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

• Published in: International journal of production research Vol. 54; no. 9; pp. 2757 - 2770
• Main Authors: Tsai, Juin-Ming, Hung, Shiu-Wan
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02.05.2016; Taylor & Francis LLC
• Subjects: Artificial neural networks; Decision making; Fluid flow; Mathematical models; Neural networks; Performance enhancement; Quality; relationship quality; Supply chain management; Supply chains; Turbulence; Turbulent flow
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2016.1140919

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.