The Secret Recipe for Modeling Warehouse Throughput
Name
DeSutter_Gao_Final Capstone Report_The Secret Recipe for Modeling Warehouse Throughput.pdf
Description
Supply Chain Management capstone research project
Size
878.03 KB
Format
Adobe PDF
Checksum (MD5)
31294348f6c320ccea451d6b1e772775
Author(s)
DeSutter, Dana
Gao, Sherry
Date Issued
June 16, 2021
Abstract
Throughput is a critical performance metric for warehouse operations in the food industry. Accurate throughput estimations are necessary for effectively planning replenishments, inventory levels, and labor resources to meet the needs of customers. General Mills, who manages a large variety portfolio with different packaging, demand volatility, storage requirements, and outbound weight requirements, is interested in throughput estimation at their existing warehouses, also called Customer Service Facilities (CSFs). This project utilizes data collected from various data sources at General Mills to understand the factors that influence throughput. After interviewing key company stakeholders to learn more about warehouse operations, we collected and analyzed data. We developed a linear regression model, using machine learning to predict throughput. Ultimately, the analysis demonstrated that warehouse throughput at General Mills is not only impacted by internal factors, such as labor and product mix, but it is also impacted by external factors, such as day of the week, and higher demand requirements near quarter-end. With less than a year of data, the model still achieved a low mean absolute percentage error (MAPE) around 10%, implying highly accurate results. The strong forecast accuracy allows General Mills to create strategic plans to manage their labor constraints and improve the predictive performance of their throughput estimations.
Subjects
Machine Learning
Supply Chain Strategy
Warehouse
Terms of Use
CC0 1.0 Universal
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