[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122322-en":3,"doc-seo-122322-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122322,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Decision Support Tool for Implementing Machine Learning in SME Manufacturing Companies","The manufacturing industry is essential to national economies, with small and medium-sized enterprises (SMEs) providing major value to countries and their people. Machine learning can improve efficiency, traceability, and cost performance, yet SMEs face constraints that hinder full adoption. This thesis equips SMEs with practical means to implement machine learning by defining needs, requirements, benefits, and drawbacks, and by presenting a decision support tool. The tool produces project plans, data preparation guidance, selected algorithms, template code, evaluation, and deployment guidance via GPT-4. Validation through tests, interviews, and a case study confirms accuracy, reliability, usability, and practical code outputs.","A Decision Support Tool for Implementing Machine Learning in SME Manufacturing Companies  \nby  \nChristiaan Lodewyk Wentzel van Coller  \nThesis presented in partial fulfilment of the requirements for the degree of  \nMaster of Engineering (Engineering Management)  \nin the Faculty of Engineering at Stellenbosch University This thesis has also been presented at Reutlingen University, Germany, in terms  \nof a double-degree agreement  \nSupervisor: Prof. L. Louw  \nCo-supervisor: Prof. D. Palm  \nDeclaration  \nBy submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated, that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification.  \nDate: March 2024  \nCopyright © 2024 Stellenbosch University  \nAll rights reserved  \nAbstract  \nThe 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.  \nThis 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.  \nThe 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.  \nFor 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.  \nKeywords: Machine learning, SME, Decision support tool, manufacturing, Artificial Intelligence.  \nOpsomming  \nDie vervaardigingsindustrie is 'n integrale deel van 'n land se ekonomie. Om 'n sterk vervaardigingsindustrie te hê, en veral sterk klein en mediumgrootte ondernemings (KMO's) in vervaardiging, bring aansienlike voordele vir lande en hul mense. Masjienleer kan gebruik word om aansienlike verbeteringe te bring aan vervaardigingsmaatskappye. Masjienleer kan gebruik word om doeltreffendheid ","cbCaibH69SXSZTir","https://ap.wps.com/l/cbCaibH69SXSZTir","pdf",5961025,1,171,"English","en",105,"# Abstract\n## Manufacturing context and machine learning value\n## Goals and tool scope for SMEs\n## Tool outputs and GPT-4 driven generation\n## Validation methods and results\n## Research implications and future improvements","[{\"question\":\"What problem does the thesis address for SME manufacturing companies?\",\"answer\":\"The thesis targets limitations that prevent SMEs from unlocking the full potential of machine learning in manufacturing operations.\"},{\"question\":\"What does the decision support tool generate for users?\",\"answer\":\"It generates a project plan, data preparation guidelines, the most applicable algorithm for the scenario, template code, evaluation guidelines, and deployment guidelines.\"},{\"question\":\"How are the tool’s outputs validated?\",\"answer\":\"Validation is performed using dataset tests, interviews, and a case study to assess accuracy, reliability, relevance, usability, and code practicality.\"}]","A Decision Support Tool for Implementing Machine Learning in SME Manufacturing Companies | 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