A new heuristic algorithm is presented to manage the effect of internal disruptions in an assembly, or other multi-stage value adding systems, where, at least, a proportion of resources are multi-functional. The idea is that when some resources become unavailable for a time, other resources as identified by the algorithm are relocated from other parts of the system to the affected processes to minimise the loss of overall throughput. The context of the problem is first established by defining the system at an abstract level, and the various types of disruptions are identified. A formal definition of the problem is provided followed by the design of the heuristic algorithm based on the establishing the dominant variable. The performance of the algorithm is then assessed using a Monte Carlo parametrisation of simulations. As the baseline solution, the output of the heuristic is compared with that of a genetic optimisation algorithm running an agent-based simulation in a first-order Monte Carlo experiment to produce robust results. Several disruption scenarios are used to validate the heuristic across various values of parameters such as number of resources affected by the disruption, concentration of the disruption in the assembly system and the time of the distribution. The reduction in throughput is used as the measure of comparison in the experiments. The heuristic is found to be effective when the disruption time is more than four times as long as the time that it takes to relocate resources.