Measuring Cost, Revenue, and Profit Efficiency in General Two-Stage Network DEA with Flexible Measures

This dissertation addresses the problem of measuring cost, revenue, and profit efficiency in general two-stage network structures with flexible measures, where a performance measure may serve as an input for some decision-making units and as an output for others. Traditional Data Envelopment Analysis models assume predetermined roles for variables and cannot handle such ambiguity, particularly in network settings with intermediate measures. This study develops novel two-stage network DEA models that simultaneously determine the optimal classification of flexible measures while calculating cost, revenue, and profit efficiency under fixed costs and prices. The key innovation lies in classifying flexible measures to minimize costs and maximize revenue and profit. The proposed models demonstrate that optimal classification of flexible measures leads to cost reduction in the cost efficiency model, and revenue and profit increases in the revenue and profit efficiency models, compared to the fixed-role assumption. The findings validate that the proposed approach provides more realistic and accurate efficiency evaluations by incorporating the flexibility inherent in real-world production processes. This research contributes to the literature on network DEA, flexible measures, and economic efficiency, offering practical tools for performance assessment in complex organizational structures such as banks, supply chains, and insurance companies.

کلیدواژه ها:

Data envelopment analysis ، Flexible measure ، Cost efficiency ، Revenue efficiency ، Profit efficiency ، General two -stage network