IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v24y2018i3d10.1007_s10588-017-9262-4.html
   My bibliography  Save this article

Fitting an uncertain productivity learning process using an artificial neural network approach

Author

Listed:
  • Toly Chen

    (National Chiao Tung University)

Abstract

Productivity is critical to the long-term competitiveness of factories. Therefore, the future productivity of factories must be estimated and enhanced. However, this is a challenging task because productivity can be improved based on a learning process that is highly uncertain. To address this problem, most existing methods fit fuzzy productivity learning processes and convert them into mathematical programming problems. However, such methods have several drawbacks, including the absence of feasible solutions, difficulty in determining a global optimum, and homogeneity in the solutions. In this study, to overcome these drawbacks, a specially designed artificial neural network (ANN) was constructed for fitting an uncertain productivity learning process. The proposed methodology was applied to an actual case of a dynamic random access memory factory. Experimental results showed that the ANN approach has a considerably higher forecasting accuracy compared with several existing methods.

Suggested Citation

  • Toly Chen, 2018. "Fitting an uncertain productivity learning process using an artificial neural network approach," Computational and Mathematical Organization Theory, Springer, vol. 24(3), pages 422-439, September.
    • Handle: RePEc:spr:comaot:v:24:y:2018:i:3:d:10.1007_s10588-017-9262-4
    DOI: 10.1007/s10588-017-9262-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-017-9262-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-017-9262-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Brandt, Loren & Van Biesebroeck, Johannes & Zhang, Yifan, 2012. "Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing," Journal of Development Economics, Elsevier, vol. 97(2), pages 339-351.
    2. Ulrich Doraszelski & Jordi Jaumandreu, 2013. "R&D and Productivity: Estimating Endogenous Productivity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(4), pages 1338-1383.
    3. Liu, Fuh-Hwa Franklin & Wang, Peng-hsiang, 2008. "DEA Malmquist productivity measure: Taiwanese semiconductor companies," International Journal of Production Economics, Elsevier, vol. 112(1), pages 367-379, March.
    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. Min-Chi Chiu & Tin-Chih Toly Chen & Keng-Wei Hsu, 2020. "Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    2. Yazhou Zhou & Yong Huang & Wenyuan Liu, 2024. "Understanding the Conflict between an Ecological Environment and Human Activities in the Process of Urbanization: A Case Study of Ya’an City, China," Sustainability, MDPI, vol. 16(15), pages 1-19, August.

    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. Toly Chen & Chungwei Ou & Yu-Cheng Lin, 2019. "A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 85-107, June.
    2. Brooks, Wyatt J. & Kaboski, Joseph P. & Li, Yao Amber & Qian, Wei, 2021. "Exploitation of labor? Classical monopsony power and labor's share," Journal of Development Economics, Elsevier, vol. 150(C).
    3. Li, Mingyang & Jin, Man & Kumbhakar, Subal C., 2022. "Do subsidies increase firm productivity? Evidence from Chinese manufacturing enterprises," European Journal of Operational Research, Elsevier, vol. 303(1), pages 388-400.
    4. Shengyu Liu & Heng Yin, 2017. "A Structural Method to Estimate Firm-level Capacity Utilization and Application to Chinese Heavy Industries," Annals of Economics and Finance, Society for AEF, vol. 18(1), pages 1-28, May.
    5. Redha Fares, 2022. "Bankruptcy, Performance and Market Selection: Evidence from Firms in France," Erudite Ph.D Dissertations, Erudite, number ph22-01 edited by Claude Mathieu.
    6. Chang, Chong-Chuo & Tang, Hui-Wen, 2021. "Corporate cash holdings and total factor productivity – A global analysis," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    7. Zhang, Hongsong, 2019. "Non-neutral technology, firm heterogeneity, and labor demand," Journal of Development Economics, Elsevier, vol. 140(C), pages 145-168.
    8. Yoonseok Lee & Mary E. Lovely & Hoang Pham, 2023. "Dynamic and non‐neutral productivity effects of foreign ownership: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 24-48, January.
    9. Zhifeng Wang & Guiying Laura Wu & Qu Feng, 2020. "Productivity of core infrastructure investment in China: An input–output approach," The World Economy, Wiley Blackwell, vol. 43(12), pages 3384-3406, December.
    10. Chen, Zhiyuan & Zhang, Jie & Zi, Yuan, 2021. "A cost-benefit analysis of R&D and patents: Firm-level evidence from China," European Economic Review, Elsevier, vol. 133(C).
    11. He, Ming & Chen, Yang & Schramm, Ronald M., 2016. "Agglomeration and Technological Spillovers: Firm-Level Evidence from China's Electric Apparatus Industry," RIEI Working Papers 2016-02, Xi'an Jiaotong-Liverpool University, Research Institute for Economic Integration.
    12. Walheer, Barnabe & Hudik, Marek, 2019. "Reallocation of resources in multidivisional firms: A nonparametric approach," International Journal of Production Economics, Elsevier, vol. 214(C), pages 196-205.
    13. Yuyu Chen & Mitsuru Igami & Masayuki Sawada & Mo Xiao, 2021. "Privatization and productivity in China," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 884-916, December.
    14. Wu, Guiying Laura & Feng, Qu & Wang, Zhifeng, 2021. "A structural estimation of the return to infrastructure investment in China," Journal of Development Economics, Elsevier, vol. 152(C).
    15. Huang, Minjie & Zhao, Shunan & Kumbhakar, Subal C., 2022. "Decomposition of Output, Productivity and Market Structure Changes," European Journal of Operational Research, Elsevier, vol. 303(1), pages 422-437.
    16. ZHANG,Hongyong & CHENG,Wenyin & LIANG,David Tao & Meng,Bo, 2024. "Industrial Subsidies along Domestic Value Chains and their Impacts on China’s Exports," IDE Discussion Papers 937, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    17. Andrés César & Guillermo Falcone, 2020. "Heterogeneous Effects of Chinese Import Competition on Chilean Manufacturing Plants," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 1-60, December.
    18. Laura Hospido & Eva Moreno-Galbis, 2015. "The Spanish productivity puzzle in the Great Recession," Working Papers 1501, Banco de España.
    19. Andrew Agopsowicz & Bassirou Gueye & Natalia Kyui & Youngmin Park & Mohanad Salameh & Ben Tomlin, 2017. "April 2017 Annual Reassessment of Potential Output Growth in Canada," Staff Analytical Notes 17-5, Bank of Canada.
    20. Laiqun Jin & Xiuyan Liu & Sam Hak Kan Tang, 2021. "High-Technology Zones, Misallocation of Resources among Cities and Aggregate Productivity: Evidence from China," Economics Discussion / Working Papers 21-11, The University of Western Australia, Department of Economics.

    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:spr:comaot:v:24:y:2018:i:3:d:10.1007_s10588-017-9262-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.