Author: Nihar Sahay

Publisher:

Published: 2016

Total Pages: 239

ISBN-13:

Supply chain management (SCM) has been recognized as one of the key issues in the process industry. The growing size of the distributed supply chain structures, market dynamics and variability involved in the internal operations pose a challenge to efficiently managing the whole network. Globalization of supply chains and advances in information technology have led to a greater need for integrated operations as they have caused a more distributed network with potentially larger number of customers. It is essential that the various bodies constituting the supply chain operate in an integrated manner and their activities are synchronized towards a common goal. Thus, there is a need for efficient integration of information and decision making among the various functions of the supply chains. The growing need for integrated information and decision-making necessitates the development of a framework which allows the different entities of a supply chain to have access to a common information system as well as provides them with advanced decision-making tools. With the advancements in information technology, it is possible for supply chain members to share information and several such tools are also commercially available. However there is a need to combine intelligent decision making with information sharing to develop the required framework. The main objective of this dissertation is the development of novel methodologies that will facilitate intelligent decision-making and their application in the analysis of supply chains for chemical industries. Simulation models are used to depict supply chain dynamics so that they represent the decision-making by various entities. In order to obtain improved decision-making, a hybrid simulation based optimization framework is proposed. The framework considers the decision rules followed by the different entities and guides the simulation model towards improved solutions. The benefits of these methodologies include a more realistic representation of supply chain dynamics and reduced computational times for large-scale problems. The framework is applied to a number of case studies. Uncertainty in supply chain is also considered and the framework is used to determine the flexibility of the supply chain and manage risk under uncertainty. A derivative free optimization method is also proposed which has been applied to optimize the performance of a multi-enterprise supply chain network.