Substituting inventory with information: A trace-driven simulation on the value of IoT in airport mobile asset management

Airport mobile assets, specifically baggage trolleys, frequently exhibit a paradox of “overall surplus yet local shortages†due to stochastic passenger demand and opaque ground logistics. This study quantifies the economic substitutability of information for physical inventory using a trace-driven simulation framework. Leveraging 10.54 million flight records from 42 major airports, we reconstruct minute-level trolley demand profiles and stress-test inventory systems with an M(t)/G/∞ queueing model, contrasting manual collection (informed by Koopman’s search theory) with IoT-enabled dynamic retrieval. Under the central baseline and optimized recycle-time scenarios examined here, the trace-driven simulation results indicate a modeled fleet reduction of approximately 25%–33% across the 42 airports, with a median near 30%, while maintaining a very high target service level. A pronounced scale effect is observed: for Tier-1 mega-hubs, absolute fleet reductions exceed 2000 units, offering a compelling return on investment in digital tracking. The underlying mechanism is the truncation of the long tail of recirculation times, which acts as a buffer against the propagation of delay-induced demand surges. Under comparable operating conditions, these findings suggest that digital transformation can be not merely an operational upgrade but a strategic priority for asset-heavy hubs.