In this paper, a three-stage heuristic is proposed to solve the machine-part cell formation (MPCF) problem in which parts have alternative process plans, commonly known as the generalized group technology problem. In the first stage, the best process plan (part route) for each part is selected using the proposed route rank index (RRI), a ranking measure calculated from the correlation among the process plans (CoRa – Correlation based ranking). In the second stage, machine-part cells are identified with an objective to maximize the grouping efficacy. A fine-tuning module validates the covering set in the third stage. Computational performance of the proposed heuristic on a set of generalized group technology dataset available in the literature is presented. The process plans identified by CORA resulted in a higher grouping efficacy for 25% of the test instances and for the other test instances the grouping efficacy achieved was as good as the best results reported in literature. © 2017, Springer-Verlag London.