Three-stage performance modeling using DEA–BPNN for better practice benchmarking

He Boong Kwon, Jon H. Marvel, James Jungbae Roh

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


This paper proposes an innovative three-stage model using data envelopment analysis (DEA) and backpropagation neural network (BPNN) for supporting ‘better practice’ benchmarking as contrasted with the traditional ‘best practice’ benchmarking. Research has shown that DEA models have the capability of setting optimal goals, but the drawback of the standard DEA approach is its inability to propose actionable targets necessary for incremental improvement. Overcoming the shortfalls of DEA and its superiority-driven practices, the neural network approach accommodates stepwise improvement through adaptive learning and prediction capability. Consequently, the proposed three-stage model is capable of generating feasible improvement options for managers as an intelligent decision support tool. At its core, the innovative approach provides a sound methodological foundation for shaping a ‘better practice’ paradigm and contributes to the literature through methodological advancement. The effectiveness of the model is empirically tested through the use of data from the healthcare industry, and the results confirm a practical utility of the model.

Original languageEnglish (US)
Pages (from-to)429-441
Number of pages13
JournalExpert Systems with Applications
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


Dive into the research topics of 'Three-stage performance modeling using DEA–BPNN for better practice benchmarking'. Together they form a unique fingerprint.

Cite this