A Prescriptive Data Pipeline Framework for Modeling Cost-to-Serve Variability and Enhancing Operational Transparency in CPG Ecosystems Samuel Oladapo Taiwo1 & Oluwafijimi Mayowa Ayodele2 1,2 Rawls College of Business, Texas Tech University, United States DOI – http://doi.org/10.37502/IJSMR.2024.71212 Abstract Cost-to-serve (CTS) variability remains a persistent challenge in Consumer-Packaged Goods (CPG) ecosystems due to fragmented data architectures, complex…
A Prescriptive Data Pipeline Framework for Modeling Cost-to-Serve Variability and Enhancing Operational Transparency in CPG Ecosystems