Managing operational uncertainty in manufacturing with industry 4.0 and 5.0 technologies: a modified neo-configurational perspective
- Authors: Mtotywa, Matolwandile Mzuvukile
- Date: 2025-04-02
- Subjects: Industry 4.0 , Industry 5.0 , Operational risk , Organizational learning , Qualitative comparative analysis , Configurational analysis
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/479584 , vital:78326 , DOI 10.21504/10962/479584
- Description: The manufacturing sector is a significant economic multiplier due to its strong connections to the economy's downstream and upstream output sectors. It supports the notion that manufacturing drives industrialisation and can serve as the primary engine for growth and employment creation. Despite its importance, the manufacturing sector has challenges associated with diminishing size and lack of competitiveness, especially in countries such as South Africa. These challenges are exacerbated by prevailing operational uncertainties that negatively impact manufacturing firms. Literature on operational uncertainty, fourth and fifth industrial revolution technologies and organisational learning show several interrelated theoretical and methodological gaps, highlighting three empirical and theoretical gaps as well as two methodological gaps. Six propositions were developed to investigate the research objectives. This was done using a multi-method quantitative design based on the post-positivist paradigm, with data collected from 22 experts (expert survey) and 262 firm representatives (firm survey). The results of the study confirmed that operational uncertainty is a multi-dimensional construct with a reflective model for dimensions and reflective-reflective for higher-order construct. This means that for the dimensions, the indicators can be added or excluded in the formation of the dimension. The same is also true for construct, operational uncertainty. The results of the present study also confirm that operational uncertainty is a norm in the manufacturing industry with a Manufacturing Operational Uncertainty Index (MOUI) = 0.752, indicating the range of futures. This posits that it is difficult to divide these futures into a discrete and exhaustive set of possibilities due to the complexity of conditions (variables) at play. Industry 4.0 and 5.0 technologies and their capabilities can manage the operational uncertainty dimensions with these technologies capable of scenario planning and supply chain integration (SPSI), flexible production and mass customisation (FPMC), real-time system and process monitoring and response (RPMR), root cause analysis and sustainable solutions (RCAS). These technologies are mainly artificial intelligence (AI), Internet of Things (IoT), big data analytics (BDA) and to a less extent advanced robotics (ARB), blockchain and augmented and virtual reality (ARVR). Organisation learning is also an effective causal condition to incorporate in managing operational uncertainty with Industry 4.0 and 5.0 technologies. The study has both theoretical and methodological contributions. In theory, it advanced the modified neo-configuration theory, while the methodology provided an Manufacturing Operational Uncertainty Index and integrated fsQCA with fuzzy decision-making trial and evaluation laboratory (DEMATEL), and structural equation modelling partial least square (PLS-SEM). This research study is important since the recognition and dissemination of subjects within the field of operations management hold great significance for firms, which is contingent upon their sector of operation. This research offers valuable insights for academia, policymakers, and the manufacturing sector. It helps with their activities to effect meaningful change in day-to-day business operations, allowing for more effective progress in the subject area, and promoting practical, real-world issue-solving. , Thesis (PhD) -- Faculty of Commerce, Rhodes Business School, 2025
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- Date Issued: 2025-04-02
Analysis of corporate failures: a case study of two South African banks
- Authors: Mqomboti, Xitshembiso Pronacia
- Date: 2023-02
- Subjects: Business failures South Africa , Corporate governance South Africa , Risk management , Operational risk , Business ethics , Bank management South Africa , Banks and banking South Africa
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/419151 , vital:71620
- Description: This study analysed the factors that contributed to the failure of Venda Building Society Mutual Bank (VBS) and African Bank Limited and the impact it had on their key stakeholders. The specific objectives of this study were to evaluate African Bank and VBS bank's operational risk management processes and controls, the role of ethical failures at VBS bank and African Bank; and assess how the failures affected their stakeholders. The population sample of the study included African Bank and VBS. The study adopted a qualitative research method. Existing reports from both African Bank and VBS were used to collect data. The study adopted a thematic data analysis method, which includes data coding and the development of themes. The data analysis framework was derived from a defined set of research propositions and seven (7) themes were derived from this analysis method. The failure in operational controls of both banks and ineffective risk management structures including unethical conduct by the executive management and board of VBS bank, irregular financial transactions and weakened external auditing function resulted in an unaccountable executive relationship and reckless lending decision-making. This research study will expand on the existing body of knowledge on the failures and near-failures of banks in the South African banking sector. The South African banking industry and its regulatory bodies will be better equipped to strengthen their corporate governance in risk controls to mitigate future collapses and near collapses of banks. , Thesis (MBA) -- Faculty of Commerce, Rhodes Business School, 2023
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- Date Issued: 2023-02