Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685463
Title: A cloud manufacturing based approach to suppliers selection and its implementation and application perspectives
Author: Hassanzadeh, Soheil
ISNI:       0000 0004 5915 0660
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2016
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Abstract:
Multi-service outsourcing has become an important business approach since it can significantly reduce service cost, shorten waiting time, improve the customer satisfaction and enhance the firm’s core competence. In fact, on-demand cloud resources can lead manufacturers to improve their business processes and use an integrated and intelligent supply chain network. In addition, cloud manufacturing, as an emerging manufacturing system technology, will likely enable small and medium sized enterprises (SMEs) to move towards using dynamic scalability and ‘free’ available data resources in a virtual manner. Although there has been some research in these areas, there is still a lack of proper cloud based solutions for the whole manufacturing supply chain network. In addition, of the research papers studied, only a few reviewed and implemented the cloud based supply chain from a decision-making point of view, especially in suppliers evaluation and selection studies. Most studies only focused on cloud-based supply chain definitions, architectures, applications, advantages and limitations which can be offered to SMEs. Hence, a comprehensive research study to find an optimum set of suppliers for a number of goods and services required for a project within the cloud manufacturing context is necessary. Providing real and multi-way relationships through a suppliers selection process based on an intelligent cloud-based manufacturing supply chain network, by using the Internet, is the main aim of this research. The research has an emphasis on multi-criteria decision making approach. The proposed model is based on ‘Goal Integer 0-1 Programming’ method for the suppliers selection part and ‘Linear Programming’ method for the project planning part. The proposed framework consists of four modules, namely a) multi-criteria module, b) bidding module, c) optimisation module, and d) learning module. Learning module allows the model to learn about the suppliers’ past performance over the course of the system’s life. Average performance measures are calculated over a moving fixed period, results of which are stored in a ‘dynamic memory’ element as linked to the suppliers’ database. The methodological approach is validated based on a case study in the oil and gas industry, characterised by 29 services linked together in a network structure, 108 suppliers, and 128 proposals for the services. The case study covers a variety of services from designing to manufacturing and delivery. On the implementation side, a cloud manufacturing based suppliers selection system (OPTiSupply.uk®) is designed and uploaded on the virtual server of Amazon EC2. The system enables customers and suppliers to offer and receive various services on the Web. Apart from the user interface functionality, the system also allows interaction with the MS-Excel© based data and the associated mathematical programming.
Supervisor: Cheng, K. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.685463  DOI: Not available
Keywords: Cloud computing ; Supply chain management ; Mathematical programming ; Goal programming ; Project management
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