Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.712254
Title: Global dual-sourcing strategy : is it effective in mitigating supply disruption?
Author: Ahmad Mustaffa, Nurakmal
ISNI:       0000 0004 6062 7729
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2015
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Abstract:
Most firms are still failing to think strategically and systematically about managing supply disruption risk and most of the supply chain management efforts are focused on reducing supply chain operation costs rather than managing disruption. Some innovative firms have taken steps to implement supply chain risk management (SCRM). Inventory management is part of SCRM because supply disruptions negatively affect the reliability of deliveries from suppliers and the costs associated with the ordering process. The complexity of existing inventory models makes it challenging to combine the management of the supply process and inventory in a single model due, for example, to the difficulty of including the characteristics of the disruption process in the supply chain network structure. Therefore, there is a need for a simple flexible model that can incorporate the key elements of supply disruption in an inventory model. This thesis presents a series of models that investigate the importance of information on disruption discovery and recovery for a firm’s supply and inventory management. A simple two-echelon supply chain with one firm and two suppliers (i.e., referred to as the onshore and offshore suppliers) in a single product/component setting has been considered in this thesis for the purpose of experimental analyses. The sourcing decisions that the firm faces during periods of supply disruption are examined leading to an assessment of how information about the risk and length of disruption and recovery can be used to facilitate the firm’s sourcing decisions and monitor the performance of stock control during the disruption. The first part of this thesis analyses basic ordering models (Model 1 and Model 2 respectively) without the risk of supply disruption and with the risk of supply disruption. The second part analyses the value of supply disruption information, using a model with advance information on the length of disruption (Model 3) and a model with learning about the length of disruption (Model 4). The third part explores a quantitative recovery model and the analyses in this part consider of three models. Model 5 assumes a basic phased recovery model, Model 6 assumes advance information about the phased recovery process and Model 7 assumes learning about the phased recovery process. The last part of this thesis investigates the order pressure scenario that exists in the firm’s supply chain. Under this scenario, disruption to one part of the supply chain network increases demand on the remainder resulting in a lower service levels than normal. This scenario is applied to all the previous models apart from Model 1. The models in this thesis are examined under finite and infinite planning horizons and with constant and stochastic demand. The objective of the models is to minimise the expected inventory cost and optimise the order quantity from the suppliers given the different assumptions with respect to the length of supply disruption and information about the recovery process. The models have been developed using the discrete time Markov decision process (DTMDP) technique and implemented using the Java programming language. The findings of this thesis could be used to help a firm that is facing the risk supply disruption to develop its SCRM program. The findings highlight the importance of considering quantitative measures of the disruption and recovery processes, something which is still not popular within SCRM in some organisations.
Supervisor: Archibald, Tom ; Andreeva, Galina Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.712254  DOI: Not available
Keywords: supply disruption ; discrete time Markov decision process ; DTMDP
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