Manufacturing

Manufacturing Machine Learning Consultants


Mosaic offers manufacturing machine learning consultants who partner with manufacturing firms to help drive actionable insights from their data.

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 provides manufacturing machine learning consultants, who 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 & Connected Device Analytics  

  • Advanced Analytics Readiness Assessment. 
    • Mosaic helped deliver a prioritized project road map with recommendations on how to execute.
  • Mosaic, a leading manufacturing analytics consulting firm, was contacted to set up and run near real-time device diagnostic analytics.
    • Data architecture and infrastructure support.
    • Analytical models to ingest diagnostic data streams and deliver insights.

Leading Multinational Industrial Adhesive Manufacturer – Advanced Revenue Forecasting  

  • Improve forecasting processes and outputs to optimize inventory and supply to customers, to better understand the market trends that influenced their customers’ behavior, and to provide shareholders a more accurate representation of the business. 
    • The firm wanted to segment their forecasts by markets they serve and needed to uncover hidden relationships in their data to build more robust and accurate revenue forecast models for each business segment.
    • Mosaic performed a pure time series analysis, a time series regression analysis, and an ARIMAX analysis on the data to incorporate market indices (predictor variables) alongside traditional time series effects.
  • Mosaic was able to apply predictive analysis tools to identify key predictors of aggregate customer behavior and to more accurately forecast the manufacturer’s monthly line-of-business revenues, and providing the business with analytics insights they can act upon.

Link to full case study.


Leading Valve Manufacturer – Scrap Rate Diagnostics 

  • Heart valve manufacturer required root cause analysis and diagnostic analytics to identify drivers of rise in scrap rate. 
    • Utilized innovative exploratory data analysis and anomaly detection techniques.
    • Performed multiple data transformations using principal component analysis.
  • Mosaic was able to identify statistically significant factors that led to the rise in defects and delivered these insights to the business to optimize their production process.

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

  • Advanced Analytics Readiness Assessment for specific predictive order use case. 
    • Mosaic provided manufacturing machine learning consultants 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.


Deploying Analytics for CPG Late Delivery Prediction 

  • Objective: to identify costly late shipments early in the supply chain.
  • A large player in the CPG sector came to Mosaic with a late delivery forecasting model built from historical data.
  • The company wanted Mosaic’s expertise to transition from a statistical model – which, while useful in explaining the past, does not help predict the future – to a forward-looking predictive system that could be integrated with the company’s existing IT infrastructure.
  • Mosaic created a modular architecture for the predictive platform that can be run in two modes:
    • A training mode that takes a model specification and a historical dataset as inputs and returns a trained model.
    • A scoring mode that takes a trained model and current data snapshot as inputs and returns a risk score (probability of late delivery) for each currently scheduled delivery.
  • Extracting insights from data is easy; making those insights actionable, particularly in an automated system, is difficult.

Link to the full case study.