GSPANN identified and analyzed the attributes impacting the surged price. We addressed each suppliers’ behavior while charging extra fees and its association with the delivery time that was affecting the cost. This resulted in the development of a structured and trained model to uncover the hidden price patterns for estimating the surged price.
GSPANN helped the client by solving business problems through a combined power of data science and machine learning techniques. We analyzed the behavior of each supplier as their price surge was incrementing year-over-year (YOY).
Moreover, we trained the predictive model (developed in Azure ML Studio) on historical data of purchases for the last three years, which consists of purchase order data, supplier features, historical transactions, product features, etc. This real-time estimation helped the client in reducing the expedited delivery expenses.
The client was interested in understanding the supplier’s behavior for predicting and estimating the surge in price. To get predictive analysis in a simplified way, we developed the web application’s frontend in Python and Microsoft Azure ML Studio. We extracted the supplier’s historical data from the database through Azure ML API and hosted the ML-trained model on Azure Cloud for sharing the analytical data.