DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting

Supply chain forecasting models degrade over time as real-world conditions change. Promotions shift, consumer preferences evolve, and supply disruptions alter demand patterns, causing what is known as concept drift. This silent degradation leads to stockouts or excess inventory without triggering any system warnings. Current industry practice relies on manual monitoring and scheduled retraining every 3-6 months, which wastes computational resources during stable periods while missing rapid drift events. Existing academic methods focus narrowly on drift detection without addressing diagnosis or remediation, and they ignore the hierarchical structure inherent in supply chain data. What retailers need is an end-to-end system that detects drift early, explains its root causes, and automatically corrects affected models. We propose DriftGuard, a five-module framework that addresses the complete drift lifecycle. The system combines an ensemble of four complementary detection methods, namely error-based monitoring, statistical tests, autoencoder anomaly detection, and Cumulative Sum (CUSUM) change-point analysis, with hierarchical propagation analysis to identify exactly where drift occurs across product lines. Once detected, Shapley Additive Explanations (SHAP) analysis diagnoses the root causes, and a cost-aware retraining strategy selectively updates only the most affected models. Evaluated on over 30,000 time series from the M5 retail dataset, DriftGuard achieves 97.8% detection recall within 4.2 days and delivers up to 417 return on investment through targeted remediation.

GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments

The convergence of robotics and virtual reality (VR) has enabled safer and more efficient workflows in high-risk laboratory settings, particularly virology labs. As biohazard complexity increases, minimizing direct human exposure while maintaining precision becomes essential. We propose GAMORA (Gesture Articulated Meta Operative Robotic Arm), a novel VR-guided robotic system that enables remote execution of hazardous tasks using natural hand gestures. Unlike existing scripted automation or traditional teleoperation, GAMORA integrates the Oculus Quest 2, NVIDIA Jetson Nano, and Robot Operating System (ROS) to provide real-time immersive control, digital twin simulation, and inverse kinematics-based articulation. The system supports VR-based training and simulation while executing precision tasks in physical environments via a 3D-printed robotic arm. Inverse kinematics ensure accurate manipulation for delicate operations such as specimen handling and pipetting. The pipeline includes Unity-based 3D environment construction, real-time motion planning, and hardware-in-the-loop testing. GAMORA achieved a mean positional discrepancy of 2.2 mm (improved from 4 mm), pipetting accuracy within 0.2 mL, and repeatability of 1.2 mm across 50 trials. Integrated object detection via YOLOv8 enhances spatial awareness, while energy-efficient operation (50% reduced power output) ensures sustainable deployment. The system's digital-physical feedback loop enables safe, precise, and repeatable automation of high-risk lab tasks. GAMORA offers a scalable, immersive solution for robotic control and biosafety in biomedical research environments.