Artificial Intelligence (AI) Adoption in Supply Chain Management Dynamics of Manufacturing Firms in Emerging Markets
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Abstract
While big firms in developed countries have embraced Artificial Intelligence (AI) for Supply Chain Management (SCM), the same cannot be said for firms in emerging markets, hence necessitating this study to examine AI adoption in plastic manufacturing firms in emerging markets, as a broad objective. The study relied on secondary qualitative data from peer-reviewed journals published between 2020-2025. Data collection followed a structured literature review protocol, and findings were analyzed thematically. The thematic analysis for the first objective, which sought to identify the types of AI applicable in SCM, indicated a clear set of AI technologies applicable to SCM in plastic firms, including machine learning, robotics, computer vision, and natural language processing. The result for objective two, which sought to determine the prospects of adopting AI in SCM plastic manufacturing firms in emerging markets, showed that plastic firms that adopt AI for their SCM stand to gain from more accurate forecasting, improved quality, lower costs, and stronger competitiveness. Findings for objective three, which assessed challenges of AI adoption in SCM in plastic manufacturing firms in emerging markets, revealed a set of interrelated barriers, including economic (costs), infrastructural (power/connectivity), technical (data availability/quality), human (skills and resistance), and institutional (security/privacy and policy). The study concluded that indeed, there are several areas AI can be adopted in SCM in manufacturing firms in emerging markets, and that when deployed, they stand to gain massively, notwithstanding the challenges they could face while attempting to adopt it. The study, therefore, among others, recommended that plastic manufacturing firms in emerging markets need to adopt practical AI tools for demand forecasting, warehouse automation, and quality control to improve efficiency, reduce waste, and enhance responsiveness.
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Copyright (c) 2025 Okereke Chukwuemeka Chidiadi, Arachie Augustine Ebuka, Onah Fortunatus Sochima, Ndum Ngozi Blessing (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
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