Improving project timelines using Al / ML to detect forecasting errors

Abstract

This project focuses on the creation of a novel tool to detect and flag potential errors within Amgen's capacity management forecast data, in an automated manner using statistical analysis, artificial intelligence and machine learning. User interaction allows the tool to learn from experience, improving over time. While the tool created here focuses on a specific set of Amgen's data, the framework, approach and techniques offered herein can more broadly be applied to detect anomalies and errors in other sets of data from across industries and functions. By detecting errors in Amgen's data, the tool improves data robustness and forecasts, which drive decisions, actions and ultimately results. Flagging and correcting this data allows for overcoming errors, which would otherwise damage the accurate allocation of Amgen's human resources to activities in the drug pipeline, ultimately hampering Amgen's ability to develop drugs for patients efficiently. A user interface (UI) dashboard evaluates the tool's performance, tracking the number of errors correctly identified, the accuracy rate, and the estimated business impact. To date the tool has identified 893 corrected errors with a 99.2% accuracy rate and an estimated business impact of $77.798M optimized resources. Using the paradigm of intelligent augmentation (IA), this tool empowers employees by focusing their attention and saving them time. The tool handles the human-impossible task of sifting through thousands of lines and hundreds of thousands of data points. The human user then makes decisions and takes action based on the tool provided output.