Doctoral Degrees (Industrial Engineering)

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A decision support framework for the selection of appropriate time series forecasting methods in the retail sector
(Stellenbosch : Stellenbosch University, 2023-10) Ganzevoort, Reinard Christiaan; Van Vuuren, Jan Harm ; Lindner, Berni G; Du Toit, Jacques; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT:There is a significant trade-off in any high-turnover retail environment between limiting in-store inventory levels and mitigating the risk of stock-outs. This trade-off is caused by the typical aim of retail organisations to minimise the capital tied up in inventory without incurring a significant deterioration of their service levels (i.e. to ensure product availability for customers). In order, therefore, to better manage their inventories, retailers often consider the prediction of customer behaviour as a main priority. In practice, however, sales forecasting processes are usually automated to some extent and practitioners often have limited knowledge pertaining to the selection of appropriate forecasting methods. A generic framework is proposed in this dissertation for assisting retail forecasting practitioners in the selection of appropriate forecasting methods based on available time series data sets pertaining to retail sales. This forecasting framework takes as input a multivariate time series sales data set and facilitates the configuration, transformation and extraction of valuable information from these data in order to partition the data set into clusters of time series exhibiting similar attributes. The working of the framework is based on a generic, two-phased approach. One phase of the framework, called its benchmarking phase, involves establishing a benchmark data set (or updating it if it already exists) which can be leveraged to inform feature-based forecast model identification and ranking for different clusters of time series. The computationally efficient identification of a tailored shortlist of forecast models is thus facilitated during the other framework phase, called its implementation phase, for each sales time series presented to it by a retail organisation, based on the features of the time series presented. The two phases of the framework may be applied repeatedly in alternating fashion, thus enlarging the benchmark data set and improving its representativeness each time after having applied the implementation phase to the sales time series data of a new retail organisation. The framework is verified with reference to well-established retail sales benchmark data. The verified framework is employed to evaluate the difference in forecast quality and computational time, based on the benchmark data, that results from applying the forecasting methods recommended by the framework to newly presented retail timeseries data as opposed to exhaustively applying forecasting methods classified as traditional statistical techniques, machine learning techniques and ensemble techniques. The working of the framework is finally validated by applying computerised instantiations thereof to real-world data sets of time series representing retail sales.
A new simulation-based methodology for pro-active planning in deep-level mine ventilation systems to identify and mitigate hazards
(Stellenbosch : Stellenbosch University, 2024-03) Jacobs, Daniël Rudolf; Schutte, Cornelius Stephanus Lodewyk; Van Laar, Jean Herman; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Deep-level mining is present in various countries around the world. Gold production continues to decrease, and this places strain on the gold mining industry in South Africa. The depleting gold reserves meant that the existing deep-level gold mines had to expand deeper into the earth’s crust. Consequently, effective ventilation of deep-level mines is challenging. Deep-level mines rely on complex and dynamic ventilation systems to supply adequate air to underground workers. Changes to these systems are implemented to enable the expansion and deepening of the mines. These changes could cause certain hazards underground. The three main hazards that occur are high temperatures, gas accumulation and dust pick-up. It is therefore crucial to ensure that these hazards are prevented through effective planning. Digital twinning is a cutting-edge technology that simplifies the simulation and planning of the entire deep-level mine ventilation system. Currently, a problem persists in the absence of a concise strategy for identifying and mitigating hazards in life-of-mine planning, specifically when utilising a calibrated digital twin. Therefore, a systematic literature review was conducted to confirm this unique research opportunity. Additionally, a need is identified to determine the frequency of planning. There are currently two planning methods, namely incremental and end-state planning. The case study research methodology was utilised to develop a new strategy that uses a calibrated digital twin to identify and mitigate hazards in the planning of the life-of-mine. The strategy will then be verified in two parts, firstly verifying the strategy itself and secondly by utilising it in the two mentioned planning methods. The first verification case study implemented the hazard identification and mitigation strategy on a deep-level gold mine. The study produced a calibrated model with an accuracy of 95%. The calibrated model was then expanded according to the strategy and was used to identify various problem areas where high temperatures and insufficient airflow were present. These hazards were then mitigated, and sufficient ventilation was supplied throughout the three-year life-of-mine plan. The second verification case study implemented the hazard identification and mitigation strategy in both the incremental and end-state planning method. This enabled the comparison of these planning methods to evaluate the impact of the lower frequency of planning. This study highlights the significance of effective planning to minimise delays to ensure continuously safe working environments during the entire life-of-mine, rather than just at specific stages in the life-of-mine plan. Therefore, the developed solution in this research study can be used as a new simulation-based methodology for pro-active planning in deep-level mine ventilation systems to identify and mitigate hazards. The original contributions of the study include: • The development of a new strategy used in deep-level mine ventilation system life-of-mine planning. • The utilisation of a calibrated digital twin to identify possible problem areas in life-of-mine planning. • The reproducibility of the implementation of the strategy on all deep-level mines. • The improvement in the management and planning of a deep-level mine ventilation system. • The identification of which planning method is applicable for various applications.
A distributed simulation-optimisation system in support of goal pursuit in large-scale urban growth scenarios
(Stellenbosch : Stellenbosch University, 2024-03) Van Heerden, Quintin; Van Vuuren, JH; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Cities are complex systems and become increasingly complex as they grow. Urbanisation has to be managed carefully so as to avoid exerting unnecessary pressure on infrastructure and not to exacerbate further any unsustainable practices, such as overcrowding or urban sprawl. Urbanisation plays an important role in achieving the sustainable development goals of the United Nations, but local planning practices have to be aligned with these goals in order to achieve them. Local planning is plagued by requirements in national directives and legislation that mandate several planning instruments, frameworks, and policies, but do not provide clear direction on how to achieve these often-grandiose goals. Moreover, municipalities are not required to test the feasibility or the potential effects of their plans before implementing them. This may be due, in part, to a lack of scientific tools capable of assisting planners in this regard. State-of-the art land-use models, which are available for this purpose, are often too complex, require large volumes of data and specialist expertise to execute them, are limited in their application, or are too involved in terms of the underlying process of setting up appropriate test scenarios. A novel, generic system for long-term land-use planning is proposed in this dissertation which combines the powerful modelling paradigm of integrated land-use transport models with optimisation algorithms in a simulation-optimisation setting. The system comprises four functional components which together facilitate the pursuit of goals in large-scale urban growth scenarios. These components are a data component, a simulation component, an optimisation component, and an interpretation component. The main objective of the data component is to guide a decision-maker systematically through the processes of data collection, curation, preparation, and storage. The simulation component facilitates the establishment and execution of an integrated land-use transport model, while urban development aspiration levels may be specified in the optimisation component, which is aimed at performing multi-objective optimisation in pursuit of these targets. The working of the optimisation component is based on the execution of a self-adaptive metaheuristic responsible for managing various perturbation operators and interacts with the simulation component. Finally, the interpretation component provides a structured approach towards the interpretation of the performance of the entire system, the performance of the metaheuristic, as well as the output results with a view to make informed decisions with respect to land-use planning. integrated land-use transport models are notorious for their long execution times and require vast amounts of data and resources. The system proposed in this dissertation, therefore, conforms to one of two possible distributed system designs in order to guide the decision-maker during the process of establishing and running such a model in a distributed manner — either making use of microservices or else implementing the system on a high-performance computing cluster. An analysis of the costs involved is also carried out so as to assist the decision-maker in selecting the most appropriate system design for his or her needs.
The development of a platform using digitalisation and networked modules for forecasting, planning and management to facilitate long-term success of SMEs in South Africa
(Stellenbosch : Stellenbosch University, 2023-12) Du Plessis, Carl Jan; Prof. Schutte, Corné; Prof. Dr. Eng Hummel, Vera; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
ENGLISH ABSTRACT: The high attrition rate of Small and Medium Enterprises (SMEs) in South Africa, with a staggering 70% ceasing operations within their initial two years, is a pressing concern extensively highlighted in academic literature. This dissertation presents an innovative approach to support the growth and success of these SMEs by holistically addressing the multifaceted challenges they face, such as limited education, restricted _nancial access, and inadequate management skills. Current interventions often fall short due to their sector-speci_c focus, lack of adaptability to diverse user contexts, and scalability challenges, further exacerbated by low adoption rates among SMEs. To bridge the identi_ed gap in existing solutions, the study poses a central research question: How can a con_gurable, adaptable, and accessible platform be developed to holistically address the challenges faced by South African SMEs, thereby bolstering their prospects for long-term growth and success? The proposed platform is then developed into a prototype, which is validated in real-world use cases across the services, online retail, and subsistence agriculture sectors. The _ndings from these implementations underscore the platform's potential in facilitating long-term success. This research lays the foundation for further advancements aimed at strengthening the SME sector in South Africa, with the overarching ambition of fostering a vibrant and resilient national economy. This research introduces _ve unique contributions. Foremost is the development of a comprehensive set of networked modules, tailored specifically for South African SMEs. These modules holistically address the multifaceted challenges that SMEs encounter. Bolstering this is a novel platform design, informed by a synthesis of insights from earlier research objectives. This design serves as a roadmap for devising solutions essential to the long-term success of SMEs. The third contribution, inherent in the platform design, is the integration of strategic business management systems, machine learning, and digitalisation. This multi-pronged approach, drawing on the core tenets of industrial engineering, has culminated in a platform tailored to augment SME success in South Africa. Furthermore, the establishment of a database backend for operational planning and operations management dispels the conventional complexities SME stakeholders face, facilitating seamless business performance management. Lastly, building on these foundational elements, an automated mechanism for deriving use case-speci_c KPIs has been introduced. This mechanism leverages the intricate relationships among measures, sensors, and objectives, with machine learning serving as the catalyst for producing KPIs precisely attuned to speciFIc business objectives.
Qualification and certification of laser powder bed fusion for aerospace applications: a model-based production systems engineering approach.
(Stellenbosch : Stellenbosch University, 2023-12) Gibbons, Duncan William; Van der Merwe, André Francois ; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
ENGLISH ABSTRACT: Qualification approaches to aid the certification of additive manufacturing are being widely researched by academia and the aerospace industry due to the potential benefits this technology offers once industrialised. Such benefits include the ability to produce lightweight structures, reduced material waste, the ability to produce unique and complex structures, and production is economical to produce small batches when compared with some traditional manufacturing processes that are reliant on extensive tooling. However, there are challenges hindering the wider adoption of metal additive manufacturing processes in the industry. Such challenges include production controls, data management, process characterisation, material and product traceability, and a general lack of additive manufacturing qualification and certification guidance material, particularly for sub-tiered production and manufacturing organisations. This research aims at developing a production system model that defines the production system lifecycle in terms of qualification and certification, and the standard production operations for laser powder bed fusion production. This model aims at capturing the current additive manufacturing and aerospace production best practices to reduce the steep learning curve that organisations experience when implementing and industrialising new production processes such as laser powder bed fusion. A mixed-method research approach utilising both qualitative and quantitative methods was erformed. A systems engineering methodology was applied which utilised elements of design science research and model-based tools and techniques. Interviews, surveys, observations and benchmarking, and case study research methods were used during the design of conceptual production system models and during model evaluation phases. The production system model was implemented at local industrial and academic facilities. Four test cases were carried out to gather test data and evaluate the production system operation to assess the quality of the developed model. Mechanical and material testing was performed to evaluate the material and articles produced by the developed production system. The developed production system model consists of context and conceptual, operational, logical, physical, and instantiated architectural views. The model addresses production activities from an aerospace part manufacturer and producer perspective, design activities are excluded from the scope of this research. An operational architecture was modelled that defines the production system lifecycle from installation through qualification phases to ongoing production. A production system architecture was modelled that defines the standard laser powder bed fusion production operations. The production system produced material that conforms with industry specification requirements and is comparable to its wrought counterparts. An initial production run of structural components was performed to demonstrate the production system for the full product lifecycle. The use of a model-based system engineering approach for production system design improves information traceability, structuring production facilities, mapping information and material flows, controlling processes and parameters, and implementing production processes. Such aspects are important for achieving qualification and certification in the aerospace industry. Using the model, production and process controls are defined and part quality can be controlled. The developed production system model acts as a single source of truth and a mechanism for communicating production information with stakeholders. The developed architecture and model provide value as a reference for the industry for laser powder bed fusion production. The model can be used as a benchmark for future additive manufacturing and production system development undertakings and for the design and structuring of additive manufacturing quality management systems.

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