Automation Impact Analysis: A Conceptual Framework for Tracing System-Wide Consequences of Automation in Off-Site Construction
Abstract
The automation of production activities in off-site manufacturing plants has demonstrated measurable gains in cycle time, labor efficiency, and dimensional precision. Yet despite these well-documented benefits, adoption among manufacturers remains notably low, driven in part by an insufficient understanding of what automation implementation operationally entails beyond the automated station itself. Existing decision-support frameworks evaluate automation at the station level through performance comparisons, neglecting the system-wide knock-on effects that may propagate through interconnected production activities when a single station is automated. These effects, including upstream shifts in data preparation and procurement demands, downstream bottleneck migrations, workforce restructuring, and spatial reconfiguration, represent real operational costs that remain invisible in current assessment approaches. To address this gap, this paper proposes the Automation Impact Analysis (AIA) framework, a modular, system-oriented analytical structure adapted from the Change Impact Analysis methodology established in the manufacturing industry. The AIA traces how a defined automation scenario propagates through the production system by following the material flow, informational, technical, spatial, and organizational dependencies that link activities to one another, surfacing the consequential activities and responsive actions that the system must accommodate. These propagated effects are then translated into system-level performance shifts and consolidated into a total cost-benefit evaluation that brings implementation costs and operational gains into the same analytical frame. The framework provides manufacturers with a structured, auditable pathway from automation intent to informed investment decision, grounded in the realistic complexity of their production systems rather than idealized station-level projections.