Applied Economics ISSN: 0003-6846 (Print) 1466-4283 (Online) Journal homepage: http://www.tandfonline.com/loi/raec20 Performance assessment for electronic manufacturing service providers using two-stage super-efficiency SBM model Chia-Nan Wang, Hsien-Pin Hsu, Yen-Hui Wang & Thi-Thu-Huyen Pham To cite this article: Chia-Nan Wang, Hsien-Pin Hsu, Yen-Hui Wang & Thi-Thu-Huyen Pham (2016): Performance assessment for electronic manufacturing service providers using two- stage super-efficiency SBM model, Applied Economics, DOI: 10.1080/00036846.2016.1229446 To link to this article: http://dx.doi.org/10.1080/00036846.2016.1229446 Published online: 13 Sep 2016. Submit your article to this journal Article views: 23 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=raec20 Download by: [UCL Library Services] Date: 28 January 2017, At: 23:05 APPLIED ECONOMICS, 2016 http://dx.doi.org/10.1080/00036846.2016.1229446 Performance assessment for electronic manufacturing service providers using two-stage super-efficiency SBM model Chia-Nan Wanga,b, Hsien-Pin Hsuc, Yen-Hui Wangd and Thi-Thu-Huyen Phame aNational Kaohsiung University of Applied Science, Kaohsiung, Taiwan; bTon Duc Thang University, Ho Chi Minh City, Vietnam; cNational Kaohsiung Marine University, Kaohsiung, Taiwan; dChihlee University of Technology, New Taipei City, Taiwan; eFoxconn Electronics Inc., Honi, Vietnam ABSTRACT KEYWORDS Manufacturing Service providers (EMSs) offer services to Original Equipment Manufacturers Data envelopment analysis; (OEMs). However, increasing challenges require an EMS to be more capable, adaptable and EMS industry; forecast; grey responsive. For survival, an EMS manager has to understand its relative efficiency in the industry. model; performance In addition, an investor also requires such information for investments. In this research, we assessment propose a novel approach, which combines GM(1,1) with a two-stage super-efficiency slacks- JEL CLASSIFICATION based measure (SBM) model, to forecast and assess the efficiencies of 18 EMSs. The GM(1,1) was C22; C53; C67; D78; M21 first used to forecast future data of EMSs, and then the two-stage super-efficiency SBM model was used to measure the marketability and profitability efficiencies for an EMU in two stages. The results build a ‘past-current-future’ view on the two efficiencies for each EMS. In addition, the profitability efficiency can help justify the reasonability of marketability efficiency. Our results showed that Hon Hai tops the rankings in both profitability and marketability efficiencies. These results also provided information about relative efficiencies of these EMSs, which helps EMS managers and investors to make better decisions. I. Introduction keeping pace with demand; (4) keeping the business model competitive and (5) managing geopolitical risk Equipment Manufacturing Service Providers (EMSs), as their biggest challenge. To deal with these chal- also known as Contract Manufacturer (CM), provide lenges, an EMS needs to understand its relative effi- subcontract services such as design, testing, manufactur- ciency among rivals in order to survive in the industry. ing to Original Equipment Manufacturers (OEMs). For investors, they also require such information for Since 1960s, OEMs started subcontracting services to better decision-making on investments. For these pur- EMSs in order to lower labour cost, increase profit and poses, an effective approach is required to assess and avoid risks, leading to the rapid increase of market share predict the performance of an EMS. for EMSs. Due to continuous efforts to advance their For an enterprise, understanding the future per- manufacturing capability, equipment and purchasing formance is probably more important than the power, the EMS has become an important industry. pastperformanceasthiscanhelpmangersforesee After many years of prosperity, however, many future results and they can immediately do some- EMSs today are facing difficulty of increasing more thing to change unacceptable results. There are market shares due to the trends of globalization and some approaches available for forecasting, includ- integration. In addition, factors such as more demand- ing the statistical methods and Grey Model (GM). ing customers, advancing technology, increasing Between the two approaches, the GM has the labour cost and shortening product lifecycles have advantage of requiring fewer data. To assess the put more challenges on EMSs. The International past as well as future performance of an EMS, an Global Manufacturing Outlook indicated that Top 5 effective methodology is definitely required. In challenges faced by electronic manufacturers include: past studies, Data Envelopment Analysis (DEA) (1) intense competition and prices pressure; (2) effi- has been widely used to assess the efficiency of ciency in research and development; (3) IT system an enterprise, termed decision-making unit CONTACT Hsien-Pin Hsu [email protected] National Kaohsiung Marine University, Kaohsiung, Taiwan © 2016 Informa UK Limited, trading as Taylor & Francis Group 2 C.-N. WANG ET AL. (DMU). For example, Charnes, Cooper and Ang, and Poh 2006; Hernández-Sancho, Molinos- Rhodes (1978) proposed CCR model to evaluate Senante, and Sala-Garrido 2011), we have thus pro- the efficiency of an enterprise while Banker et al. posed using a two-stage super-efficiency SBM model to (1984) proposed BCC model to measure the effi- assess the EMS performances, in addition to using GM ciency of a DMU. These models, however, are (1,1) for forecasting. based on the proportional reduction (enlargement) Our approach, which combines GM(1,1) with a of input (output) (Chang et al. 2013). The model two-stage super-efficiency SBM model, is novel as based on radial assumption has the following this kind of combination has never been used to weaknesses: (1) it does not give information assess the performances of EMSs in terms of profit- regarding the efficiency of the specific inputs or ability efficiency and marketability efficiency. To outputs and (2) it is difficult for ranking the per- facilitate empirical study, we have additionally pro- formance of the efficient DMUs. Differing to these posed an 8-step procedure. Following this proce- traditional models, Seiford and Zhu (1999)pro- dure, after determining the input and output posed a two-stage approach that broke down effi- variables, we have first selected 18 DMUs from the ciency into two components (efficiencies) that are Top 50 EMSs in the industry and collected their assessed in two respective stages. This kind of historical data from 2011 to 2014. Then, the GM models enabled authors to better understand the (1,1) was used to predict their input and output source(s) of inefficiency. However, in that study, a values from 2015 to 2017. After this, we employed traditional DEA model was employed. Recently, the two-stage super-efficiency SBM model to assess two-stage models have been increasingly appeared the efficiency of each DMU. In the first stage of the such as in Chen (2009), Zha and Liang (2010)and model, the equity and employees were used as inputs Song, Wang and Liu (2014). Especially, in Song, and revenue and profit were used as outputs and they Wang and Liu (2014), the authors proposed a two- assessed the profitability efficiency of each DMU. In stage slacks-based measure (SBM) model to assess the second stage, the outputs of the stage 1 served as the environmental efficiency. Their model was inputs and the return on capital (ROC) and market foundabletoprovideamoreprofoundanalysis value were used as outputs to assess the market- for decision-making. In a later study, Song and ability efficiency of each DMU. The results obtained Guan (2015) further proposed a super-efficiency from the two-stage approach gave a ‘past-current- SBM model to evaluate the e-government perfor- future’ view on the performance of each EMS in mance of environmental protection administra- terms of profitability and marketability efficiencies. tions in the 16 cities of the Anhui province in After analysis, some managerial/investment implica- China based on an analysis of their websites. tions are derived and presented in the conclusion Their results showed the capability of super-effi- section. ciency SBM model in discerning the efficiency of This rest part of this article is organized as fol- e-government web sites (DMUs). lows. Section II includes a literature review on the From our literature review, it is found that neither Grey system theory and DEA. Section III introduces two-stage approach nor super-efficiency SBM model the research methodology that includes Grey fore- has ever been used to assess the performances of an casting model GM(1,1) and the super-efficiency EMS. In addition, for an EMS, it is noted that profit- SBM model. Section IV conducts an empirical ability efficiency, which represents the capability of a study and analyses the results. Section V has a con- DMU in making profit, has never been used to justify clusion and suggestion on future research direction. the marketability efficiency that represents the market values of an EMS. Since our literature review showed that a two-stage model (approach) could provide more II. Literature review profound analysis for decision-making and a super- Grey system theory efficiency SBM model was capable in discerning the efficiency of an DMU (Song, Wang and Liu 2014), Introduction and related research meanwhile a slacks-based non-radial DEA model Grey system theory was introduced by Deng (1989). appeared to have higher discriminatory power (Zhou, In Grey system theory, a system totally unknown is APPLIED ECONOMICS 3 called ‘black’ system; a system totally known is called Forecasting accuracy ‘white’ system; a system partially known is called ‘grey’ A forecast always exists forecasting error that indi- system. Accordingly, most systems are ‘grey’ system cates the accuracy of forecasting model used. (Lin and Liu 2004). The Grey system theory has Forecasting error is defined as the difference become popular due to its simplicity of using fewer between forecasted and actual values.