Flexible supply chain planning based on variable transportation modes

Abstract

This paper investigates the application of diverse transportation modes for a global supply chain (SC) in stochastic environments. The motivation of our paper is to investigate the idea of enabling a global flexible SC with disruptive risks in making it less vulnerable by applying diverse transportation modes which is also our first contribution. The flexibility stems from the fact that transportation modes with a low-speed transportation contain latent time buffers that can be used by accelerating transport activities. This represents a promising approach to make supply chains (SCs) more flexible and to establish an additional degree of freedom in order to manage stochastic events like minor disruptions or serious catastrophes. In this paper, a stochastic programming model for a multi-stage multi-product SC is developed. SC partners, including multiple suppliers, a processing center, two assembling centers, multiple distribution centers and retailers, are incorporated into the model. The second contribution of this paper is that different types of possible future catastrophic disruptions are quantified and included in the model. SC catastrophic disruptions like transportation delays or the fact that a SC node is disrupted by a serious catastrophe are stochastic factors of our model. The model is solved by using PySP, a specific modeling and stochastic programming framework. In order to show the quality of solutions of the stochastic programming model (SP solutions), a large amount of scenarios is generated to simulate the real case for each instance. The expected SC costs for these scenarios will be evaluated based on SP solutions and wait-and-see solutions, which are benchmarks. In addition, decision makers with neutral, optimistic and pessimistic attitudes regarding the occurrence of disruptions are also simulated and evaluated in the computational experiments. Managerial insights are concluded from computational results. The most important conclusion is that proper transportation mode planning enables a flexible global supply chain. Further conclusions like the quality of stochastic solutions and solutions of simulating decision makers with neutral, optimistic and pessimistic attitudes, as well as the most beneficial transportation modes in SCs with uncertain environments are proposed based on the computational results.