Modelling viable supply networks with cooperative adaptive financing

TL;DR

This paper addresses liquidity-driven ripple risk in deep-tier supply networks by proposing a privacy-preserving, distributed control framework called distributed collaborative finance threshold control. Using a complex-agent, DAG-based CAS simulation, it demonstrates that limited yet strategically structured visibility across ego-networks can substantially improve long-term and indefinite viability, especially in scale-free topologies. The study introduces novel viability metrics ($LTVM$, $IVM$) and a nexus-theory–driven mechanism that dynamically sets financing thresholds via centrality-informed local information sharing, outperforming centralized control in scalability. The findings suggest practical policy implications: enabling privacy-preserving, decentralized interfirm financing governance can enhance resilience and autonomous adaptation in complex supply chains, with topology and visibility shaping viability outcomes.

Abstract

We propose a financial liquidity policy sharing method for firm-to-firm supply networks, introducing a scalable autonomous control function for viable complex adaptive supply networks. Cooperation and competition in supply chains is reconciled through overlapping collaborative sets, making firms interdependent and enabling distributed risk governance. How cooperative range - visibility - affects viability is studied using dynamic complex adaptive systems modelling. We find that viability needs cooperation; visibility and viability grow together in scale-free supply networks; and distributed control, where firms only have limited partner information, outperforms centralised control. This suggests that policy toward network viability should implement distributed supply chain financial governance, supporting interfirm collaboration, to enable autonomous control.

Figures (6)

• Figure 1: $E(100,0.1), \delta = 7$ supply network: A visualisation of a random, Erdoős Rényi, $E(n,p)$ graph topology with structural modifications to represent a deep tier supply chain. $n=100$, $p = 0.1$, and diameter, $\delta = 7$.
• Figure 2: $\text{BA}(100,3), \delta = 3$ supply network: A visualisation of a scale-free, Barabasi-Albert, $\text{BA}(n,m)$ graph topology with structural modifications to represent a deep tier supply chain. $n=100$, $m = 3$, $\delta = 3$.
• Figure 3: KDEs of Days Survived vs Information Reach for Scale Free Graphs: Vertical lines denote breakpoints separating short-lived supply chains and long-lived supply chains
• Figure 4: Regressions of $\text{RV}_D$ to $\Lambda^\text{SF}_D$, $\text{IVM}^\text{SF}_D$, and $\text{LTVM}^\text{SF}_D$ for scale-free networks
• Figure 5: KDEs of Days Survived vs Information Reach for Random Graphs: Vertical lines denote breakpoints separating short-lived supply chains and long-lived supply chains