Mosaic builds robust supply chain optimization solutions powered by artificial intelligence and machine learning.

Intelligent Supply Chain Management

According to Gartner, 85% of businesses are still relatively immature in adopting analytics into their workflow. Companies who have resisted the wave of digital transformation are not only at a significant disadvantage of losing customers, but they are at extreme risk of closing their doors because they refuse to rethink operating models and leverage data and analytics to inform adaptations. Data analytics can deliver actionable insights incredibly quickly, and even if executives aren’t familiar with techniques, machine learning & predictive analytics can drive insights from data streams within weeks, not years.

Inventory optimization is a complicated component of supply chain management that is vulnerable to many internal and external factors. It involves having the right inventory to meet your demand, and buffer against unexpected disruption, while avoiding wasteful surplus. A successfully optimized inventory process will accurately forecast demand and respond quickly to both risks and opportunities.

Mosaic Data Science helps our customers futureproof their supply chains by leveraging data techniques like machine learning, mathematical optimization, deep learning, and agile software development to build custom applications that help everyone from hotels to energy companies make better supply/demand decisions. 

Advanced Industrial Inventory Management Analytics

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

Integrated Machine Learning and Mathematical Optimization

For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO...

Combat Supply Chain Disruptions with Data Science

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.

Price Optimization for Major Clearance Sales

A leading clothing manufacturer distributor and retailer of clothing realized they needed to fortify their pricing decisions with machine learning insights.

AI-Enabled Retail Inventory Management

Retail inventory optimization is a great candidate use case to apply machine learning & deliver immediate business value.

Oil Terminal Inventory Imbalance Prediction

Oil & gas firms have a tremendous opportunity to refresh demand forecasts with ML techniques, improving accuracy and adding to the bottom line.

Combatting Supply Chain Disruptions with Advanced Analytics

As the corporate world becomes increasingly more globalized, it is not uncommon for a company to move a product through multiple locations before it lands in a customer’s hand.

The increased complexity might be daunting to some managers, but from a data scientists’ point of view, these flows produce a ton of information that is ripe for analysis and can enable discovery of new opportunities. Customers now expect a certain level of service, and companies need to manage a complex network of plants, providers, suppliers, and buyers that enable them to remain flexible, operate efficiently, and meet customer demand.

Explore Mosaic’s Efficient Engagement Process

AI Supply Chain Solutions Resource Library

AI Supply Chain Solutions Sheet

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