Unfolding the impact of supply chain quality management practices on sustainability performance: an artificial neural network approach

Abstract

Purpose

In today’s globalized and heavily industrialized economy, sustainability issues that negatively affect the human population and external environment are on the rise. This study aims to investigate a synergistic combination of supply chain management and quality management practices in strengthening the sustainability performance of Malaysian manufacturing firms.

Design/methodology/approach

A total sample of 177 usable surveys was collected. Given the contributions and acceptability of the artificial neural network (ANN) approach in evaluating the findings of this study, this study uses ANN to measure the relationship between each predictor (i.e. supply chain integration [SCI], quality leadership [QL], supplier focus [SF], customer focus (CF) and information sharing [IS]) and the dependent variable (i.e. sustainability performance). Via sensitivity analysis, the relative significance of each predictor variable is ranked based on the normalized importance value.

Findings

The sensitivity analysis indicates that CF has the greatest effect on sustainability performance (SP) with 100% normalized relative importance, followed by QL (75%), IS (61.5%), SF (57.3%) and SCI (46.7%).

Originality/value

The findings of this study have the potential to provide valuable guidance and insights that can help all manufacturing firms enhance their SP from the optimum combination of the selected SCQM practices with a focus on sustainability.

Citation

Lim, A.-F., Lee, V.-H., Foo, P.-Y., Ooi, K.-B. and Wei–Han Tan, G. (2022), "Unfolding the impact of supply chain quality management practices on sustainability performance: an artificial neural network approach", Supply Chain Management, Vol. 27 No. 5, pp. 611-624. https://doi.org/10.1108/SCM-03-2021-0129