Motivated by the increasing demand for faster service when advanced capital goods fail, we address the problem of dispatching and pro-actively repositioning service engineers in a service logistics network such that extremely short solution times to service requests can be realized in a cost-ecient way. By formulating this problem as a Markov decision process, we are able to investigate the structure of the optimal policy, thereby focusing on specic characteristics of this optimal policy. Using these insights, we then propose scalable static and dynamic heuristics for both the dispatching and repositioning sub-problem for networks of industrial size, based on the minimum weighted bipartite matching problem and the maximum expected covering location problem, respectively. The dynamic dispatching heuristic takes into account real-time information about both the state of equipment and the meet of service engineers, while the dynamic repositioning heuristic maximizes the expected weighted coverage of future service requests. In a test bed with a small network, we show that our most advanced heuristic performs excellent with an average optimality gap of 4.6% under specific circumstances, but strictly outperforms all other heuristics across all instances. To show the practical value of our proposed heuristics, we conducted extensive numerical experiments on a large test bed with networks of industrial size where signicant savings of up to 61.9% compared to a benchmark static policy are attained. In the same test bed, we show that being flexible in deviating from previous dispatch and reposition decisions, regardless of the heuristics that are used for these decisions, can lead to substantial savings of 49.2% compared to when reallocation is not allowed. The results also show that using the proposed dynamic dispatching heuristic, instead of the widely adopted `closest-idle first’-heuristic, leads to savings of 27.7%.