Browsing by Author "van Coller, Christiaan"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    A decision support tool for implementing machine learning in SME manufacturing companies.
    (Stellenbosch : Stellenbosch University, 2024-03) van Coller, Christiaan; Louw, Louis; Palm, Daniel; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: The manufacturing industry is an integral part of a country’s economy. Having a strong manufacturing industry, and especially strong small and medium-sized enterprises (SMEs) in manufacturing, brings a significant benefit to countries and their people. A tool that can be used for improving manufacturing companies is machine learning. Machine learning can be used to significantly improve efficiency, traceability, reduce costs, as well as many other benefits. There are, however, many limitations, especially for SMEs, that can withhold them from unlocking the full potential of machine learning. This thesis aims to provide SMEs with the necessary tools to implement machine learning into their manufacturing operations. This includes the necessary requirements, benefits, and drawbacks to implementing machine learning. This aim is achieved through a literature review identifying the needs, requirements, and benefits of implementing machine learning in manufacturing, as well as the use of a decision support tool for implementing machine learning into the manufacturing operations of SMEs. This thesis focuses specifically on the manufacturing industry; therefore, all context falls within the manufacturing industry. Although the tool might have limited applications outside of manufacturing, it is designed for the manufacturing industry. The tool generates a project plan, guidelines for data preparation, the most applicable algorithm for the scenario as well as template code, evaluation guidelines, and deployment guidelines. These are generated using information provided by the user of the tool that is processed by OpenAI’s GPT-4 large language model. These outputs are validated using dataset tests, interviews, and a case study. The validation process showed that the tool is accurate and reliable, relevant in its recommendations, user friendly in terms of its user interface, and that it provides accurate and practical code for the appropriate algorithms. The thesis as a whole highlights the unique challenges and limitations faced by SME manufacturing companies, after which it addresses the challenges associated with machine learning. For further research, improvements can be made to the decision support tool to make it a direct channel for machine learning implementation. Having a functionality that allows users to upload their data will significantly improve the guidance of the tool as well as the algorithm that will be suggested.