Business and Social Sciences
Business and social sciences | Online ISSN 3067-8919
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Volume 3 Number 1 2025
RESEARCH ARTICLE (Open Access)
Comparative Econometric Analysis of AI-Based and Stochastic Inventory Optimization for Mitigating Inflationary Supply Shocks
Md Nazmuddin Moin Khan1*, Md. Rezaul Haque2
Business and Social Sciences 3 (1) 1-8 https://doi.org/10.25163/business.3110484
Submitted: 03 November 2024 Revised: 01 January 2025 Accepted: 08 January 2025 Published: 10 January 2025
Abstract
Background: The success of inventory management systems depends on their ability to handle supply disruptions which become more complex during periods of changing inflation rates. Traditional stochastic models deliver stability but their adaptability remains restricted because they show delayed responses to actual demand fluctuations.
Methods: The study collected operational metrics through a cross-sectional survey of 270 U.S. supply chain professionals who worked in manufacturing (45.2%) and retail (35.1%) and logistics (14.8%) and wholesale (4.9%) sectors. AI-based and stochastic models were evaluated under 3–15% inflation scenarios. The analysis used Pearson correlation (r) together with regression coefficients (β) and χ² tests and p-values (p=0.05) to measure how each variable affected cost efficiency and forecast accuracy and service-level stability.
Results: AI models consistently outperformed stochastic approaches across all metrics. The efficiency distributions show that AI systems reached forecast accuracy rates of 63% and backorder reduction rates of 62% which exceeded the 37–38% performance of stochastic models. The Pearson correlation analysis revealed strong to moderate connections between inflation rate (r=0.61) and backorder reduction (r=0.62) and inventory turnover (r=0.60). The regression analysis shows AI delivers major cost efficiency improvements through its influence on cost efficiency β= –0.462 while inflation index β=0.291, lead-time variance β=0.179, and service level β=–0.239, generate effects on the results.
Conclusion: AI-based inventory optimization improves responsiveness, reduces costs, and maintains service-level stability under inflationary supply shocks. The system delivers better predictive and adaptive results than stochastic models which help organizations handle their supply chains in real time.
Keywords: Cost Efficiency, AI Inventory Optimization, Service-Level Stability, Stochastic Models, Inflationary Supply Shocks
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