This paper focuses on performance assessment of a freight network with stochastic capacities by using reliability analysis, in which a node denotes a supplier, a transfer center, a distribution center, or a market, while an arc denotes a logistics service provider offering the freight traffic service for a pair of nodes. Due to some uncertainties in real environment, the available capacity of the provider along each arc is stochastic. Thus, network reliability that at least d units of commodity demand can (...) be successfully delivered from the source to the destination denotes the operational performance of such a freight network. A d-minimal path–based method is developed to evaluate the freight network reliability. To advance the efficiency of solving d-minimal paths, an improved model is established by redefining capacity bounds of arcs and minimal paths. Furthermore, a new concept of expected capacity grounded on network reliability is presented to measure the service performance of the freight network. A practical case related to the coal delivery network is studied to demonstrate the implications of both network reliability and expected capacity. (shrink)

We investigate two main sources of information inaccuracies in demand information sharing along the supply chain. Firstly, we perform a systematic literature review on inaccuracy in demand information sharing and its impact on supply chain dynamics. Secondly, we model several SC settings using system dynamics and assess the impact of such information inaccuracies on SC performance. More specifically, we study the impact of four factors using three SC dynamic performance indicators. The results suggest that demand error has a negative impact (...) on SC performance, which is exacerbated by the magnitude of the error and by low demand variability scenarios. In contrast, demand delay produces a nonlinear behavior in the supply chain response, being influenced by the supply chain configuration. (shrink)