Optimizing ROP Metrics and Reporting: Cloud Migration and Automation Strategies

This document details a methodology to modernize your ETL (Extract/Transform/Load) pipelines using automated processes, cloud conversion, and performance tracking as appropriate for business goals. This includes a transition to scalable, cloud-native operations with Informatica PowerCenter and Azure Data Factory to replace outdated SSIS packages as well as implementing properly structured ETL testing through CI/CD processes using tools such as Jenkins and GitHub. This paper describes how to recommend the modernization of an ETL (Extraction, Transformation, and Loading) process to include: automating the entire ETL solution (replace the existing configurations with automation), hosting the solution in a Cloud platform, and monitoring ETL performance against business requirements. Examples of how we will accomplish this include using the Cloud-based applications Informatica PowerCenter or Azure Data Factory to replace legacy SSIS packages. Additionally, we will follow a structured test process using the CI/CD (Continuous Integration/Continuous Delivery) tool suites of Jenkins and GitHub. Power BI will be the analytics and reporting tool selection for Agile project management. The paper describes how to benchmark pipeline performance to identify the Key Performance Indicators (KPIs) to evaluate pipeline performance. The document evaluates why the traditional measure of Reorder Point (ROP) is inaccurate and typically not used; the solution proposed is to use a cloud-native architecture such as AWS MSK and Snowflake to support the development of predictive reorder points. Lastly, a case study of an Electronics company will be provided where a 60% savings and 96% accuracy have occurred during peak seasons, and a comprehensive six-month migration plan to augment ROP adjustments through Generational AI presents opportunities for improvement. Keyboards: Informatica PowerCenter, Azure Data Factory, SSIS packages, Reorder Point (ROP), Generational AI