Health Benefits Optimization

This Professional Employment Organization (PEO) managed over 30,000 employees staffed at client companies. The PEO and clients needed to improve effectiveness of a new program for personal health counseling. Many employees left their companies before showing improved health.

We combined several sources of company data and used machine learning to predict retention for program candidates. The predictive model identified candidates with higher likelihoods for continued employment. The PEO can now better assign counseling resources to improve employee health.

Supply Chain Leakage

A manufacturer was losing product along its supply chain. Involvement by specific vendors was unknown except for a small number previously under suspicion.

We applied machine learning to over 3 Million transactions and discovered patterns previously hidden in the data. The team identified additional vendors and assessed damages. The manufacturer was able to recoup expenses.

Inventory Planning

An online retailer suffered frequent stockouts of its best-selling products. They also carried excess inventory for other products due to seasonal changes in demand.

We built a model based on time series forecasting to better predict upcoming demand. We used the forecast as input to an inventory planning model based on product profitability. The company can now reduce stockouts without increasing expensive inventory.