Supply Chain Optimization

Approach

Harvest information from transactional systems against data lake:

1. Extract data from transactional systems
2. Apply data transformations to perform aggregations, time series analysis and additional features
3. Developed custom AI/ML models to detect fraud
4. Integration of forecasted materials into Transaction system
5. This information is also available using a customer web application

Value Delivered

We addressed these challenges by leveraging mathematical models and harnessing the power of Big Data. The implementation brought forth several key values to the business:

• Automation: introduced automation into the logistics activities, streamlining processes and reducing the reliance on manual intervention.
• Enhanced Accuracy in Forecasting: The tool employed advanced mathematical models and Big Data analytics to enhance the accuracy of forecasting methods. This allowed to make more informed decisions, optimize inventory levels, and reduce the likelihood of shortages or excess stock, ultimately contributing to cost savings.
• Optimized Resource Utilization: With improved forecasting accuracy and automation, E-Redes could optimize the utilization of warehouse resources. This led to a reduction in unnecessary costs, better utilization of space, and improved overall resource efficiency.
• Strategic Decision Support: The tool not only automated day-to-day operations but also provided valuable data-driven insights for strategic decision-making. Could use the information generated to make informed decisions about resource allocation, inventory management, and overall logistics strategy.