Best performance modeling using complementary DEA-ANN approach

Abstract

Purpose – The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement. Design/methodology/approach – Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms. Findings – The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation. Research limitations/implications – Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis. Originality/value – This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.

Do Ownership Structure, Political Connections and Executive Compensation Have Multifaceted Effects on Firm Performance? An Alternative Approach

“…using traditional or single measures representing firms' performance may ignore any interaction, substitutions or trade-offs among various firm performance measures. Hence, since DEA of multiple performance measures is proven to be effective in firms' performance evaluation (e.g, Kwon, Lee, & Roh, 2016;Shaddady & Moore, 2018), the current study, after performing the mentioned method, expects that the influence of ownership and board characteristics on firm performance are multifaceted.…”

Developing and training artificial neural networks using bootstrap data envelopment analysis for best performance modeling of sawmills in Ontario

“…The ANN models are being increasingly used in the literature for input-output based performance evaluation and benchmarking techniques (Yi and Thomas, 2009; Hsiang-Hsi et al , 2013; Kwon, 2014; Kwon et al , 2016). In the industry, the most significant application of the ANN models is found in data mining, which includes the processes of data understanding, data preparation and data analysis and knowledge generation (Knoll et al , 2016).…”

“…roundwood, number of employees and lumber and one optimal value of either of input or output variables). Although a few studies have used the ANN modeling approach in other industries with promising results (Athanassopoulos and Curram, 1996; Emrouznejad and Shale, 2009; Hsiang-Hsi et al , 2013; Kuo et al , 2012; Kwon, 2014; Kwon et al , 2016), there are no such studies for predicting the DEA efficiency scores and optimal inputs and outputs using the ANN modeling approach in the forest products industry.…”

Measuring performance of government-supported drug warehouses using DEA to improve quality of drug distribution

“…A performance measurement framework is highly needed for any healthcare organization to understand the existing problems. There are so many approaches to measure the performance of a system such as balance scorecard method, an integrated model based on total quality, Action–Profit Linkage Model, Performance Prism, Capability Economic Value of Intangible and Tangible Assets Model, Unused Capacity Decomposition Framework, Dynamic Performance Measurement System, Activity-Based Costing, Supportive Performance Measures, Customer Value Analysis, Artificial Neural Network, Fuzzy Best–Worst Multicriteria, DEA (Paranjape et al , 2006; Sinclair and Zairi, 1995; Taticchi et al , 2010; Franco-Santos et al , 2012 and Kwon et al , 2016). DEA is the most prevalent and popular quantitative approach for performance measurement because of accuracy (Shirouyehzad et al , 2017 and Ho et al , 2010).…”