Investigating total closed-loop supply chain variance amplification using a Monte-Carlo system dynamics method

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.