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.
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.