Abstract:

Data envelopment analysis (DEA) is a linear programming approach for evaluating relative efficiency of peer decision making units (DMUs) that consume multiple inputs to produce multiple outputs. DMUs can have parallel independent sub-units, where inputs and outputs of each DMU are the sum of those its sub-units. This paper investigates the properties and relationships of the existing parallel DEA models, i.e., network, multi-component and relational DEA models, that address measuring the performance of parallel production systems. A major limitation of the existing DEA models is that efficiency optimization may produce multiple sets of efficiency scores for individual sub-units. The current paper proposes a new DEA approache through efficiency decomposition to deal with this problem. A case of Canadian bank branches is employed to illustrate these parallel DEA approach.

Key words: data envelopment analysis (DEA), parallel production system, efficiency decomposition