Neural network modeling for a two-stage production process with versatile variables: Predictive analysis for above-average performance. (15th June 2018)

• Record Type: Journal Article
• Authors: Kwon, He-Boong; Lee, Jooh; White Davis, Kristyn N
• Is Part Of: Expert systems with applications. Volume 100(2018)
• Page Start: 120
• Page End: 130
• Publication Date: 2018-06-15
• Subjects: Above-average performance -- Bank production process -- Neural networks -- Two-stage production modeling; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.01.048
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A standalone neural network approach to model sequential production processes. Salient above-average performance model in a two-stage process. Prediction-focused model overcoming shortfalls of DEA. Neural network application to the two-stage bank production process. Neural network-based performance segmentation and efficiency analysis. Abstract: With growing academic interest and pragmatic need, adaptive two-stage production modeling becomes an emergent research topic for decision sciences and production management. Although prior research has addressed sequential production process, the primary focus was limited to efficiency analysis with a narrow scope of applications. Data envelopment analysis (DEA) has been commonly used for earlier studies; however, its lack of learning and deficiency in predictive capability seriously diminish the practical utility of DEA and call for an intelligent information-processing technique for further advancement. This paper uniquely presents an output-focused backpropagation neural network (BPNN) approach with capabilities to capture patterns of high performers, a significant departure from conventional efficiency-driven DEA analysis, as well as a promising analytic paradigm. In so doing, the proposed standalone BPNN can predict above-average performance and supports managerial decision-making in setting progressive performance targets in consecutive stages. The sound empirical application to the two-stage bank production process proves the effectiveness of the proposed analytic paradigm. In brief, the intelligent learning model advances existing two-stage production modeling with a methodological breakthrough and makes significant contributions to the existing literature.