Smart manufacturing uses advanced data-driven solutions to maximise performance and resilience of daily operations. It requires large amounts of data delivered quickly. Datatransfer is enabled by telecom networks and network elements such as routers or switches. Disruptions can render a network inoperable, and advanced responsiveness to network usage is required to avoid them. This may be achieved by embedding autonomy into the network, providing fast and scalable algorithms that use key metrics for prioritising the management of a potential disruption, such as the impact of a failure in a network element on system functions. Centralised approaches are insufficient for this as they require time to transmit data to the controller, by which time it may have become irrelevant. Decentralised and information bounded measurements solve this by situating computational agents near the data source. We propose a method to assess the value of the amount of information for calculating decentralised criticality metrics. The method introduces an agent-based model that assigns a data collection agent to every network element and computes relevant indicators of the impact of a failure in a decentralised way. The method is evaluated through simulations of discrete information exchange and concurrent data analysis, comparing accuracy of simple measures to a benchmark, and computation time of the measures as a proxy for computation complexity. Results show relative losses in accuracy are offset by faster computations with fewer network dependencies.