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Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach

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  • Juin-Ming Tsai
  • Shiu-Wan Hung

Abstract

A well-functioning supply chain management relationship cannot only develop seamless coordination with valuable members, but also improve operational efficiency to secure greater market share, increased profits and reduced costs. An accurate decision-making system considering multifactor relationship quality is highly desired. This study offers an alternative perspective and characterisation of the supply chain relationship quality and performance. A decision-making model is proposed with an artificial neural network approach for supply chain continuous performance improvement. Supply chain performance is analysed via a supervised learning back-propagation neural network. An ‘inverse’ neural network model is proposed to predict the supply chain relationship quality conditions. Optimal performance parameters can be obtained using the proposed neural network scheme, providing significant advantages in terms of improved relationship quality. This study demonstrates a new solution with the combination of qualitative and quantitative methods for performance improvement. The overall accuracy rate of the decision-making model is 88.703%. The results indicated that trust has the greatest influence on the supply chain performance. Relationship quality among supply chain partners impacts performance positively as the pace of technological turbulence increases.

Suggested Citation

  • Juin-Ming Tsai & Shiu-Wan Hung, 2016. "Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach," International Journal of Production Research, Taylor & Francis Journals, vol. 54(9), pages 2757-2770, May.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:9:p:2757-2770
    DOI: 10.1080/00207543.2016.1140919
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    Cited by:

    1. Ma, Rui & Mao, Di & Cao, Dongmei & Luo, Shuai & Gupta, Suraksha & Wang, Yichuan, 2024. "From vineyard to table: Uncovering wine quality for sales management through machine learning," Journal of Business Research, Elsevier, vol. 176(C).
    2. Sedov, A.V. (Седов, А.) & Chelyshkov, P.D. (Челышков, П.) & Rujitskaya, S.A. (Ружицкая, С.) & Solntseva, M.G. (Солнцева, М.), 2016. "The European Concept of 'Smart City' [Европейская концепция "Умного города - Smart City"]," Working Papers 1652, Russian Presidential Academy of National Economy and Public Administration.
    3. Afraz, Muhammad Fawad & Bhatti, Sabeen Hussain & Ferraris, Alberto & Couturier, Jerome, 2021. "The impact of supply chain innovation on competitive advantage in the construction industry: Evidence from a moderated multi-mediation model," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    4. Pourvaziri, H. & Sarhadi, H. & Azad, N. & Afshari, H. & Taghavi, M., 2024. "Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    5. Huynh Thi Thu Suong & Tieu Van Trang, 2021. "Adoption of Supply Chain Model to Improve Terminal Device Distribution at Mobifone: Evidence from Vietnam," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 3-18.
    6. v. Alberti-Alhtaybat, Larissa & Al-Htaybat, Khaldoon & Hutaibat, Khalid, 2019. "A knowledge management and sharing business model for dealing with disruption: The case of Aramex," Journal of Business Research, Elsevier, vol. 94(C), pages 400-407.
    7. Gabueva, Larisa A. (Габуева, Лариса) & Pavlova, Nina F. (Павлова, Нина), 2017. "Foreign and Domestic Trends in the Development of Competition in Health Care: Recognition and Measurement of Professional Reputation [Зарубежные И Отечественные Тренды Развития Конкуренции В Здраво," Working Papers 021705, Russian Presidential Academy of National Economy and Public Administration.

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