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About

Elena Tappia is a researcher specializing in industrial logistics, automated material handling, and smart manufacturing systems, with a particular focus on the design and optimization of part feeding systems for assembly lines. Her work sits at the intersection of robotics, operations research, and production management, addressing the growing industrial challenge of delivering customized goods efficiently at scale. Tappia's most influential contribution, "A performance model for mobile robot-based part feeding systems to supermarkets" (2021, 12 citations), pioneered the analytical modeling of autonomous mobile robot (AMR) deployment in supermarket-style feeding architectures — a first in the field. Building on this foundation, her 2024 study on real-time data integration for mixed-model assembly scheduling (7 citations) demonstrates her commitment to translating theoretical models into practical, data-driven solutions. Her research portfolio also extends to automated palletizing systems and vertical robotic storage, reflecting a broad command of warehouse and shop-floor automation technologies. With nearly two dozen citations across her key works, Tappia's research is gaining meaningful traction among academics and practitioners navigating Industry 4.0 transformations. Her contributions are particularly valuable for engineers and supply chain researchers seeking rigorous, model-based frameworks for deploying flexible robotic systems in dynamic manufacturing environments.