IDEAS home Printed from https://ideas.repec.org/a/cys/ecocyb/v50y2016i1p215-234.html
   My bibliography  Save this article

Development of Network-Ranking Model to Create the Best Production Line Value Chain: A Case Study in Textile Industry

Author

Listed:
  • ABBAS SHEIKH ABOUMASOUDI

    (Iran University of Science and Technology, Iran)

  • SAEED MIRZAMOHAMMADI AHMAD MAKUI

    (Iran University of Science and Technology, Iran)

  • AHMAD MAKUI

    (Iran University of Science and Technology, Iran)

  • JOLANTA TAMOŠAITIENĖ

    (Vilnius Gediminas Technical University, Lithuania)

Abstract

The main reason for creating value chain is fulfilling needs and organizational resources with the least cost and highest quality. Application of most of the current techniques has merely intended to choose the best scenario. But industrial units need to build an ideal scenario as a value chain which focuses on intangible interstitial and hidden factors: good (good nature), bad (bad nature), fixed (obligatory nature) and free (not identifying their nature) and creates value. Therefore, the model presented in this article answers this issue. First of all we present a model based on the network approach of data envelopment analysis, then we assess and rank the stages based on the scenarios for the stages forming the value chain and finally, the ideal decision unit is presented. For this reason, the general efficiency is designed with two natures; 1.input-centered (concentration on the costs) and 2.output-centered (concentration on the incomes).

Suggested Citation

  • Abbas Sheikh Aboumasoudi & Saeed Mirzamohammadi Ahmad Makui & Ahmad Makui & Jolanta Tamošaitienė, 2016. "Development of Network-Ranking Model to Create the Best Production Line Value Chain: A Case Study in Textile Industry," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 215-234.
  • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:1:p:215-234
    as

    Download full text from publisher

    File URL: ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb1_2016p215-234.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Anne Schaefer & Andreas Burger & Jürgen Moormann, 2012. "Sophisticating business performance management for banks: using data envelopment analysis on business process level," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 13(3/4), pages 227-243.
    3. André, Francisco J. & Herrero, Inés & Riesgo, Laura, 2010. "A modified DEA model to estimate the importance of objectives with an application to agricultural economics," Omega, Elsevier, vol. 38(5), pages 371-382, October.
    4. Lewis, Herbert F. & Lock, Kathleen A. & Sexton, Thomas R., 2009. "Organizational capability, efficiency, and effectiveness in Major League Baseball: 1901-2002," European Journal of Operational Research, Elsevier, vol. 197(2), pages 731-740, September.
    5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    6. Cook, Wade D. & Zhu, Joe & Bi, Gongbing & Yang, Feng, 2010. "Network DEA: Additive efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1122-1129, December.
    7. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2011. "A field-standardized application of DEA to national-scale research assessment of universities," Journal of Informetrics, Elsevier, vol. 5(4), pages 618-628.
    8. Akther, Syed & Fukuyama, Hirofumi & Weber, William L., 2013. "Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking," Omega, Elsevier, vol. 41(1), pages 88-96.
    9. Wolfgang Barth & Matthias Staat, 2008. "Restructuring the branch network of a bank: the dynamic perspective," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 2(3), pages 272-284.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chiu, Singa Wang & Liang, Gang-Ming & Chiu, Yuan-Shyi Peter & Chiu, Tiffany, 2019. "Production planning incorporating issues of reliability and backlogging with service level constraint," Operations Research Perspectives, Elsevier, vol. 6(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Guo-liang & Fukuyama, Hirofumi & Song, Yao-yao, 2018. "Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model," Journal of Informetrics, Elsevier, vol. 12(1), pages 10-30.
    2. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    4. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2018. "Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences," Journal of Informetrics, Elsevier, vol. 12(3), pages 784-805.
    5. Villa, G. & Lozano, S., 2016. "Assessing the scoring efficiency of a football match," European Journal of Operational Research, Elsevier, vol. 255(2), pages 559-569.
    6. Mergoni, Anna & Soncin, Mara & Agasisti, Tommaso, 2023. "The effect of ICT on schools’ efficiency: Empirical evidence on 23 European countries," Omega, Elsevier, vol. 119(C).
    7. Chi-Yo Huang & Min-Jen Yang & Jeen-Fong Li & Hueiling Chen, 2021. "A DANP-Based NDEA-MOP Approach to Evaluating the Patent Commercialization Performance of Industry–Academic Collaborations," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    8. Hirofumi Fukuyama & William L. Weber, 2017. "Japanese Bank Productivity, 2007–2012: A Dynamic Network Approach," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 649-676, October.
    9. Yang, Guo-liang & Fukuyama, Hirofumi & Chen, Kun, 2019. "Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach," Omega, Elsevier, vol. 84(C), pages 141-159.
    10. Phung, Manh-Trung & Cheng, Cheng-Ping & Guo, Chuanyin & Kao, Chen-Yu, 2020. "Mixed Network DEA with Shared Resources: A Case of Measuring Performance for Banking Industry," Operations Research Perspectives, Elsevier, vol. 7(C).
    11. Isidoro Guzmán-Raja & Manuela Guzmán-Raja, 2021. "Measuring the Efficiency of Football Clubs Using Data Envelopment Analysis: Empirical Evidence From Spanish Professional Football," SAGE Open, , vol. 11(1), pages 21582440219, February.
    12. Tavassoli, Mohammad & Faramarzi, Gholam Reza & Farzipoor Saen, Reza, 2014. "Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 146-153.
    13. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    14. Kao, Chiang, 2018. "Multiplicative aggregation of division efficiencies in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 270(1), pages 328-336.
    15. Li, Yongjun & Chen, Yao & Liang, Liang & Xie, Jianhui, 2012. "DEA models for extended two-stage network structures," Omega, Elsevier, vol. 40(5), pages 611-618.
    16. Plácido Moreno & Sebastián Lozano, 2014. "A network DEA assessment of team efficiency in the NBA," Annals of Operations Research, Springer, vol. 214(1), pages 99-124, March.
    17. Shamohammadi, Mehdi & Oh, Dong-hyun, 2019. "Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    18. Ching-Chin Chern & Tzi-Yuan Chou & Bo Hsiao, 2016. "Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis," Information Systems and e-Business Management, Springer, vol. 14(4), pages 823-856, November.
    19. Hsiao-Yen Mao & Wen-Min Lu & Hsin-Yen Shieh, 2023. "Exploring the Influence of Environmental Investment on Multinational Enterprises’ Performance from the Sustainability and Marketability Efficiency Perspectives," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    20. Marcel Clermont & Alexander Dirksen & Harald Dyckhoff, 2015. "Returns to scale of Business Administration research in Germany," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 583-614, May.

    More about this item

    Keywords

    Best Value Chain; Data Envelopment Analysis (DEA); Network-Ranking Models; Ideal Decision Making Unit.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • L67 - Industrial Organization - - Industry Studies: Manufacturing - - - Other Consumer Nondurables: Clothing, Textiles, Shoes, and Leather Goods; Household Goods; Sports Equipment

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cys:ecocyb:v:50:y:2016:i:1:p:215-234. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/feasero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.