On stochastic modelling of uncertainties in supply chain management under varying environments

Abstract

In recent decades, supply chain management has emerged as one of the most important operational strategies for improving the competitiveness of organizations. A supply chain is a network of activities that processes customer requirements into functional goods and delivers them to consumers. Typical supply chain management systems necessitate complete information about the model (e.g., the influence of price factor on customer demand). However, in reality, some or all of the information related to the business cannot be deterministic or certain, and thus dealing with uncertainties is one of the most significant challenges that must be addressed and is the primary motivation for this thesis. Any business with a well-managed supply chain that is flexible to respond to uncertainty will substantially reduce the risks associated with that chain, resulting in a better profit. Thus, the analysis of supply chain uncertainties is essential to the economic growth of a country. Therefore, the objective of this thesis is to represent uncertainties in supply chain management problems using appropriate probabilistic distributions through stochastic modelling. The thesis is categorized into three segments, each of which discusses different sources of uncertainty in the forward, reverse, and closed-loop supply chains. The first part of the thesis studies the maintenance policy for a degenerative repairable forward supply chain system that accounts for the uncertainty of supplier disruptions caused by unanticipated crisis events. By employing the and#948;-shock model, the forward supply chain structure is examined, for a single and dual supplier encountering random shocks, via quasi renewal and phase type geometric processes. Through and#948;-shock models, repair and reengineering techniques are proposed for repairable supply chain systems which are limited to the analysis of single and dual supplier sources.