Resilient Suppliers Selection System Using Machine Learning Algorithms And Risk Assessment Methodology
| dc.contributor.advisor | Zohdy, Mohamed A. | |
| dc.contributor.advisor | Olawoyin, Richard | |
| dc.contributor.author | Albadrani, Abdullah Meteb | |
| dc.contributor.other | Edwards, William | |
| dc.contributor.other | Ruegg, Erica | |
| dc.date.accessioned | 2022-11-15T17:47:41Z | |
| dc.date.available | 2022-11-15T17:47:41Z | |
| dc.date.issued | 2022-07-02 | |
| dc.description.abstract | Demand, supply, pricing, and lead time are all unpredictable in the manufacturingindustry, and the manufacturer must function in this environment. Because of the enormous amount of data available and the introduction of new technologies, such as theinternet of things (IoT), machine learning (ML), and Blockchain, administrators and government officials are better able to deal with uncertainty by applying intelligent decision-making principles to their situations. All supply chains must make use of new technology and analyze previous data to forecast and improve the success of future operations. At the moment, we rely on the supply chain and its facilities for healthcare when we receive our vaccine, for our food when we go grocery shopping, and for transportation when we drive our cars. Supplier selection is exposed to the three most significant factors: quality, delivery, and performance history, which are all evaluated separately. The use of data analytic capabilities in the selection of robust supplier portfolios has not been thoroughly investigated. Manufacturers typically have three to four resilient suppliers for the same item, but occasionally one or two of them will fail, causing a ripple effect throughout the entire supply chain. This is a frequent problem that the supply chain must deal with on a daily basis. Supply chain resilience, on the other hand, ignores or is incompatible with the risk profiles of suppliers' performance. | |
| dc.identifier.uri | http://hdl.handle.net/10323/12008 | |
| dc.relation.department | Computer Science and Engineering | |
| dc.subject | Industrial engineering | |
| dc.subject | Computer science | |
| dc.subject | Artificial intelligence | |
| dc.subject | Logistics | |
| dc.subject | Machine learning | |
| dc.subject | Supplier selection | |
| dc.subject | Supply chain | |
| dc.subject | System engineering | |
| dc.title | Resilient Suppliers Selection System Using Machine Learning Algorithms And Risk Assessment Methodology | |
| dc.type | Dissertation |