SpaceTime Insight’s machine learning analytics, powered by our Lateo machine learning library, predict the future probability of failure without loss of confidence. You can “see over the hill” and understand your risk of surviving an event, where other analytics may project failure. Our optimization algorithms then have more time and conditions to work with, so you can optimize purchasing, crew schedules, down times, and other maintenance activities. As a result, you extend asset life, increase your return on invested capital, and reduce maintenance and operating costs.
Provides a summary of what happened, usually for a set of predefined periods and other attributes. Useful for reviewing historical performance and understanding areas for operational improvement.
Identifies why, when, and where something happened, commonly to determine the root cause of an operational failure or unexpected event. Useful for identifying effective remediation alternatives.
Identifies a probable outcome based on assessing past behavior and likely operating conditions in the future. Useful for taking preventative measures, estimating remaining useful life, and reducing the impact of unplanned events, theft, and other scenarios.
Identifies specific actions to optimize processes, routes, schedules, and plans. Useful for planning under uncertain conditions, scheduling and positioning resources, and determining the most cost-effective routes.