From AGVs to AMRs: A Decision-Driven Framework for the Selection and Implementation of Autonomous Transport Systems in Intralogistics.

Abstract

The transition toward automated material handling solutions is increasingly pursued as an urgent necessity in modern intralogistics, yet many manufacturing companies find themselves unprepared and lack of awareness regarding the exact process required to execute it. While various autonomous transport technologies, such as Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs), are available, industrial decision-makers are often unaware of structured methodologies to evaluate and compare vehicle suitability against their operational requirements. This makes it difficult to assemble fragmented pieces of knowledge scattered across literature and industry practice. The present study addresses this bottleneck by providing a structured decision-support framework and guidelines for the preliminary implementation of the selected fleet. Using a mixed-methods approach, a qualitative parameters prioritization matrix was developed to synthesize key vehicle and process characteristics. This qualitative tool is integrated with a Total Cost of Ownership (TCO) model; rather than providing rigid quantitative data, the TCO highlights overlooked operational variables that translate into financial advantages. The framework was calibrated and validated through industrial field insights, involving a prominent automated transport systems supplier and a large-scale manufacturing end-user within the automotive sector. The findings demonstrate that a successful automation path depends on balancing environmental dynamics with system predictability: while AMRs excel in highly flexible configurations, AGVs remain a critical, reliable solution that should not be underestimated where operational consistency is crucial. The resulting framework offers logistics managers a holistic, replicable guide to streamline knowledge, reduce implementation risks and evaluate the hidden economic benefits of autonomous transport adoption.