International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 6 | Year 2025 | Article Id. IJCTT-V73I6P116 | DOI : https://doi.org/10.14445/22312803/IJCTT-V73I6P116

The Role of Generative AI in Retail Supply Chain Planning: Use Cases, Constraints, and Future Outlook

Lukesh Singla

Received Revised Accepted Published
06 May 2025 06 Jun 2025 22 Jun 2025 30 Jun 2025

Citation :

Lukesh Singla, "The Role of Generative AI in Retail Supply Chain Planning: Use Cases, Constraints, and Future Outlook," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 6, pp. 134-140, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I6P116

Abstract

This paper examines the disruptive effect of generative Artificial Intelligence (AI) on retail supply chain planning via a mixed-methods sequential explanatory study. Retailers are now confronted with unprecedented pressures from changing consumer expectations, global disruptions, and competitive threats, with generative AI bringing fresh solutions to improve forecasting precision, optimize stock management, and create resilience. This study combines a systematic literature review (n=97 articles), expert interviews (n=25), case studies (n=6 retailers), and a quantitative survey (n=217 retail supply chain professionals) to determine where generative AI provides quantifiable value in retail supply chains. The paper establishes a theoretical framework that describes how the adoption of generative AI is mediated by organizational preparedness factors and moderated by volatility in the marketplace. Findings show statistically significant gains in accuracy of forecasts (10-25%, p<0.001) and inventory efficiency (5-15%, p<0.01) across implementation instances, and qualitative results identify data quality, integration complexity, and organizational readiness as ongoing challenges. Theory is advanced by applying the Technology-Organization-Environment framework to include AI-specific constructs and through empirical validation of performance outcomes. For practitioners, this paper offers an empirically validated implementation framework with decision routes for various retail segments and organizational settings.

Keywords

Demand Forecasting, Generative AI, Inventory Optimization, Large Language Models, Mixed-Methods Research, Organizational Readiness, Retail Supply Chain, Technology Adoption.

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