[ Case Study ]
Optimized Forecasting: From 70% to 92% Accuracy
The client was facing challenges with their current demand forecasting system. It was not scalable, unstable, and was prone to errors.
Our objective was to enhance the demand forecasting system's accuracy and efficiency, reducing operational costs and improving on-time deliveries, ultimately boosting customer satisfaction.
Overhauling an unstable demand forecasting system.
Integrating various external factors to produce a more accurate forecasting model.
Solution and Technologies
Our solution involved replacing the existing Python Kubernetes scripts with Databrics notebooks using Spark ML on Scala. The entire system was executed on Azure Data Factory, making use of Azure Blob storage.
To improve the forecasting accuracy, we cleaned and pre-processed historical shipment data, integrated machine learning algorithms, and accounted for external variables such as seasonal changes, economic indicators, and competitor data.
Conclusions on the Project
With the implemented changes, we achieved a significant boost in forecasting accuracy, elevating it from 70% to 92%. This heightened accuracy allowed for better resource allocation, slashing operational costs by 15% and elevating on-time deliveries by 18%. As a direct result, customer satisfaction among the logistics partners grew by a substantial 25%. This project underscored the vital role of data-driven strategies in the logistics sector.
A significant boost in forecasting accuracy