Lu, Wen-MinKweh, Qian LongNourani, MohammadWang, Hsiu-Fei2022-02-072022-02-07© 20212021Lu, W. -., Kweh, Q. L., Nourani, M., & Wang, H. -. (2021). Using network DEA and grey prediction model for big data analysis: An application in the global airline efficiency, International Series in Operations Research & Management Science, vol 312. Springer, Cham, 312, https://doi.org/10.1007/978-3-030-75162-3_1208848289https://doi.org/10.1007/978-3-030-75162-3_12http://hdl.handle.net/20.500.12519/509This study proposes a big data enabled analytics approach to extract the valuable information through network Data Envelopment Analysis (DEA) integrated with multiplicative efficiency aggregation (MEA) and Grey Prediction Model. That is, when dealing with large volumes of data, network DEA can uncover hidden information that are valuable for decision making. To illustrate the application, we develop an airline operational framework in a network structure, whose operation is often time too complex. With a two-stage network DEA model in the form of second-order cone programming (SOCP) that solves issues related to nonlinearity, we also address the undesirable output of carbon dioxide emissions and solve potentially nonconvex optimization problem in revealing the energy efficiency and revenue efficiency of 23 global airlines over the period of 2013–2017. In this evaluation, carbon dioxide emissions exit the first stage without being inputted in the second stage with the assumption of variable returns to scale. This study also estimates and predicts airline efficiency by integrating the network DEA with grey prediction model, which assesses the impacts of all ratio combinations of inputs, intermediates, and outputs on overall efficiency. Overall, this study proposes an approach to transform large volumes of data into multiple useful information, and hence, extracts the value dimension of big data hidden in the airline operation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.enLicense to reuse abstract has been provided by Springer Nature and Copyright Clearance Center.Airline energy efficiencyBig dataData enabled analyticsForecastingGrey prediction modelNetwork data envelopment analysisSecond order cone programmingUndesirable outputBook chapterCopyright : © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.