Digital transformation has driven companies to enhance supply chain efficiency and resilience through innovative technologies. However, challenges such as a lack of transparency, low efficiency, and risks of operational disruptions remain significant barriers in Supplier Relationship Management (SRM). This study aims to explore the role of blockchain technology in improving transparency, trust, and supply chain resilience. Using a qualitative case study approach, data were collected through in-depth interviews with supply chain managers from three companies in Indonesia and supporting document analysis. The findings reveal that blockchain implementation enhances operational efficiency by up to 35%, reducing the average process cycle time from 10 days to 6 days. Additionally, transparency levels increased by 40%, reflected in the ability of supply chain participants to monitor information flow in real time. Trust among stakeholders also improved by 25%, attributed to encrypted data security and immutable transaction records. These findings confirm that blockchain strengthens supply chain resilience through three main dimensions: response, recovery, and adaptation. The contribution of this research lies in enriching the literature on the role of blockchain in SRM, particularly in the context of supply chain resilience. Furthermore, the results provide practical guidance for companies in designing blockchain implementation strategies to mitigate operational risks and enhance stakeholder collaboration

Savings and loan cooperatives play an important role in strengthening the community's economy, especially in the micro and small sectors. However, many cooperatives face various challenges related to proper financial management and recording. Limited human resources and lack of understanding of relevant accounting standards are the main obstacles. This community service aims to increase understanding and application of SAK ETAP (Financial Accounting Standards for Entities Without Public Accountability) in the Koperasi Simpan Pinjam (KSP) Inti Dana Semarang.  The methods used include training, mentoring, and evaluation of the implementation of  SAK ETAP. The results of this program show an improvement in the quality of financial reports and transparency in cooperative management. This initiative has a positive impact on member trust and the sustainability of cooperative businesses

The purpose of the study is to determine and test whether partially the size of the company, the age of the company, is significant to the audit tenure and audit delay and also to test whether the audit tenure is able to mediate the effect of reducing the effect of the relationship between company size, company age and audit delay. Secondary quantitative financial statements of 48 Indonesia Stock Exchange companies in the property and real estate sector. Samples were obtained by 48 companies by purposive sampling. Analysis with path analysis software SPSS v24. The company's age results are the only significant variable to audit delay. While audit tenure, partial company size is not significant to audit delay, and audit tenure cannot increase influence as an intervening variable.

In today's competitive business environment, organizations are increasingly adopting Artificial Intelligence (AI) to enhance the efficiency of their Management Information Systems (MIS). The integration of AI into MIS has the potential to improve operational efficiency, decision-making processes, and customer satisfaction. This study aims to investigate the impact of AI on business performance by exploring its role in automating processes and providing data-driven insights. A systematic literature review (SLR) methodology was employed to analyze a range of studies on AI integration into MIS, focusing on improving business efficiency. The findings indicate that AI significantly reduces data processing time, increases decision-making accuracy, and improves customer satisfaction. Specifically, AI implementation led to a 66% reduction in data processing time, a 29% increase in decision-making accuracy, and a 20% reduction in operational costs. These results highlight AI's ability to optimize business processes and enhance overall productivity. However, the study also identified key challenges, including the need for high-quality data, specialized workforce training, and ethical considerations surrounding data privacy. This research contributes to both theoretical and practical knowledge by providing a comprehensive understanding of AI's role in MIS. It offers strategic recommendations for organizations aiming to leverage AI to drive operational efficiency and maintain competitive advantage. Future research should focus on exploring synergies between AI and emerging technologies such as big data and the Internet of Things (IoT) to further improve business outcomes.

This research is motivated by the challenges faced by Micro, Small, and Medium Enterprises (MSMEs) in Indonesia in adopting artificial intelligence (AI) technology to enhance sales and business sustainability. The objective of this study is to examine the impact of AI adoption on the operational efficiency and sales of MSMEs, as well as to identify the challenges encountered in implementing this technology. The research methods used include in-depth interviews with ten MSME owners and a questionnaire survey involving 50 MSME participants. The findings indicate that AI implementation, particularly through the use of chatbots and data analytics for marketing, significantly enhances operational efficiency and sales, with approximately 70% of respondents reporting increased sales. However, the study also identifies key challenges such as high implementation costs, limited technological literacy, and uneven digital infrastructure. The conclusion emphasizes the importance of support from the government and relevant institutions in providing financial incentives and training programs to encourage broader AI adoption among MSMEs.

This study investigates the impact of Artificial Intelligence (AI) implementation in customer service within start-up companies, focusing on its effects on operational efficiency and customer satisfaction. Through a mixed-method approach that combines case studies and surveys, data was collected from customer service managers and customers of various start-ups utilizing AI technologies. The findings reveal that the adoption of AI leads to a 20% reduction in operational costs and significantly enhances customer service productivity by reducing response times and improving information accuracy. Survey results indicate that customers are generally satisfied with AI-driven services, particularly regarding the speed and relevance of recommendations. Furthermore, the study highlights the importance of integrating AI with human interactions to provide a holistic customer experience. This research contributes to the understanding of AI's potential in optimizing customer service processes in resource-constrained environments, offering valuable insights for start-ups looking to leverage technology for competitive advantage.
Keywords: Artificial Intelligence, Customer Service, Start-up Companies, Operational Efficiency, Customer Satisfaction, Technology Integration.