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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
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    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.

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