Data-driven robust strategic sourcing considering supply-side competition: Insights into blockchain adoption for coordination

Abstract

Given the increasing vulnerability of global supply chains (SCs) to disruptions, improving resilience through strategic sourcing is crucial for maintaining continuity and adaptability in dynamic markets. Integrating blockchain technology (BT) can further support these efforts by ensuring data integrity, transparency, and real-time visibility across SCs. This study develops a data-driven robust multi-objective programming (DDRMOP) model to explore the role of BT in designing sourcing strategies and creating effective partnerships in the face of uncertainties. The DDRMOP employs a data-driven robust optimization approach utilizing principal component analysis and robust kernel density estimation to formulate uncertainty sets for market demand. The model aims to minimize SC coordination costs, defective rates, and delivery delays while enhancing sourcing efficiency by selecting the most sustainable and BT-friendly suppliers. A Nash game-enabled data envelopment analysis is incorporated into this model to investigate sourcing efficiency under competitive dynamics and demand uncertainty simultaneously. This integration provides insights into how these dynamics influence the trade-off between cost efficiency and SC resilience. As the DDRMOP model includes three conflicting objectives, the augmented ε-constraint method is adopted to analyze the impact of each function on strategic sourcing across multiple products. The findings highlight the importance of BT and sustainability in forming reliable partnerships between the buyer and suppliers to enhance dynamic SC capabilities during disruptions. BT-friendly suppliers are preferred for their alignment with sustainability and information coordination goals. Although supply-side competition may increase coordination costs and operational complexities, it ultimately improves overall sourcing efficiency.