BUILDING SUPPLY CHAIN RESILIENCE THROUGH ARTIFICIAL INTELLIGENCE: TRANSFORMING TOWARDS A DIGITAL SUPPLY CHAIN

Abstract

Purpose

This paper discusses how Artificial Intelligence (AI) can be used to improve the resilience of the supply chain and help the supply chain change to digital supply chains. It will determine the patterns of adoption, its use, obstacles, and how AI will influence the competitiveness and sustainability of organizations in the long term.

Design/Methodology/Approach

The quantitative research design was employed, with the help of a structured questionnaire, provided to 300 supply chain professionals working in various industries, such as manufacturing, logistics, retail and technology. The data were examined based on descriptive statistics, reliability testing, correlation analysis, regression, and ANOVA to determine the patterns of adoption, the contribution of resilience, and differences by organizational levels.

Findings

The findings suggest that although the use of AI is currently average, it is highly perceived by professionals that AI could contribute to enhancing the resilience and increasing visibility and improving decisions. The regression analysis revealed the most important contributors included visibility and predictive decision-making, which then came to be risk mitigation. It was common knowledge that there are barriers to AI implementation like high implementation costs, lack of skills, and organizational resistance, but this did not discourage hopefulness over the long-term benefits of AI.

Practical Implications

The study provides useful recommendations to managers, policy-makers and technology suppliers. It revolves around incremental adoption strategies, human capital development investments, enabling policy frameworks, and development of easily accessible and programmable AI solutions to industry needs.

Originality/Value

The study contributes to the growing trend in the literature on AI-based supply chain by positioning AI as a resiliency enabler and not necessarily an efficiency tool. It bridges the knowledge gap between the theoretical possibilities and the implementation challenges in real life, and gives an insight into how to create intelligent, adaptive, and future-ready supply networks.