Speaker
Description
Abstract
The advent of AI has transformed various industries, with significant implications for manufacturing supply chain performance (SCP). This study aims to investigate the effects of AI-enabled factors on manufacturing SCP. Based on an extensive review of the literature, a conceptual framework integrating key AI-enabled factors and SCP was developed. Data were collected from 412 respondents from manufacturing industries in Ethiopia, and the hypothesized relationships in the proposed framework were empirically tested using Structural Equation Modeling (SEM). The Confirmatory Factor Analysis (CFA) results demonstrated that the measurement and structural models satisfied the recommended model-fit criteria, with χ²/df < 5, CFI, GFI, and TLI > 0.90, and RMR and RMSEA < 0.08. The findings of the study revealed that all five AI-enabled factors had positive and statistically significant effects on supply chain performance: AIF (β = 0.409, p = 0.001), AII (β = 0.309, p = 0.001), AIL (β = 0.245, p = 0.001), AIP (β = 0.209, p = 0.004), and AID (β = 0.609, p = 0.001). Among these factors, AID demonstrated the strongest effect on SCP. The novelty of this study lies in developing and empirically validating an integrated model that links multiple AI-enabled factors with manufacturing supply chain performance using SEM within the context of a developing country. The findings provide empirical evidence that AI-enabled capabilities can significantly enhance supply chain performance and offer practical insights for manufacturing firms seeking to improve supply chain efficiency, responsiveness, and competitiveness through AI adoption.
| Topic Area | Smart Operations and Maintenance |
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