Mining has a long history of production and operation management. Economies of scales have changed drastically and technology has transformed the mining industry significantly. One of the most important technological improvements is increased equipment, human, and plant tracking capabilities. This provided a continuous data stream to the decision makers, considering dynamic operational conditions. However, managerial approaches did not change in parallel. Even though many process improvement tools using equipment/human/plant tracking capabilities were developed (Fleet Management Systems, Plant Monitoring Systems, Workforce Management Systems etc.), to date there is no holistic approach or system to manage the entire value chain in mining. Mining operations are designed and managed around the already known system designated bottlenecks. However, contrary to common belief in mining, bottlenecks are not static. They can shift from one process or location to another. It is important for management to be aware of the new bottlenecks, since their decisions will be effected. Therefore, identification of true bottlenecks in real-time will help tactical level decisions (use of buffers, resource transfer), and identification of historical bottlenecks will help strategic-level decisions (investments, increasing capacity etc.). This thesis aims to address the managerial focus on the true bottlenecks. This is done by first identifying and ranking true bottlenecks in the system. The study proposes a methodology for creating Bottleneck Identification Model (BIM) that can identify true bottlenecks in a value chain in real-time or historically, depending on the available data. This approach consists of three phases to detect and rank the bottlenecks. In the first phase, the system is defined and variables are identified. In the second phase, the capacity, rates, and buffers are computed. In the third phase, considering particularities of the mine exceptions are added by taking mine characteristics into account, and bottlenecks are identified and ranked.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/566211 |
| Date | January 2015 |
| Creators | Kahraman, Muhammet Mustafa |
| Contributors | Poulton, Mary, Dessureault, Sean, Poulton, Mary, Dessureault, Sean, Son, Young-Jun, Kim, Kwangmin |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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