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Attaining the optimal scale size of production systems is an issue frequentlyfound in the priority questions on management agendas of various types oforganizations. Determining the most productive scale size (MPSS) allows thedecision makers not only to know the best scale size that their systems canachieve but also to tell the decision makers how to move the inefficientsystems onto the MPSS region. This paper investigates the MPSS concept forproduction systems consisting of multiple subsystems connected in parallel.First, we propose a relational model where the MPSS of the whole system and theinternal subsystems are measured in a single DEA implementation. Then, it isproved that the MPSS of the system can be decomposed as the weighted sum of theMPSS of the individual subsystems. The main result is that the system isoverall MPSS if and only if it is MPSS in each subsystem. MPSS decompositionallows the decision makers to target the non-MPSS subsystems so that thenecessary improvements can be readily suggested. An application of China'sFive-Year Plans (FYPs) with shared inputs is used to show the applicability ofthe proposed model for estimating and decomposing MPSS in parallel network DEA.Industry and Agriculture sectors are selected as two parallel subsystems in theFYPs. Interesting findings have been noticed. Using the same amount ofresources, the Industry sector had a better economic scale than the Agriculturesector. Furthermore, the last two FYPs, 11th and 12th, were the perfect twoFYPs among the others.

Estimating and decomposing most productive scale size in parallel DEA networks with shared inputs: A case of China's Five-Year Plans