Artificial Intelligence and the Business Revolution: A Systematic Literature Review of Transforming Supply Chain Management Practices in South Africa

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI’s influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI’s transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth.

Publication Details

Journal
RePEc: Research Papers in Economics
Published
2024-09-01
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence and the Business Revolution: A Systematic Literature Review of Transforming Supply Chain Management Practices in South Africa

RePEc: Research Papers in Economics
Supply Chain Resilience and Risk Management
article

Artificial Intelligence and the Business Revolution: A Systematic Literature Review of Transforming Supply Chain Management Practices in South Africa

article en

Abstract

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI’s influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI’s transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth.

RePEc: Research Papers in Economics
Partnerships for the goals
Openalex Percentile: Top 34%
Supply Chain Resilience and Risk Management
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Artificial Intelligence and the Business Revolution: A Systematic Literature Review of Transforming Supply Chain Management Practices in South Africa · RePEc: Research Papers in Economics (2024) | TGRS Research Map | TGRS