Data Envelopment Analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics.
DEA is a data-oriented tool for performance evaluation & benchmarking. DEA generates a composite index. DEA identifies an envelopment of the data.
DEA is a classification tool. DEA measures efficiency & productivity.
Let's discover the new uses and potentials of DEA under big data research.
Network DEA is based upon DEA ratios, but can behave very differently from the standard (or traditional) DEA. Network DEA may not be able to be converted into linear programming problems. Depending on the specific network structures of the decision making units (DMUs), network DEA can be solved via parametric linear programming and most recently by using second-order cone programming techniques.
There are many new research opportunities within the network DEA modeling. Convex optimization techniques need to be employed and adopted for solving non-convex network DEA mdoels.
Note that the standard DEA models can be presented in either envelopment or multiplier form. However, under network DEA, such a duality no longer exists. In the dual to the multiplier DEA model (namely, the envelopment model), researchers discover the "convexity" and established a link between DEA and production function. This indicates that returns to scale (RTS) assumptions when applied to the network DEA need to be investigated.
Under the DEA ratio, the assumptions of constrant RTS (CRS) and variable RTS (VRS) represent two different shapes of the DEA best-practice frontier. Under the standard DEA, a CRS score cannot exceed the corresponding VRS score. However, under network DEA, this observarion may not hold.
although it bears with the name of "DEA".
Returns to Scale (RTS) in DEA can only refer to the shape of the DEA best-practice frontier. The term constant RTS (CRS) or variable RTS (VRS) only bears the economic meaning if DEA is used under production technology.
Network DEA models can be nonlinear. Nonlinear optimization technqies are needed for solving network DEA models.
Production research enabled by data has shifted from analytical models to data-driven, and manufacturing and DEA have been the most popular application areas of data-driven methodologies.
Kuo, Y-H and Kusiak, A. (2019), From data to big data in production research: the past and future trends, International Journal of Production Research, in press.