Investigating total closed-loop supply chain variance amplification using a Monte-Carlo system dynamics method
提出一种系统动力学随机排名指数方法，衡量闭环供应链中订单、现有库存和在制品库存的总方差放大效应，发现不同库存控制策略应基于方差放大的不同权重实施，且制造和再制造提前期及回收率的最优值在不同权重下相似。
Unwanted fluctuations and instability in operations can significantly increase the operational costs of closed-loop supply chains (CLSCs). Given the coexistence of variance amplifications of order, on-hand inventory, and work-in-process inventory in CLSCs, measuring the overall system variance amplification to support coordinated production and inventory decisions can support performance improvement. However, existing research has largely focused on variance amplification in order and on-hand inventory volumes, ignoring work-in-process variance which can also have significant economic implications. Also, most relevant studies examined the behaviour of each variance separately or adopted a weighted average of inventory and order variances with fixed, predetermined weights. Motivated by these gaps, we propose a novel System Dynamics Stochastic rank Index (SDSI) approach to analyse the total variance amplification of CLSCs. The results showed that different inventory control strategies should be implemented based on different weights among variance amplifications. However, the values of manufacturing lead times, remanufacturing lead times, and return rate that minimise the total variance amplification remain similar under different relative weights. Furthermore, the lead time and yield paradox phenomena were observed under the SDSI framework, suggesting relevant trade-offs between economic and sustainable operations in CLSCs.