Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability

Syed Mithun Ali (), Amanat Ur Rahman, Golam Kabir and Sanjoy Kumar Paul
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Syed Mithun Ali: Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
Amanat Ur Rahman: Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Golam Kabir: Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada
Sanjoy Kumar Paul: UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia

Sustainability, 2024, vol. 16, issue 6, 1-31

Abstract: The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers.

Keywords: artificial intelligence; artificial neural network; balanced scorecard; grey theory; supply chain performance analytics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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