Browsing by Author "Du Plessis, Carl Jan"

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    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.
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    A framework for implementing Industrie 4.0 in learning factories
    (Stellenbosch : Stellenbosch University, 2017-03) Du Plessis, Carl Jan; Von Leipzig, Konrad; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Globalisation and resource scarcity have drastically increased the intensity of competitive manufacturing, forcing companies to deliver on higher expectations with less staff and minimal resource usage. This situation has ushered forth a new industrial revolution, Industrie 4.0. This new revolution, analogous to coined phrases such as smart factories and the internet of things, has given way to exponential advances in technologies. The implementation of these technologies, should in theory, enable firms to reduce the negative impact of their operations on their triple bottom line and improve efficiencies. However, whilst the implementation of these technologies provides seeming and logical improvements; how to implement them, with what, and where is still largely unclear and can lead to total abolishment due to the lack of knowledge. A decision support framework is proposed in this thesis for aiding companies, specifically small and medium enterprises (SMEs), and learning factories in their implementation efforts towards Industrie 4.0. Industrie 4.0 is broken down into basic levels corresponding to those found in learning factories and SMEs. These levels are put forward as dimensions of the framework, ranging from objects and technologies to Industrie 4.0 applications in SMEs and competencies taught in learning factories, offering the user a visual representation of the practical implementations needed for Industrie 4.0. This framework can be used in three different ways. The first, is a greenfield design, aimed at new learning factories who wish to develop industry related competencies and skills. In this greenfield design, the framework suggests implementation guidelines for the user based on the targeted competency criteria that the user seeks to develop through an Industrie 4.0 perspective. The second, an Industrie 4.0 greenfield design, is for the case where a user seeks to implement Industrie 4.0 concepts with the purpose of enhancing traditional operations in SMEs or showcasing the possibilities of Industrie 4.0 in learning factories. The third application is for redesigning the current operations within a learning factory or SME with Industrie 4.0 in mind. The Industrie 4.0 redesign is aimed at learning factories and SMEs who wish to implement Industrie 4.0 concepts using the infrastructure and equipment they already have. The decision support framework is implemented in two learning factories, representing the two extreme cases of the framework, namely the Industrie 4.0 redesign and the greenfield design. The framework is applied in a technologically-advanced environment in Germany, and successfully allows for the incorporation of three distinct Industrie 4.0 applications in an already well-established learning factory. For further validation, the framework is applied at the Stellenbosch University learning factory to showcase the case where the complete design of a learning factory is commenced with the aim of incorporating Industrie 4.0 into the desired learning competencies.