries

Machine learning can increase the value and operational efficiencies of complex equipment, proactively detect potential failures and increase operational readiness of complex systems.

Leading companies in transportation, logistics, energy, and utilities are using Skytree to solve some of their most complex problems. One of the most common in the transportation and logistics industry is developing optimized preventive maintenance schedules of their fleets to maximize operational efficiencies.

Using Skytree software, a logistics service company can analyze brake wear, idle time, average speed, gas usage, ambient temperature and other data sources that predict potential failure of transportation systems — enabling proactively scheduled maintenance to minimize disruptions.

Skytree Machine Learning delivers advanced predictive and prescriptive maintenance analytics that:

  • Detect potential failure of equipment through analysis of sensors and operating environment
  • Analyze underlying causes of parts failure
  • Identify defects in the manufacturing process
  • Develop efficient preventive maintenance schedules

Examples of High Value Analytics Use Cases

asset-intensive

Resources