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A proposed framework for supply chain analytics using customer data
Thesis (PhD (Business Management))--University of Pretoria, 2022.
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| Format: | Thesis |
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University of Pretoria
2023
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| _version_ | 1867613621335359488 |
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| access_status_str | Open Access |
| author2 | Das, Sonali |
| author_browse | Das, Sonali |
| author_facet | Das, Sonali |
| collection | Thesis |
| dc_rights_str_mv | © 2022 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 | Thesis (PhD (Business Management))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/90061 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:39:00.799Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/90061 A proposed framework for supply chain analytics using customer data Das, Sonali u29023051@tuks.co.za Phadi, Nteboheng Pamella UCTD Supply chain analytics Customer data Digital supply chain Petroleum consumption SCOR Predictor Thesis (PhD (Business Management))--University of Pretoria, 2022. The COVID-19 pandemic and recent geopolitical events have called for a need to re-evaluate methodologies for Supply Chain Risk management. Significant investment in supply chain technology has resulted in data being generated throughout the value chain. Customer data, specifically, is of interest in order to establish customer-centricity and an enhanced customer journey. However, the transformation of this data to insight is not obvious for some organisations. Forecasting models are typically used to inform decision-making, mitigate risks and enlighten policymakers. This thesis aims to address this challenge by proposing a set of capabilities that will enhance the integration of the supply chain network to its customer data. Given this context, two methodologies were used to address the research problem; (i) multinational petrochemicals company was considered for our case study and a web-based survey was distributed among key stakeholders at their head offices in South Africa. A structured equation model (SEM) was constructed to empirically test the proposed relationships among the constructs, specifically: People, Process and Technology capabilities; (ii) The macro-economic factors that drive customer demand also considered. Increasing crude oil prices have increased logistics costs and have incited the deglobalization of supply chain operations. A novel petroleum forecasting model is also proposed, particularly focusing on the forecasting on South Africa’s petrol and diesel consumptions. The model uses indices for Brent crude oil price (ZAR), Gross Domestic Product (GDP), Rand to Dollar exchange rate, Consumer Confidence Index (CCI) and Business Confidence Index (BCI) data as input data. Overall, this study suggests that in order to effectively serve their customers, organisations need to establish a culture of customer centricity that is underpinned by appropriate supply chain analytics techniques. The predictive model further highlights the need to establish the relationship between the organisation’s supply chain and micro and macro-economic drivers. Business Management PhD (Business Management) Unrestricted 2023-03-09T12:45:32Z 2023-03-09T12:45:32Z 2023-05-10 2022 Thesis * A2023 https://repository.up.ac.za/handle/2263/90061 © 2022 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. application/pdf University of Pretoria |
| spellingShingle | UCTD Supply chain analytics Customer data Digital supply chain Petroleum consumption SCOR Predictor A proposed framework for supply chain analytics using customer data |
| title | A proposed framework for supply chain analytics using customer data |
| title_full | A proposed framework for supply chain analytics using customer data |
| title_fullStr | A proposed framework for supply chain analytics using customer data |
| title_full_unstemmed | A proposed framework for supply chain analytics using customer data |
| title_short | A proposed framework for supply chain analytics using customer data |
| title_sort | proposed framework for supply chain analytics using customer data |
| topic | UCTD Supply chain analytics Customer data Digital supply chain Petroleum consumption SCOR Predictor |
| url | https://repository.up.ac.za/handle/2263/90061 |