Data Engineering: The Critical Foundation for Scalable Supply Chain Optimization Models

This paper examines the crucial role of data engineering in developing and maintaining effective supply chain optimization models. While organizations often emphasize data science and mathematical optimization techniques, the foundational importance of data engineering is frequently underestimated [1]. The research demonstrates how robust data engineering practices directly impact model accuracy, reliability, and business adoption. Through analysis of real-world use cases and implementation challenges, this study presents a comprehensive framework for building scalable data engineering solutions that support complex supply chain optimization models. The findings indicate that organizations implementing structured data engineering practices experience significantly higher model adoption rates and more reliable optimization outcomes [2].