Manufacturers are always looking to optimize operations which lower production costs and maximize margins. Whether it be shipping finished product more efficiently, to decreasing waste on the production lines, to managing inventories, or optimal sourcing of raw materials, there are dollars to be saved. Predictive operational analytics allows a manufacturer to accurately understand, anticipate and then make data-driven decisions to positively impact the behavior of their mechanical assets, logistics and employees.

The days of relying on million dollar decisions being left to a small group of executives are now gone. You cannot know everything because there is too much data. Businesses who now rely on predictive manufacturing analytics combined with executive expertise are seeing an increased competitive advantage along with a substantial boost to the bottom line.

Mosaic, a leading analytics firm, can help run these high velocity data streams through world class custom built predictive models. We partner with your business to make sure you are well prepared to take advantage of the wealth of insights provided by mechanical, human, and market data.

Leading Medical Device Manufacturer – Analytics Assessment 

  • Advanced Analytics Readiness Assessment. 
    • Mosaic helped deliver a prioritized project road map with recommendations on how to execute.

Leading Soft Drink Manufacturer and Distributor – Analytics Assessment & Daily Store-Order Prediction

  • Advanced Analytics Readiness Assessment for specific predictive order use case. 
    • Mosaic helped assess:
      • Data, technologies, processes, in-house skill sets, ROI.
    • The customer now has a clear cut path on how to get the most value from data analytics for this particular use case.
  • Daily Store-Order Prediction Model developed and implemented
    • Predicting store demand accurately is a major industry challenge. Mosaic Data Science, a consultancy specializing in advanced analytics, is uniquely positioned to tackle and create applicable solutions.
    • Mosaic data scientists developed a short-horizon predictive model that forecasts the case-quantity orders at a SKU-store level on the day of an account manager’s store visit
      • This architecture combines the predictions of the above three models and selects the one performing best based on historical model performance. Because the three models complement each other in characterizing certain behavior of the case quantities, this model generally provides the most intelligent information and can adjust to pattern changes over time such as when a SKU moves from mainline to secondary status in a store or when a new account manager is assigned to a store,
  • The prediction model and automated insights more accurately predict correct store orders, providing efficient and intelligent results.

Link to full case study.

Leading Alcoholic Beverage Distributor and Manufacturer 

  • Objective: to help better understand trends in state and brand-level craft beer trends.
  • Mosaic quantified metrics that provide more clarity to differentiate states according to current craft maturity and rate of growth/development.
    • Developed K-means clustering model of states using novel metrics, including appropriate visualizations of clusters and metrics within each cluster.
    • Developed a hierarchical clustering model using same metrics to provide alternate description of state-level data.
  • Delivered a summary of statistical relationships between metrics, clusters, and appropriate visualizations of trends within each cluster, and list of states in each hierarchically-derived cluster and appropriate visualizations of tends within each cluster.

Largest Global Express Shipping Companies – Custom Decision Support Tools 

  • Improve operational efficiency with more accurate predictions on overnight package airport operations.
  • Decision support tool running 24/7 at client site providing situational awareness and demand predictions.
    • Gives decision makers awareness across all operational areas, including ramps, the ramp control center (tower), and the air traffic control coordination group.
    • Fuses a variety of customer and external data feeds, sends the data through complex prediction and optimization algorithms to compute and recommend ideal engine-starting times for the customer’s aircraft.
    • Saves millions of dollars annually in fuel costs.

Link to full case study.