Mosaic Data Science uses artificial intelligence techniques and data analysis to predict future demand for products or services. Read up on how we have used demand forecasting to provide businesses with insights into the expected demand patterns to make informed decisions about production, inventory management, resource allocation, and overall business strategy.
Mosaic helped one of the US’s largest regional homebuilders deploy an artificial intelligence engine that recommends the optimal community features based on different geographic regions. The recommender delivers data-driven insights the company can trust when planning, building, and positioning new communities.
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.
Advanced analytical techniques help manufacturers reduce inventory levels of parts required in manufacturing activities while maintaining confidence that they will not run out of parts. Mosaic was tapped to help a manufacturing company in the semiconductor industry solve the complex problem of optimizing their inventory for both future orders and historical demand rates
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…
The business was investigating how to incorporate modern data science techniques into their budgeting decisions. Since they didn’t have this expertise in-house, they needed an analytics partner that could provide the right capabilities to accurately model and forecast revenue and serve these results to users in the proper context.
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.
A leading clothing manufacturer distributor and retailer of clothing realized they needed to fortify their pricing decisions with machine learning insights.
For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.
Data Science, specifically machine learning forecasting techniques, enables decision makers across a spectrum of industries to set more effective sales targets and plan profitable customer routes that boost top and bottom line growth.
In our white paper, Mosaic examines fresh machine learning based approaches to more accurately forecast airline seat demand.