He-Boong Kwon

Implementation of strategic green orientation in supply chain

Purpose – The purpose of this paper is to present a research model that defines the inter‐relationships between strategic green orientation, integrated product development, supply chain coordination, green performance outcomes and business unit performance. This paper aims to address innovation issues by integrating strategic orientation, internal business practices, supply chain coordination, and performance outcomes measures.Design/methodology/approach – The international data of 711 firms accessed through the International Manufacturing Strategy Survey (IMSS IV) are used to validate this model.Findings – A firms strategic green orientation involves past green practices, implementation of innovative environment improvement program and future commitment for environmental practices. This strategic green orientation is supported by a set of inter‐organizational innovation practices such as integrated product development practices, effective coordination of supply chain network and relevant and measurable ...

Two-stage production modeling of large U.S. banks

This study proposes a new approach to model a two-stage production process.A DEA-BPNN method adds flexibility in two-stage production modeling.The proposed model complements two-stage DEA by adding predictive power.The adaptive method can support incremental performance improvement.The proposed model is empirically supported by using data from large U.S. banks. The purpose of this paper is to explore an innovative performance model for a two-stage sequential production process by combining data envelopment analysis (DEA) and back propagation neural network (BPNN). Recent literature shows a growing interest on performance modeling of two-stage production process using DEA. But, most previous studies on the scope of two-stage modeling are still limited to the efficiency measurement and also have neglected the progressive direction of predictive value and capacity. As an optimization technique, two-stage DEA model lacks predictive capacity. Despite an adaptive prediction model being a practical necessity, this area has rarely been addressed in the previous studies. This paper demonstrates an integrative approach to constructive performance modeling of a two-stage sequential production process by exploring predictive capacity of BPNN in conjunction with DEA. The effectiveness of our jointly integrated performance model through this study is empirically supported by its practical application to the financial banking operations across large U.S. banks.

Emerging issues of procurement management: a review and prospect

Increasingly, procurement management is becoming a strategic priority of firms for their sustainable competitive advantage in turbulent times. In todays dynamic market environment, procurement is positioned as a critical integrative business process and its focus has been extended from short term cost minimisation to long term value creation and delivery. In this paper, we examine a major procurement literature and present a framework that suggests evolving patterns of strategic procurement practices of firms. This review of articles published in major journals about procurement management shows that procurement literature has evolved from specialised functional orientation to more integrative and strategic approaches. This article provides summaries of the procurement literature in terms of its key dimensions and emerging patterns. Future research issues are discussed.

Performance modeling of mobile phone providers: a DEA-ANN combined approach

Purpose – The purpose of this paper is to investigate the feasibility of using artificial neural networks (ANNs) in conjunction with data envelopment analysis (DEA) for the performance measurement of major mobile phone providers, and for subsequent predictions related to best performance benchmarking and decision making. Design/methodology/approach – DEA and ANN are combined, providing an integrated modeling approach via a two-stage process. DEA is used for front end measurement, while ANN provides learning and prediction capabilities. DEA analysis of industry characteristics is based on the measurement of each decision-making units (DMU) performance. Back propagation neural networks (BPNN) can then predict each DMUs efficiency score, based on the results of the DEA models. Additional BPNN models provide best performance predictions. Findings – The DEA module successfully evaluates the competitive status of firms in the mobile phone industry in terms of efficiency. Efficiency trends over the observation...

Best performance modeling using complementary DEA-ANN approach

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 sequenti...

Better practice prediction using neural networks: an application to the smartphone industry

Purpose – The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry. Design/methodology/approach – A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement. Findings – This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive pred...

Neural network modeling for a two-stage production process with versatile variables: Predictive analysis for above-average performance

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.

A Neural Network Approach to the Operational Strategic Determinants of market Value in High-Tech Oriented SMEs

The purpose of this paper is to present an adaptive performance model using backpropagation neural network (BPNN) in scrutinizing impact of strategic factors on firm performance, especially within high-tech SMEs in U.S. The novel design approach introduced in this paper segments SMEs into high and low performance groups and captures different impact patterns of strategic variables (e.g. R&D). This paper explores both explanatory and predictive capacity of a neural network and extends its application to the measurement of relative efficiency and subsequent prediction of potential improvement. This paper demonstrates effectiveness of a neural network for SME analysis and its potential advancement toward performance modeling as an adaptive decision support tool.

Comparative efficiency assessment and strategic benchmarking of smartphone providers with data envelopment analysis

The purpose of this study is to conduct comparative efficiency assessment and strategic benchmarking of smartphone providers. Data envelopment analysis (DEA) is useful in examining competitive patterns of products and services. Since smartphone products are moved away from functional devices into platform products and services, the new business model focuses on perpetual dynamic value deployment beyond hardware functionality of products. In this research, DEA is utilised as a strategic decision support tool that measures the extent of firm efficiencies based on multiple year performance outcomes. DEA efficiency trend in particular is a significant indicator of a companys near future sustainability. This study is the first attempt of benchmarking smartphone industry where short-term strategy and rapid innovation precedes long term tactics and incremental improvement.