Multi-criteria corporate sustainability assessment model and software application

It is evident that the textile industry is one of the most demanding sectors when it comes to the consumption of resources on an international level, exerting a considerable impact on both the environment and society. The industry is characterised by its high levels of water and energy consumption, which results in significant levels of pollution. Additionally, the industry is characterised by the use of complex international supply chains, which serve to facilitate the movement of goods and materials across national borders. A growing awareness of environmental degradation and social responsibility issues has led to heightened expectations for greater transparency and accountability from textile companies regarding their sustainability efforts. Whilst international frameworks, for example the Global Reporting Initiative (GRI), provide guidance for reporting sustainability practices, they frequently fail to address the unique operational characteristics of specific industries. It is particularly challenging for textile companies to adapt these standards to effectively measure and communicate their actual sustainability performance. This indicates a clear necessity for sector-specific evaluation tools that can bridge the divide between global reporting norms and the real conditions of textile manufacturing.This research responds to that need by constructing a sustainability performance evaluation model specifically designed for textile firms. The aim of this research is to establish a thorough yet viable framework that can not only address the operational particularities of the textile sector, but also employs quantitative evaluation methodologies to enhance objectivity. To this end, the study introduces a methodology grounded in Multi-Criteria Decision-Making (MCDM), leveraging sector-adapted Key Performance Indicators (KPIs) and established prioritisation tools to derive measurable sustainability scores. The model has been developed for two principal functions: firstly, to provide a quantitative method by which the sustainability performance of firms can be measured; and secondly, to indicate areas in which improvement can be effected. The research commenced with an extensive review of both international sustainability standards and industry-specific literature, resulting in an initial pool of KPIs categorised across three major dimensions: environmental, social, and economic. The study utilised the GRI Universal Standards and other pertinent sources to identify performance indicators that reflect real operational outcomes while maintaining compatibility with international benchmarks. Following consultation with field professionals and sustainability experts, the list of key performance indicators was revised to include 76 KPIs. 30 of the indicators relate to the environmental factors, 31 to social factors, and 15 to the economic factors. Each indicator was structured for assessment on a three-point scale where 1 equals low, 2 equals moderate and 3 equals high performance, facilitating uniform evaluations and minimizing interpretation errors. The Analytic Hierarchy Process (AHP) was employed to establish the relative significance of each KPI. The present methodology relies on a structured comparison process to capture expert judgments regarding the relative importance of various criteria. The judgments were converted into numerical matrices, from which priority weights were derived using the eigenvalue method. The internal consistency of each comparison matrix was then subjected to testing using the consistency ratio (CR), with the objective of ensuring that only logically coherent matrices contributed to the final weights. In the course of this process, insights from experts were transformed into a robust set of weighting values that subsequently guided performance assessments. Subsequent to the determination of weights, the study conducted a sustainability evaluation employing two Multi-Criteria Decision Making methods: the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA). The application of these methods was undertaken in the context of a case study of a mid-sized textile enterprise in İzmir,Türkiye. In order to proceed with the analysis, three performance scenarios were developed: the first of these was the ideal case, in which all KPIs were scored as high as possible; the second was the real-case scenario, representing the company's actual performance and the third was the worst-case scenario, in which all scores were as low as possible. The three-scenario approach enabled the assessment of the company's actual performance in relation to both the ideal and the worst-case benchmarks. In the TOPSIS method, the decision matrix was normalised and weighted using the criteria weights calculated with AHP. The distances between each alternative and both the ideal and worst-case scenarios were calculated in order to determine their proximity to the ideal solution. It is important to note that higher closeness values are indicative of stronger alignment with sustainability goals, hence offering a clear metric for evaluating overall performance and identifying weaker dimensions. Because GRA works well with sparse or unclear data, it was used as an independent evaluation method. By finding discrepancies and turning them into similarity scores, it calculates how closely the business's actual performance resembles the ideal situation. GRA emphasizes pattern similarity as opposed to TOPSIS, which depends on geometric distance. GRA and TOPSIS were used independently in this study to compare the outcomes of two distinct assessment methods and offer different viewpoints on sustainability performance. It was demonstrated by the findings that the company demonstrated optimal performance in the environmental dimension, with particular strengths in the areas of waste management, emission reduction, and energy efficiency. By contrast, social and economic factors exhibited greater variability, often accompanied by enhanced occupational safety outcomes. Nevertheless, performance in domains such as supplier development and stakeholder engagement continued to be inadequate. In order to create an overall sustainability score, the TOPSIS and GRA results were averaged separately, thus providing a reasonable and useful tool with which to identify performance gaps and direct strategic improvements. The proposed model offers a structured and flexible framework that has been specifically designed for the textile industry by integrating AHP with unique TOPSIS and GRA applications. While the model offers an effective calculation method for the textile sector, its further development and adaptation to different applications, depending on specific needs and sectoral contexts, is also a possibility. As a key outcome of this research, the Corporate Sustainability Score Software developed has transformed the model's theoretical framework into a practical decision support tool. By combining the AHP, TOPSIS, and GRA methods on a single platform, this application has eliminated errors that could arise from manual calculations, accelerated the analysis process, and enabled the repeatable assessment of sustainability performance under different scenarios.