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dc.contributor.author | Hamdan, Maysaa M. | en_US |
dc.date.accessioned | 2015-07-31T22:10:22Z | |
dc.date.available | 2015-07-31T22:10:22Z | |
dc.date.submitted | January 2015 | en_US |
dc.identifier.other | DISS-13202 | en_US |
dc.identifier.uri | http://hdl.handle.net/10106/25131 | |
dc.description.abstract | The Build-to-Order supply chain (BTO-SC) is one agile supply chain that has received great attention in research and industry. Flexibility and responsiveness in mass customization has become a major objective of many companies that lead to the development of BTO-SC. Knowing the efficiency of the BTO-SC and the decision making units responsible for controlling the production process and decision making at different managerial levels is important for resources utilization to stay competitive in such a global market. The main purpose of this study is to measure, compare and analyse the impact of BTO and traditional supply chain on the company's performance and highlight the performance measures that can influence on the decision making. A methodology for analysing the efficiency of the BTO-SC is developed using Stochastic Frontier Analysis (SFA) compared to the traditional supply chain in which both are then compared to the deterministic Data Envelopment Analysis (DEA) model. The targeted population was computer industries functioning in BTO-SC or/ and traditional supply chain network. Such a comparative analysis could provide a concrete investment guide of companies start to adopt BTO principles.KeywordsBuild to order supply chain, Performance measures, Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA). | en_US |
dc.description.sponsorship | Rogers, Jamie | en_US |
dc.language.iso | en | en_US |
dc.publisher | Industrial & Manufacturing Engineering | en_US |
dc.title | Build-to-order Supply Chain Efficiency Using Stochastic Frontier Analysis | en_US |
dc.type | Ph.D. | en_US |
dc.contributor.committeeChair | Rogers, Jamie | en_US |
dc.degree.department | Industrial & Manufacturing Engineering | en_US |
dc.degree.discipline | Industrial & Manufacturing Engineering | en_US |
dc.degree.grantor | University of Texas at Arlington | en_US |
dc.degree.level | doctoral | en_US |
dc.degree.name | Ph.D. | en_US |
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