Predictive Maintenance in Manufacturing

Predictive maintainance gives manufacturers the ability to lower mantainence costs while simultaneously gaining competitive advantage through higher customer satisfaction, optimized supply chains, new revenue streams and more. In this white paper, we cover:

  • The Evolution of Equipment Maintenance
  • Emerging Technologies in Predictive Maintenance
  • The Keys to Successful Predictive Maintenance

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Machine Learning Applied to Customer Targeting for Direct Sales

Using ML to effectively target the right customer at the right time with the right information can make a big difference in how effective your campaign is. This short white paper discusses some of the approaches and demonstrates how one company achieved a 6x uplift in conversions by apply ML to customer targeting.

Demand Forecasting with Latent Matrix Factorization

For many businesses, demand planning is essential. Profitability, cash flow, and customer satisfaction and retention all hinge on getting this right. This white paper will introduce latent matrix factorization to model demand curves and discuss how it can be used to achieve these outcomes.

COVID–19 Data Set Modeling and Analytics

During times of crisis, companies must look at the available data — both internal and external— and try to understand how that data can be used to determine how the business is currently being impacted, how it is likely to be affected in the future, what are most likely scenarios that will play out, what can be done to counter those scenarios and take advantage of hidden opportunities in this rapidly changing environment. The Starschema COVID-19 dataset ingests reliable data from multiple sources and makes it analytics-ready so it can be easily accessed and used.

Machine Learning for Business Leaders

From recommender systems on streaming services to personal assistants like Siri or Alexa, ML is nearly everywhere today. This white paper demystifies machine learning for non-technical business stakeholders to better collaborate in, and derive more value from, data science initiatives.

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