Major Biorepository
September 20th, 2024 WRITTEN BY Fresh Gravity Tags: Analytics & ML, Life Sciences
Fresh Gravity was engaged to produce an algorithmic optimization approach for its shipping functions. The goals were to reduce cost and complexity by automating packing and shipping configurations as well as providing cost estimates for both freight and handling.
Problem
The client faced a problem in packing and shipping of hazardous materials. There are thousands of business rules regarding what can and cannot be shipped in the same container.
Solution
Fresh Gravity served as a strategic advisor to reduce the client’s excessive shipping costs. We used advanced machine learning methods to empirically “reverse engineer” packing rules by analyzing past shipping data. We then designed an advanced box-packing algorithm to minimize costs attributable to shipping. In addition, we created a User Interface and API for easy input of Sales Orders. These tools effectively provided the recommended packing configuration and associated costs.
Impact
This implementation helped the client in getting an optimized and reliable cost of shipping, materials and labor that can be quoted to the customer. Based on the success of the implementation, we enhanced the solution further for automated ingestion of purchase orders from external sources that have arbitrary formats.