{"id":{"repo_id":"stellenbosch","oai_identifier":"oai:scholar.sun.ac.za:10019.1/136255"},"canonical_url":"https://search.dev.ndltd.org/etd/stellenbosch/oai:scholar.sun.ac.za:10019.1/136255","repository":{"repo_id":"stellenbosch","name":"Stellenbosch University","base_url":"https://scholar.sun.ac.za/server/oai/request"},"display":{"title":"A Decision Support System for Quantifying Sustainability in Small and Medium Enterprises","abstract":"Small and medium enterprises (SMEs) face increasing pressure to measure and improve their sustainability performance, yet most existing assessment tools remain too complex, costly, or rigid for their operational capacity. This study set out to design and implement a decision support system that enables SMEs to quantify, interpret and monitor sustainability performance in a practical and data-driven manner. The aim was to bridge the gap between high-level sustainability frameworks and the day-to-day data SMEs can realistically collect and manage. The research followed a structured engineering design process, beginning with a needs analysis to determine the indicators and functions most relevant to SMEs. From this foundation, system requirements were formulated to guide both the software and the data architecture. The decision support system was implemented in Python and incorporates an integrated database and user interface that automates the handling of sustainability data. The tool converts operational inputs such as energy, water, waste, safety and financial data to standardised performance indicators, normalises them to common scales, and aggregates them into composite sustainability scores for interpretation. A key feature of the system is its ability to visualise historical data and benchmark performance against defined targets or past results. The application supports both monthly and annual data entry, automatically calculates sustainability metrics, and displays the results in tabular and graphical form. This structure allows users to track trends, identify underperforming areas, and evaluate the effects of sustainability initiatives over time. The interface was intentionally designed for accessibility, requiring no advanced technical skills, and includes clear prompts and feedback mechanisms to guide the user throughout data entry and evaluation. Extensive verification and validation were conducted to ensure both computational accuracy and practical reliability. Verification tests confirmed that the system performed all calculations with consistent accuracy across varying data ranges and units. Beyond numerical verification, qualitative validation with SME representatives and subject matter experts confirmed that the decision support system provides tangible value in improving data transparency and supporting management decisions. The findings of this research demonstrate that an integrated, data-driven tool can substantially improve how SMEs measure and interpret sustainability performance. By combining automated data processing, structured performance indicators and intuitive visualisation, the system transforms sustainability assessment from a reporting obligation to a continuous improvement process. The study establishes a foundation for further system development, including cloud-based data storage, adaptive benchmarking, and integration with real-time monitoring technologies. Through this contribution, the research supports the broader goal of embedding sustainability within SME operations in a measurable and actionable way.","abstract_html":"Small and medium enterprises (SMEs) face increasing pressure to measure and improve their sustainability performance, yet most existing assessment tools remain too complex, costly, or rigid for their operational capacity. This study set out to design and implement a decision support system that enables SMEs to quantify, interpret and monitor sustainability performance in a practical and data-driven manner. The aim was to bridge the gap between high-level sustainability frameworks and the day-to-day data SMEs can realistically collect and manage. The research followed a structured engineering design process, beginning with a needs analysis to determine the indicators and functions most relevant to SMEs. From this foundation, system requirements were formulated to guide both the software and the data architecture. The decision support system was implemented in Python and incorporates an integrated database and user interface that automates the handling of sustainability data. The tool converts operational inputs such as energy, water, waste, safety and financial data to standardised performance indicators, normalises them to common scales, and aggregates them into composite sustainability scores for interpretation. A key feature of the system is its ability to visualise historical data and benchmark performance against defined targets or past results. The application supports both monthly and annual data entry, automatically calculates sustainability metrics, and displays the results in tabular and graphical form. This structure allows users to track trends, identify underperforming areas, and evaluate the effects of sustainability initiatives over time. The interface was intentionally designed for accessibility, requiring no advanced technical skills, and includes clear prompts and feedback mechanisms to guide the user throughout data entry and evaluation. Extensive verification and validation were conducted to ensure both computational accuracy and practical reliability. Verification tests confirmed that the system performed all calculations with consistent accuracy across varying data ranges and units. Beyond numerical verification, qualitative validation with SME representatives and subject matter experts confirmed that the decision support system provides tangible value in improving data transparency and supporting management decisions. The findings of this research demonstrate that an integrated, data-driven tool can substantially improve how SMEs measure and interpret sustainability performance. By combining automated data processing, structured performance indicators and intuitive visualisation, the system transforms sustainability assessment from a reporting obligation to a continuous improvement process. The study establishes a foundation for further system development, including cloud-based data storage, adaptive benchmarking, and integration with real-time monitoring technologies. Through this contribution, the research supports the broader goal of embedding sustainability within SME operations in a measurable and actionable way.","abstract_has_math":false,"creators":["Masefield, Jessica"],"institution":"Stellenbosch : Stellenbosch University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["De kock, Imke","Braun, Anja","Jooste, Wyhan"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-03","date_published":"2026-03","updated_at":"2026-07-24T04:40:06Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.sun.ac.za/handle/10019.1/136255","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["De kock, Imke","Braun, Anja","Jooste, Wyhan"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Stellenbosch University. 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A Decision Support System for Quantifying Sustainability in Small and Medium Enterprises. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/41272166-eb4d-469e-817d-62dc27bd9020"]},{"key":"dc:description.abstract","label":"Abstract","values":["Small and medium enterprises (SMEs) face increasing pressure to measure and improve their sustainability performance, yet most existing assessment tools remain too complex, costly, or rigid for their operational capacity. This study set out to design and implement a decision support system that enables SMEs to quantify, interpret and monitor sustainability performance in a practical and data-driven manner. The aim was to bridge the gap between high-level sustainability frameworks and the day-to-day data SMEs can realistically collect and manage. The research followed a structured engineering design process, beginning with a needs analysis to determine the indicators and functions most relevant to SMEs. From this foundation, system requirements were formulated to guide both the software and the data architecture. The decision support system was implemented in Python and incorporates an integrated database and user interface that automates the handling of sustainability data. The tool converts operational inputs such as energy, water, waste, safety and financial data to standardised performance indicators, normalises them to common scales, and aggregates them into composite sustainability scores for interpretation. A key feature of the system is its ability to visualise historical data and benchmark performance against defined targets or past results. The application supports both monthly and annual data entry, automatically calculates sustainability metrics, and displays the results in tabular and graphical form. This structure allows users to track trends, identify underperforming areas, and evaluate the effects of sustainability initiatives over time. The interface was intentionally designed for accessibility, requiring no advanced technical skills, and includes clear prompts and feedback mechanisms to guide the user throughout data entry and evaluation. Extensive verification and validation were conducted to ensure both computational accuracy and practical reliability. Verification tests confirmed that the system performed all calculations with consistent accuracy across varying data ranges and units. Beyond numerical verification, qualitative validation with SME representatives and subject matter experts confirmed that the decision support system provides tangible value in improving data transparency and supporting management decisions. The findings of this research demonstrate that an integrated, data-driven tool can substantially improve how SMEs measure and interpret sustainability performance. By combining automated data processing, structured performance indicators and intuitive visualisation, the system transforms sustainability assessment from a reporting obligation to a continuous improvement process. The study establishes a foundation for further system development, including cloud-based data storage, adaptive benchmarking, and integration with real-time monitoring technologies. Through this contribution, the research supports the broader goal of embedding sustainability within SME operations in a measurable and actionable way."]},{"key":"dc:title","label":"Title","values":["A Decision Support System for Quantifying Sustainability in Small and Medium Enterprises"]}]}],"canonical_facts":{"dc:contributor.advisor":["De kock, Imke","Braun, Anja","Jooste, Wyhan"],"dc:contributor.other":["Stellenbosch University. 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The application supports both monthly and annual data entry, automatically calculates sustainability metrics, and displays the results in tabular and graphical form. This structure allows users to track trends, identify underperforming areas, and evaluate the effects of sustainability initiatives over time. The interface was intentionally designed for accessibility, requiring no advanced technical skills, and includes clear prompts and feedback mechanisms to guide the user throughout data entry and evaluation. Extensive verification and validation were conducted to ensure both computational accuracy and practical reliability. Verification tests confirmed that the system performed all calculations with consistent accuracy across varying data ranges and units. Beyond numerical verification, qualitative validation with SME representatives and subject matter experts confirmed that the decision support system provides tangible value in improving data transparency and supporting management decisions. The findings of this research demonstrate that an integrated, data-driven tool can substantially improve how SMEs measure and interpret sustainability performance. By combining automated data processing, structured performance indicators and intuitive visualisation, the system transforms sustainability assessment from a reporting obligation to a continuous improvement process. The study establishes a foundation for further system development, including cloud-based data storage, adaptive benchmarking, and integration with real-time monitoring technologies. Through this contribution, the research supports the broader goal of embedding sustainability within SME operations in a measurable and actionable way."],"dc:identifier.uri":["https://scholar.sun.ac.za/handle/10019.1/136255"],"dc:language.iso":["en"],"dc:publisher":["Stellenbosch : Stellenbosch University"],"dc:title":["A Decision Support System for Quantifying Sustainability in Small and Medium Enterprises"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T04:40:06Z"}