Global external shocks are going to continue to happen, that is a fact of operating a business in today’s environment. As companies embrace data science in their decision-making processes, they are better positioned to deal with these disruptions, allowing them to manage a risk-optimized supply chain.
Designing and deploying machine vision is a powerful technology that humans can employ to improve their decision making. These deep learning and AI techniques are not easily developed, and trained data scientists need to be involved in the translation of analytics to business insights.
Computer vision is a powerful AI technique with vast business applications. In this white paper, Mosaic examines how to use machine vision for detecting airport layouts from the sky.
In our white paper, Mosaic examines fresh machine learning based approaches to more accurately forecast airline seat demand.
Optimizing how airplanes take off is an ideal use case for fusing IoT and predictive analytics.
We have needed to be able to predict how long a flight will take to fly its trajectory. Quite often, it has been adequate and possible to use the outputs of one of our predictive analysis tools for this purpose. It predicts both the arrival time (ETA) as well as some intermediate times that we have used in a variety of other places.
Being able to accept machine learning outputs in the decision making process is critically important, especially in Air Traffic decisions.
This case study examines how Mosaic helped NASA and the FAA dynamically balance airspace capacity & demand.
We built a predictive machine learning model that incorporates weather forecasts and air traffic movements to provide decision support to air traffic controllers.
Mosaic built custom ML & AI models for a leading express shipping company to optimize their overnight shipping operations.