Mosaic Data Science’s supply chain optimization solutions are designed to streamline operations, from sourcing and production to distribution and delivery. Our team has ample experience helping customers minimize costs, reduce delays, and enhance overall performance.
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.
Mosaic helped a nationwide retailer model the price elasticity of demand across their product catalog, empowering them to optimize prices and maximize SKU revenue.
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.
We explore how ML & optimization can aid the risk assessment and management process with an eye towards robustness and resiliency.
Network science and simulation provide a robust framework for the study of network systems in all their complexity.
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.
Summary Mosaic helped an energy company optimize its hydrocarbon inventory management process by working with them to develop a centralized system to collect and control inventory data quality, improve accuracy, and improve the bottom line. Why Intelligent Inventory Management Matters in the Oil & Gas Sector The oil and gas Read more…
Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.