This research aims to analyze the relationship between sustainable supply chains on financial performance, Sustainable Development Goals (SDGs) financial performance and Sustainable supply chains on Sustainable Development Goals (SDGs). This research uses a quantitative survey method, research data was obtained by distributing online questionnaires through media social to 390 respondents belonging to SMEs, and respondents were determined using the simple random sampling method. Data analysis used Structural Equation Modeling (SEM) Partial Least Square (PLS) with data processing tools using SmartPLS 3.0 software. The questionnaire was designed using a Likert scale of 1 to 7. The independent variable in this research is the sustainable supply chain, the mediating variable is Sustainable Development Goals (SDGs) and the dependent variable is financial performance. The stages of data analysis are validity and reliability testing, significance or hypothesis testing, and mediation influence testing. The results of data analysis show that the Sustainable supply chain has a positive and significant relationship to financial performance, Sustainable Development Goals (SDGs) have a positive and significant relationship to financial performance and the Sustainable supply chain has a positive and significant relationship to Sustainable Development Goals (SDGs). To improve the financial performance of SMEs, they must implement a sustainable supply chain in their supply chain, namely from supplier to customer. To improve the financial performance of SMEs, they must implement Sustainable Development Goals (SDGs) in their management system. Implementing sustainability in the supply chain is important to increase operational efficiency and reduce negative impacts on the environment and society.

In this descriptive analytical study conducted within the Jordanian Customs Department, the influence of Customs Intelligence and Risk Management on Sustainable Supply Chain practices and their subsequent effects on Customs Department Logistics were investigated. Data collected through a structured questionnaire distributed to department employees were analyzed using Smart PLS-4. The findings revealed that Customs Intelligence significantly shapes Sustainable Supply Chain practices, emphasizing the importance of data driven decision-making in achieving sustainability goals. Effective Risk Management strategies were found to positively contribute to Sustainable Supply Chain initiatives, highlighting the symbiotic relationship between risk mitigation and sustainability. Sustainable Supply Chain practices, in turn, were demonstrated to enhance the efficiency of Customs Department Logistics. Furthermore, the study unveiled that Sustainable Supply Chain acts as a mediator, enhancing the impact of both Risk Management and Customs Intelligence on Logistics outcomes. These findings collectively underscore the intricate dynamics of these factors in the context of the Jordanian Customs Department, providing valuable insights for optimizing logistics operations, ensuring compliance, and fostering sustainability. This paper contributes valuable insights and empirical evidence to the fields of customs-related logistics, risk management, sustainable supply chain management, and logistics operations within the Jordanian Customs Department. The paper provides recommendations that have the potential to inform and improve logistics practices, benefiting stakeholders in both the public and private sectors and advancing the understanding of these critical dynamics in the broader logistics and supply chain management discipline.

Implementing sustainable and smart supply chain practices have a great impact on the performance of an organization. In today’s globalized and highly industrialized world, sustainability is recognized as one of the highest priorities of all organizations. Evolution of internet-based technologies, digital platforms and big data analytics have paved the way for redesigning supply chains to be smart, agile, and resilient. Therefore, the implementation of practices related to these two concepts is found to improve the supply chain related organizational performance. This research aims to investigate empirically the impact of these two practices on improving the supply chain organizational performance in the Small and Medium Enterprises (SMEs) of India. This research considered the dimensions and the variables related to sustainable supply chain and smart supply chain practices in SMEs in India which were not considered in research contributions prior to this. Therefore, this research becomes a unique contribution to the existing body of knowledge. Empirical analysis was carried out on data from 92 SMEs from Telangana State in India, collected using a questionnaire. The directory of SMEs of Government of Telanagana, India was used to select the cluster sample of SMEs as respondents, based on a criterion using exploratory research methodology. SPSS software was used to test the model. Regression and ANOVA were used for this purpose. Findings of this research reveal significant influence of sustainable and smart supply chain (SC) practices on improving SC organizational performance. Additionally, individually each of these practices also have a direct influence on the performance of SMEs. Obtaining responses from the representatives of SMEs was a challenge and limitation of this research while expanding the scope of this research to different geographical regions and clusters will be a topic for further research. The outcomes and results of this research provide significant contribution to the existing body of knowledge by filling the gaps and value-adding to the researchers, academicians, students, policy makers and industry practitioners.

The objective of this study was to examine the role of Industry 4.0 in the sustainable supply chain (SSC). The relationship between Industry 4.0, data storage, data assessment, performance evaluation, data inquiry and SSC were examined. To achieve the study objective, the mediation effect of data storage, data assessment, performance evaluation and data inquiry between Industry 4.0 and SSC was examined. Employees of textile companies were selected for data collection in Indonesia. Results of the study highlighted that Industry 4.0 had a significant positive effect on SSC. Better implementation of Industry 4.0 showed a positive effect on SSC. In addition to this, Industry 4.0 had a positive effect on data storage, data assessment, performance evaluation and data inquiry. Finally, data storage, data assessment, performance evaluation and data inquiry had a positive effect on SSC.

Food industry has vital importance globally in which supply chain sustainability plays a key role. Therefore, the objective of this study is to examine the role of passion, planning, goal setting and target achievement in a sustainable supply chain. This study examined the relationship between passion, planning, goal setting, target achievement and sustainable supply chain among the food industry of Indonesia. Questionnaire survey was preferred, and 500 questionnaires were used for data collection. Findings of the study concluded that passion has a positive role in target achievement and an insignificant effect on sustainable supply chain. Planning has a positive effect on target achievement and sustainable supply chain. Moreover, goal setting has a positive role in target achievement and no role in supply chain. Furthermore, target achievement has a significant mediating role. Finally, the relationship between passion, planning, goal setting, target achievement and sustainable supply chain has valuable contributions to the literature.

The objective of this paper is to examine the determinants of the supplier selection process with green consideration. Thus, this analysis gathers a collection of factors from established literature of green supplier selection (GSS), including seven categories and 58 attributes. The objective of this research is to classify the key factors which are presented as qualitative information. Fuzzy logic rules are used to transform qualitative expert knowledge into numerical data. Then, we adopt the Delphi method (DM) to filter and rate unneeded factors according to their relevance. The results indicate 24 important factors for the GSS process. Five categories are included: Performance and technology ability, Environmental management, Pollution control, Quality and Service. The most significant factors are recognized as green research and development, eco-design, green image, green packaging and remanufacturing. Finally, the debate is held on the basis of the findings and future research are also recognized and stated.

Sustainable development in supply chain management is of great importance, as it emphasizes that the procurement process in sustainable supply chains addresses not only current needs but also future requirements. This subject, by improving processes, reducing waste and energy consumption, increasing efficiency, and establishing sustainable relationships with suppliers and customers, contributes to the sustainability and success of companies in international competition. Moreover, hub location is crucial for optimizing distribution processes and reducing costs in the supply chain, especially for perishable products with a defined and limited shelf life, which imposes various constraints on the supply chain. Additionally, to bring mathematical models closer to reality, uncertainty approaches can be employed, and strategies to deal with uncertainty can be considered. This research delves into these issues in the realm of supply chain management, focusing on sustainability and uncertainty, and provides a review of research conducted in the literature of this field.

In this paper, we study a reverse supply chain consists of three layers of the supply chain including suppliers, producers and customers by considering customers’ requirements. In the customer layer, we analyze the customer’s data to identify and fulfill their needs by collecting a list of customers’ views into consideration. In this case, the proposed model analyzes the customers view in the three areas of transport, production and quality and it uses the coding system for getting customers’ opinions. Then, by using the K-means algorithm, which is one of the data analyzing algorithms, the proposed model clusters the data so that similar data enter to the same cluster. The mathematical model is developed for each of the categories and Lingo software package is employed to solve the resulted problem in each category.