Smart manufacturing uses advanced data-driven solutions to improve performance and operations resilience requiring large amounts of data delivered quickly, enabled by telecom networks and network elements such as routers or switches. Disruptions can render a network inoperable; avoiding them requires advanced responsiveness to network usage, achievable by embedding autonomy into the network, providing fast and scalable algorithms that use key metrics to manage disruptions, such as impact of failure in a network element on system functions. Centralised approaches are insufficient for this as they need time to transmit data to the controller, by which time it may have become irrelevant. Decentralised and information bounded measures solve this by placing computational agents near the data source. We propose an agent-based model to assess the value of the information for calculating decentralised criticality metrics, assigning a data collection agent to each network element, computing relevant indicators of the impact of failure in a decentralised way. This is evaluated by simulating discrete information exchange with concurrent data analysis, comparing measure accuracy to a benchmark, and with measure computation time as a proxy for computation complexity. Results show losses in accuracy are offset by faster computations with fewer network dependencies.