Systems and methods for predicting and managing tool assets

Abstract

A scalable digital fleet management system may be leveraged by organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. An end-to-end solution spans all the way from gathering live tool data to visual representations of tool health. Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes and help improve scheduling of procurement and distribution of the fleet. Framework deployment focuses on topics related to developing an end-to-end solution architecture, scaling, and deploying it on demand, and maintaining it going forward. Sustenance and algorithm development focuses on defining a risk index, developing machine learning algorithms for different tools, defining a single comparison metric, and deciding when to trigger automated re-training for each tool.