Improving Yard Processes and Cost Reduction through Data-Driven Optimization
A leading US bottling company faced a dual challenge: optimizing yard processes across its 52 plants in the USA and significantly reducing detention costs for incoming trucks.
The company’s existing yard check-in, check-out, and pallet loading systems generated data, but it was not valuable for decision-making.
Data Science-Driven Yard Process Optimization
Our consulting team took on the challenge of optimizing yard processes using data science and statistical models. The solution included:
- Statistical Optimization Model: Developing a sophisticated statistical optimization model to tackle the “truck-in, truck-out” problem within the yard effectively.
- Automated Feature Selection: Implementing an automated feature selection mechanism, inspired by the Minimal Redundancy Maximum Relevance algorithm, to extract critical data and improve decision-making.
The data-driven optimization initiative has improved yard processes and delivered substantial cost savings for the client. The company has redefined its decision-making processes and set the stage for continued operational efficiency. In 2021, detention costs for incoming trucks were reduced by an impressive 6%, saving the company $3.4 million.