Publication Search

The rapid growth of beverage franchises in Indonesia, particularly MIXUE with over 4,000 outlets, necessitates an in-depth examination of the financial management strategies underlying its success. This study aims to analyze the effects of working capital management, supply chain support, and operational cost efficiency on financial performance, as well as to evaluate the investment feasibility of the MIXUE franchise in Indonesia. A mixed-methods sequential explanatory approach was employed, utilizing multiple regression analysis and capital budgeting methods including Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PP), and Return on Investment (ROI). Data were collected from 50 franchise outlets across Jakarta, Bandung, Surabaya, and Semarang during 2022-2024, supplemented by in-depth interviews with 15 franchisees and 3 regional managers. Results reveal that all three independent variables significantly and positively affect financial performance: working capital management (β = 0.412; p = 0.002), supply chain support (β = 0.358; p = 0.008), and operational cost efficiency (β = 0.486; p < 0.001) with R² = 0.684. Investment feasibility analysis indicates an average positive NPV of IDR 290.1 million, IRR 36.5%, PP 22.2 months, and ROI 56.5%. This study contributes novel insights by integrating financial and supply chain analysis within the context of beverage franchising in emerging Asian markets, providing a comprehensive evaluation framework for prospective investors and franchise system developers.

The purpose of this study is to analyze the effect of liquidity, asset efficiency, and capital structure on profitability in pharmaceutical manufacturing companies listed on the Indonesia Stock Exchange, using Return on Invested Capital (ROIC) as an investment-based profitability indicator. This research employs secondary data from the annual financial statements of pharmaceutical manufacturing companies over a specific period, with multiple linear regression analysis and robust models to ensure model feasibility. The results indicate that liquidity has no effect on profitability. Asset efficiency has a significant negative effect, reflecting the characteristics of the pharmaceutical industry with its high asset intensity. Capital structure has a significant positive effect on profitability, suggesting that measured use of debt can enhance the company’s return on investment. These findings provide theoretical contributions by enriching the literature on investment-based profitability determinants and practical implications for corporate management, investors, and stakeholders in understanding internal factors that influence the financial performance of pharmaceutical companies in Indonesia.

This study aims to develop a strategic plan for Information Systems and Information Technology (IS/IT) at KR Hotel Palembang by applying the Ward and Peppard methodology. The analysis began with external environment assessment using the PEST framework and internal analysis through the MOST method and Value Chain model. The results were synthesized into a SWOT analysis to identify the organization's strengths, weaknesses, opportunities, and threats. From this, Critical Success Factors (CSF) were formulated to guide the determination of key information system needs. Application portfolio mapping was then conducted using the McFarlan Strategic Grid to classify systems based on their strategic impact. Findings indicate that KR Hotel has strong potential in leveraging digital technologies but faces challenges such as the absence of integrated systems across departments and limited IT training for staff. To address these issues, an integrated system named ZKBiolock was proposed, encompassing modules such as hotel management, financial management, customer relationship management (CRM), human resource training systems, digital promotion, and internal network monitoring and control. This strategic plan is supported by a comprehensive database design, network topology, human resource and infrastructure analysis, investment budgeting, and Return on Investment (ROI) evaluation. The proposed strategy aims to enhance the efficiency and effectiveness of the hotel's operational management.

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

This study aims to analyze the financial performance of PT ABC Tbk during the period 2019–2023 using the Du Pont System as the primary analytical tool. The Du Pont System is widely recognized as a comprehensive method to evaluate a company’s overall performance by breaking down profitability into several key components: net profit margin, total asset turnover, return on investment (ROI), equity multiplier, and return on equity (ROE). The research employs a descriptive quantitative approach, with data sourced from secondary materials in the form of official financial statements published by the Indonesia Stock Exchange (IDX). A purposive sampling technique was applied to ensure the relevance and accuracy of the data analyzed. The findings reveal that the company’s financial performance throughout the five-year observation period has been less than optimal. Each of the main components of the Du Pont System showed average ratios that fell below the industry benchmark, indicating structural weaknesses in both profitability and efficiency. Specifically, the net profit margin and total asset turnover were constrained by high operational costs, while ROI and ROE were further pressured by volatility in foreign exchange rates. These inefficiencies highlight the vulnerability of the company’s financial structure to both internal management challenges and external macroeconomic factors. Based on the results, the study provides several strategic recommendations to improve financial performance. First, optimization of cost management is necessary to reduce operational inefficiencies that directly affect profit margins. Second, the implementation of foreign exchange risk mitigation strategies, such as hedging, is suggested to minimize the negative impacts of currency fluctuations. Finally, to strengthen revenue growth, the company is encouraged to adopt and expand digital marketing initiatives as a means of improving sales performance and market penetration. Overall, this study emphasizes the importance of integrating financial control with strategic innovation to ensure long-term sustainability and competitiveness in the pharmaceutical industry.

The low proportion of Grade A harvest quality in Hokkaido corn farming remains a challenge for horticultural agribusiness practitioners. This study aims to develop an operational strategy based on a managerial evaluation of key production factors to improve technical efficiency and harvest quality. The research was conducted at PT Agricole Indonesia Makmur, Cianjur, using data from 24 planting periods in 2024. Six production input variables were analyzed to identify the most influential factors on yield quantity and quality. The results show that only three variables watering frequency, organic fertilizer, and plant age at harvest consistently contributed significantly to the production of Grade A output. Although these variables are inelastic, they play a critical role in maintaining quality. The proposed strategy includes a 10% increase in harvest quantity and a 30% conversion from Grade B to Grade A. Simulation results indicate an additional 7.67 kg of Grade A yield per planting period, generating a value increase of IDR 239,490. The strategy is considered feasible with a positive Return on Investment (ROI) of 14.04% assuming a Grade A selling price of IDR 35,000/kg.

This study aims to analyze the relationship between data analytics utilization and the performance improvement of Meta Platforms Inc., particularly through its Instagram platform. In the digital era, data has become a strategic asset that enables companies to understand user behavior, optimize marketing strategies, and develop innovative products and services. Using a qualitative case study approach, this research illustrates how Meta leverages Instagram analytics—through audience segmentation, campaign effectiveness evaluation, and user insight-driven feature development—to enhance operational efficiency and profitability. The findings show that the use of data analytics not only increases advertising effectiveness and return on investment (ROI) but also strengthens Meta’s position as an innovation leader in the social media industry. The study also highlights the importance of data transparency and ethical practices in data usage, and recommends the development of more open and adaptive data infrastructure to meet evolving user needs.

Artificial Intelligence (AI) is quickly becoming a game changer in the way businesses build and manage their strategies. This article explores how AI is helping organizations make faster and smarter decisions, streamline operations, and spark innovation across various industries. With the ability to process massive amounts of data, AI tools can uncover valuable insights about market trends and customer behavior, allowing companies to respond more accurately and stay ahead of the competition. From machine learning and generative AI to natural language processing and digital twins, these technologies are transforming everything from internal workflows to how businesses connect with customers. The article also offers a practical roadmap for adopting AI in a business setting, covering steps like evaluating readiness, running pilot projects, and measuring success through return on investment (ROI). It emphasizes the need for strong data infrastructure, skilled teams, and a culture that supports innovation and data-driven thinking. Challenges such as algorithmic bias, data privacy, and internal resistance to change are also addressed. Real-world examples from banking, retail, and manufacturing show how AI can deliver real impact improving efficiency, increasing customer satisfaction, and driving business growth. Ultimately, embracing AI isn’t just about keeping up with technology it’s about shaping the future of smart, strategic, and ethical business.

This study employs Return On Investment (ROI) as a key metric to evaluate the financial performance of PT Astra Agro Lestari Tbk. The selection of ROI is justified by its capacity to comprehensively assess the efficiency of the company's total operational assets in profit generation. Utilizing a descriptive research design, the analysis is based on secondary data extracted from the company's financial statements available through the Indonesia Stock Exchange. The research reveals that PT Astra Agro Lestari Tbk achieved an ROI of 78%, indicating superior efficiency in investment management. These empirical findings provide valuable insights for corporate management to formulate strategic decisions aimed at enhancing future operational performance.

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

In today’s date Artificial intelligence (AI) has substantially transformed marketing strategies and specifically Viral Marketing by enhancing the content personalization, targeting the audience and real time campaign optimization. The study explored the Artificial Intelligence impact on Viral marketing with a comprehensive review of 20 literatures that highlights the diverse applications of AI such as predictive analytics, natural language processing (NLP) and AI-driven visual content creation. This study employed meta analysis approach to evaluate how effectively AI could boost marketing reach, engagement and return on investment (ROI). The finding of the study indicates a positive correlation between the efficiency of Viral Marketing campaigns and the integration of AI, despite the fact highlighting ethical and transparency. The study concludes with practical suggestions for using AI in Viral marketing in a responsible and efficient manner to enhance its potential while mitigating related dangers. This study also highlights AI’s revolutionary role in changing market dynamics.

Agroindustry plays a crucial role in the economy, particularly in supporting food security and creating business opportunities in rural areas. However, to remain competitive in an increasingly challenging market, a comprehensive evaluation of financial performance is essential. This study aims to analyze the financial performance of the Tahu Bulat Agroindustry in Buniasih Hamlet, Muktisari Village, Cipaku Subdistrict, Ciamis Regency, using the Du Pont System approach over the 2022–2024 period. The analysis focuses on five key indicators: Assets Turnover, Net Profit Margin, Return on Investment (ROI), Equity Multiplier, and Return on Equity (ROE). The results reveal an average Assets Turnover of 1.42 times, indicating effective asset utilization in generating sales. The average Net Profit Margin of 17.80% reflects the company’s ability to generate net profit from sales. The average ROI of 25.55% indicates efficient asset utilization. The Equity Multiplier has an average of 4.77 times, demonstrating the contribution of the capital structure to asset management. Meanwhile, the average ROE of 125.54% highlights high returns on equity, despite significant declines in the final year. The study concludes that the financial performance of the agroindustry is generally good, but declining indicators in the last year require strategies to improve efficiency and asset management. It is recommended that the company reevaluate its capital structure and enhance operational efficiency to ensure business sustainability.

This research aims to analyze the influence of cash management and inventory management on the financial performance of Ling Food Stores in Makassar City during the 2020-2022 period. The main focus of this research is to evaluate how cash and inventory management contributes to a company's liquidity, profitability and operational efficiency, which are measured through various financial ratios such as Current Ratio, Gross Profit Margin (GPM), Operating Profit Margin (OPM), Net Profit Margin (NPM), Return on Investment (ROI), and Return on Equity (ROE). The research results show that the Current Ratio in 2020 cannot be calculated due to the absence of short-term liabilities, while in 2021 and 2022 each was recorded at 0.095% and 0.216%, reflecting low liquidity. On the other hand, the decrease in Average Inventory from IDR 754,200 in 2021 to IDR 715,000 in 2022, followed by a significant increase in net profit, shows efficiency in inventory management. The increase in GPM, OPM, NPM, ROI, and ROE ratios from 2020 to 2022 reflects significant improvements in financial performance, indicating that more effective cash and inventory management can improve a company's financial health.

The growth of the human population and increased purchasing power drive greater demand for broiler meat, posing challenges for farmers to enhance their productivity and business efficiency. The development of closed-house cages offers a solution to improve production efficiency. This study compares the productivity and financial analysis between the cage and postal models. Utilizing a quantitative descriptive method, the research was conducted over a single rearing period in cages and postal setups at PT Dinamika Megatama Citra (DMC) without any specific treatment. Research parameters encompassed body weight gain, mortality, FCR, harvest weight, and Performance Index. Financial analysis was conducted using indicators such as the R/C ratio, B/C ratio, Break-Even Point (BEP), and Return on Investment (ROI). Results indicate that in cage conditions, mortality stood at 4.07%, FCR at 1.718, average weight at 2.2 kg, and Performance Index at 287.5. Meanwhile, postal cages exhibited higher mortality at 8.79%, FCR at 1.732, average weight at 2.3 kg, and Performance Index at 285.6. In financial analysis, cage systems had a BEP price of Rp 19,726/kg with a unit BEP of 40,616 kg, an R/C ratio of 1.039, a B/C ratio of 0.039, and an ROI of 3.92%. Conversely, postal cages had a BEP price of Rp 19,507/kg with a unit BEP of 39,924 kg, an R/C ratio of 1.05, a B/C ratio of 0.05, and an ROI of 5.09%.

Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.