Blogs
Why Airlines Need to Look at Holistic Machine Learning & Optimization Solutions to Improve Scheduling
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
Businesses have long struggled with how to move assets around the transportation network in the most efficient way. There are so many variables to contend with: shifting demand, human error, traffic, fuel costs, weather, etc. With technological advances, the amount of data companies collect on a daily basis is astounding. The trick then becomes analyzing this data to gain meaningful insights from which impactful decisions can be made.
Mosaic is uniquely qualified to help organizations glean insights from their data. We have more than a decade of experience designing and developing predictive analysis and decision support tools for NASA, the FAA, Boeing, Lockheed Martin, UPS and FedEx. Mosaic provides world-class analytics consulting to our transportation and logistics clients, applying cutting-edge machine learning and algorithm development techniques, coupled with unparalleled domain knowledge.
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
Mosaic applied our robust data analytics expertise across disciplines to develop novel, data-driven models that identify anomalous behavior for a leading aerospace government agency.
Mosaic brought its deep expertise in aviation to design and test effective machine learning models that would predict the arrival and departure assignments of flights, helping facilitate automated testing and deployment of advanced predictive decision-support tools.
Given a set of nodes & connections, which can abstract anything from transportation networks, connections between customers, knowledge graphs, or molecular structures to computer data, graph analytics provide a helpful tool to quantify & simplify the many moving parts of dynamic systems.
Mosaic helped a trucking and logistics operator optimize their machine learning deployment on Amazon Web Services (AWS). As an AWS Select Partner, Mosaic was well-positioned to deploy machine learning engineering and serverless architecture services that sped up model inference while performing a minor overhaul of the AWS architecture and code base organization.
Network science and simulation provide a robust framework for the study of network systems in all their complexity.
Mosaic built a computer vision solution to improve the process of a vehicle moving through an automated car wash.
Wielding the power of computer vision human action extraction to gain insights from video data presents an opportunity for logistics companies to make improvements for new and current customers.
Text data presents a tremendous opportunity to benefit all stakeholders of an organization – investors, employees, processes, and the all-important customer – if the organization can find a way to sift through this data in an automated way to extract key information and solve specific challenges. In that case, they could learn about their firm and start optimizing the way they operate.
Decision processes in support of jobs that either cannot be or are very difficult to automate are frequently overlooked by out of the box software providers. One such process is the creation of optimal staffing plans for outbound teams loading cartons onto trucks.