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Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach
Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2017.
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| Format: | Thesis |
| Language: | English English |
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University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering
2019
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| _version_ | 1867613634045149184 |
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| access_status_str | Open Access |
| author | Ingham, Michael |
| author_browse | Ingham, Michael |
| author_facet | Ingham, Michael |
| author_sort | Ingham, Michael |
| collection | Thesis |
| dc_rights_str_mv | © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/68391 |
| institution | University of Pretoria (South Africa) |
| language | English English |
| last_indexed | 2026-06-10T12:39:15.557Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering |
| publisherStr | University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/68391 Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach Ingham, Michael Mini-dissertations (Industrial and Systems Engineering) Bayesian change point detection Mining Metals Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2017. This paper identified change points in the production volumes and sales of various metals in South Africa from the period 2003-2016. The socioeconomic environment in which the mining sector operates is complicated, with countless factors influencing the production volumes and sales of metals. This complexity makes it difficult for mining stakeholders to accurately forecast the production and sales for specific metals. Possible causative events or factors were linked to identified change points in order to create a deeper understanding of the environment in which mining operates. This deeper understanding provides mining stakeholders with information on the events or factors that have the greatest impact on the performance of the various metals. This information allows mining stakeholders to focus forecasting efforts on the identified factors. Combining the focused forecasts with the impact that similar events had in the past helps the mining stakeholders to alter production levels or schedule investments before forecasted events take place, minimising the potential negative impact of said event. In this study, the monthly production volumes and sales of Gold, Platinum Group Metals (PGMs), Iron Ore and Manganese were analysed. The data spanned from 2003- 2016 and was supplied by StatsSA. The data was analysed using the Bayesian Change Point Analysis (BCP) [Barry and Hartigan, 1993], the Dynamic Programming Algorithm (DP)[Bai and Perron, 2003] and the Non- Parametric Multiple Change-Point Analysis (MCP) [Matteson and James, 2014]. The results reveal that production drops were caused predominantly by mining strikes and increases ii in production costs, while the sales were influenced by changes in the exchange rates and rand value of the commodity. Future studies will use sales volumes instead of Actual Rand values in order to identify changes that can be attributed to shifts in the demand of each metal. 2019-02-04T13:10:52Z 2019-02-04T13:10:52Z 2017 2017 Mini Dissertation http://hdl.handle.net/2263/68391 en en © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. PDF application/pdf University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering |
| spellingShingle | Mini-dissertations (Industrial and Systems Engineering) Bayesian change point detection Mining Metals Ingham, Michael Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach |
| title | Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach |
| title_full | Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach |
| title_fullStr | Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach |
| title_full_unstemmed | Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach |
| title_short | Investigating Sales and Production Volumes of Metals using a Bayesian Change Point Detection Approach |
| title_sort | investigating sales and production volumes of metals using a bayesian change point detection approach |
| topic | Mini-dissertations (Industrial and Systems Engineering) Bayesian change point detection Mining Metals |
| url | http://hdl.handle.net/2263/68391 |
| work_keys_str_mv | AT inghammichael investigatingsalesandproductionvolumesofmetalsusingabayesianchangepointdetectionapproach |