We show how reactive synthesis and automatedplanning can be leveraged effectively to find non-maximal solu-tions to deterministic supervisory control problems of discreteevent systems. To do so, we propose efficient translations ofthe supervisory control problem into the reactive synthesis andplanning frameworks. Notably, our translation methods capturethe compositional and reactive nature of control specifications,avoiding a potential exponential explosion found in alternativetranslation approaches. Additionally, we report on experimentalresults comparing the efficacy of different tools from the threedisciplines, for a particular supervisory control benchmark.