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.
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
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 are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.
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.
Retail inventory optimization is a great candidate use case to apply machine learning & deliver immediate business value.
Oil & gas firms have a tremendous opportunity to refresh demand forecasts with ML techniques, improving accuracy and adding to the bottom line.