New Research Publication: Data-driven inventory control for large product portfolios

Our Team focusses on developing and studying novel approaches for AI-assisted Supply Chain Management. One of our recent papers (co-authored by Felix Schmidt and Richard Pibernik) recently got accepted for publication in the European Journal of Operational Research. 

Motivated by the real-world inventory management problem of a large network of pharmacies, the paper proposes and studies a practically relevant Prescriptive Analytics approach for data-driven dynamic inventory control of large portfolios of interrelated products. We extend existing research on weighted Sample Average Approximation by integrating a ‘global learning’ model that effectively exploits cross-learning opportunities within the product portfolio.

The results of an extensive numerical evaluation on real-world data suggest that our approach consistently outperforms all relevant benchmarks—in particular, models that rely on ‘local learning’ strategies where weight functions are trained separately for each product. The numerical results allow us to derive important practical and structural insights regarding the value of contextual information in our global learning framework.

The final paper with the title "Data-driven inventory control for large product portfolios: A practical application of prescriptive analytics" can be found here.