Improving Business Process Efficiency for Supply Chain Finance: Empirical Analysis and Optimization Based on Stochastic Petri Net

Abstract

Efficient business process is important to the operations of supply chain finance (SCF). Many deficiencies exist in the processes of SCF such as complicated workflows and high time-consuming steps. However, few studies have paid attention to evaluate and improve the performance of SCF processes. We empirically model and investigate the processes of supply chain finance by constructing a Stochastic Petri Net based on the field survey of a focal firm. Two critical indices, place busy rate and transition utilization rate, are evaluated. The results demonstrate that some places (transitions) of the Petri Net have high busy rates (utilization rates). By integrating the Petri Net and dependency graph, several key places and transitions in the Petri Net of SCF processes are identified for further optimization. To improve the performance of SCF processes, we propose three optional adjusting schemes on the basis of information sharing perspective (for the first half of the Petri Net) and risk sharing perspective (for the second half of the Petri Net). The proposed optimization strategies have further been proven to reduce the place busy rates, shorten the process, and improve the process efficiency of the supply chain finance.

Multi-Time Scale Smoothed Functional With Nesterov’s Acceleration

“…When parameters of the objective function or the optimization algorithm have randomness, then the process of optimization is termed as Stochastic Optimization (SO) [2]. It has applications in various fields such as machine learning [3], finance, supply chain [4], network optimization [5] and optimization with information uncertainty [6], [7]. These algorithms generally involve the estimation of the gradient of the objective.…”