Curricular innovation for economic symbiosis: a neural network approach to aligning university supply chain programs with regional industry demands

Abstract

Purpose

The study aims to refine the local university’s supply chain management curriculum to meet regional industry demands, thus boosting the local economy.

Design/methodology/approach

Mixed-methods action research combined with neural network modeling was employed to align educational offerings with the needs of the local supply chain management industry.

Findings

The research indicates that curriculum revisions, informed by industry leaders and modeled through neural networks, can significantly improve the relevance of graduates' skills to the SCM sector.

Research limitations/implications

The study is specific to one region and industry, suggesting a need for broader application to verify the findings.

Practical implications

Adopting the recommended curricular changes can yield a workforce better prepared for the SCM industry, enhancing local business performance and economic health.

Social implications

The study supports a role for higher education in promoting economic vitality and social welfare through targeted, responsive curriculum development.

Originality/value

This study introduces an innovative approach, integrating neural network analysis with action research, to guide curriculum development in higher education based on industry requirements.

Citation

Daigle, J.L., Stading, G. and Hall, A. (2024), "Curricular innovation for economic symbiosis: a neural network approach to aligning university supply chain programs with regional industry demands", Higher Education, Skills and Work-Based Learning, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/HESWBL-11-2023-0309