Meta-prediction model for introducing lateral transshipment policies in a retail supply chain network through regression analysis

Abstract

This study finds a way to alleviate and predict the effects of uncertainty in a retail supply chain network through lateral transshipment policies. The study develops a simulation-based optimisation model using the ARENA and OptQuest optimisation tool, and proposes an improved application of the lateral transshipment policy, in addition to four existing policies, to solve such uncertainty. A series of experiments is performed, varying significant cost parameters to study their effects on the profit of the whole chain. In order to determine the best transshipment policy and predict its financial performance, a proposed meta-prediction model based on regression analysis is used to assist in making decisions on the implementation of different types of transshipment policies under various cost scenarios. The proposed methodology assists in decision making on the selection and management of a lateral transshipment policy for a retail supply chain network in a certain situation under an uncertain environment. [Received 20 March 2016; Revised 8 September 2016; Revised 7 December 2017; Accepted 13 December 2017]