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2. PHD Thesis / Tesis PHD
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Title:	Automated knowledge acquisition framework for supply chain management based on integrating case-based reasoning and intelligent Agent
Authors:	Mohammad Zayed Almuiet (P52686)
Supervisor:	Juhana Salim, Prof. Dr.
Keywords:	Knowledge acquisition (Expert systems)
Issue Date:	15-Feb-2018
Description:	Knowledge Acquisition (KA) refers to process that is used to acquire the knowledge from inside and outside the firms. Organizations and industrial companies realized the need for knowledge to be acquired in order to solve many problems associated with the manufacturing process, such as slow process techniques, high material consumption, and poor machine quality. KA has been examined from the theoretical perspective to indicate its relationship and impact on Supply Chain Management (SCM). Very few research have studied KA automation to simplify KA. Presently, there has been no framework that addresses the automation of KA in SCM in terms of knowledge acquiring, reusing and storing. Various frameworks in SCM have been proposed by researchers based on Case Based Reasoning (CBR) and Intelligent Agent (IA) perspective. This study developed an Automated KA Framework (AKAF) as a guide to providing KA benefits in the SCM through the understanding of the knowledge types, the functions of the supply chain, and the integration of artificial intelligence approach, which is CBR and IA. This study considers three underpinning theories to motivate the premise that supply chain knowledge management and integrating CBR-IA will improve the acquisition, storage and reuse of knowledge and they are, knowledge-based theory, resource-based theory and Nonaka’s model. The study aims to achieve four research objectives based on five research questions. Data was gathered in a span of six months (January-June 2015), using semi-structured interviews conducted with senior officials, executive managers, shop-floor employees and customers (supply chain partners) in order to design a Framework for the Automated Knowledge Acquisition in Supply Chain Management (FAKASCM). Data was analyzed using descriptive and thematic analysis. FKASCM was designed based on three parts: the first part is the supply chain knowledge that exist in SCM, and among the supply chain partners. The second part is the knowledge modelling based on supply chain functions. The last part integrates CBR-IA. The Automated Knowledge Acquisition in SCM Prototype (AKASCMP) was developed using prototyping life cycle method to validate the proposed framework. The data for evaluation of AKASCMP was collected from November 2016 to April 2017 via a structured questionnaire survey which yielded 30 usable questionnaires and interview with the 6 experts to validate the framework. The evaluation had its basis on the usability and experiments, with the former done through experts’ interviews and the latter done based on similarity function to evaluate the integration of CBR and IA. The results of this study showed that the supply chain knowledge could be acquired, stored and reused by the acceptability of the AKASCMP. It was found that the experimental result of integrating CBR and IA is effective in terms of acquisition, storing and reusing of the supply chain knowledge.,Certification of Master's/Doctoral Thesis' is not available
Pages:	285
Call Number:	QA76.76.E95A436 2018 3 tesis
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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