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Artificial Intelligence Adoption in Procurement and Operational Performance of Manufacturing Firms

Published
Pages 58-72
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Authors

  • OWOPUTI, James Ayodele Department of Banking and Finance Rufus Giwa Polytechnic Owo Author
  • AKINTAN, Akinyemi Akindele Department of Business Administration Rufus Giwa Polytechnic Owo Author
  • OLUBUNMI, Olateru-Olagbegi Department of Office Technology Management Rufus Giwa Polytechnic Owo Author

DOI

https://doi.org/10.55640/ijssll-06-08-04 ↗

Abstract

The increasing complexity of global supply chains, coupled with mounting pressure on manufacturing firms to reduce cost, shorten lead times, and improve product quality, has pushed artificial intelligence (AI) to the forefront of procurement transformation. This study empirically examines the relationship between AI adoption in procurement and the operational performance of manufacturing firms. Building on an extensive review of recent literature (2020 to date), the study conceptualises AI adoption in procurement along three dimensions, namely AI-based supplier selection and evaluation, AI-driven demand forecasting and inventory optimisation, and AI-enabled spend analysis and contract management, and tests their effects on operational performance measured through cost, quality, delivery, and flexibility. The study is anchored on Dynamic Capabilities Theory, the Resource-Based View, and the Technology-Organisation-Environment (TOE) framework, and tests four hypotheses derived from an integrated conceptual framework in which implementation barriers, namely cost, digital skills, and data quality, are modelled as a moderating variable. Using a descriptive cross-sectional survey design, primary data were obtained from 345 procurement and operations managers within manufacturing firms registered with the Manufacturers Association of Nigeria and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results show that all three AI adoption dimensions have a statistically significant, positive effect on all four dimensions of operational performance, with demand forecasting and inventory optimisation exerting the strongest effect. Implementation barriers were found to significantly moderate the AI-performance relationship for cost and delivery outcomes, but not for quality and flexibility outcomes. The study concludes that AI adoption in procurement yields measurable operational performance gains for Nigerian manufacturing firms, and provides original, developing-economy, firm-level evidence on the AI-procurement-performance relationship and its boundary conditions.

Keywords

Artificial Intelligence , Procurement , Operational Performance , Manufacturing Firms , Structural Equation Modelling , Dynamic Capabilities

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References

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