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Abstract. PT XYZ, a wooden furniture manufacturing company, served as the research site for this study which applied the Theory of Constraints (TOC) method to analyze production performance and identify bottlenecks. The company faces capacity imbalances between workstations, resulting in production targets that have not been achieved optimally. Data collection involved direct observation and interviews with related parties in the production area. The analysis was conducted by comparing the required capacity with the available capacity at each production workstation. The findings reveal that solid processing, machining, sanding, assembling, painting, and packing have sufficient available capacities to meet production requirements, thus categorized as non-bottleneck processes. In contrast, the panel processing station is identified as the main bottleneck due to its highest workload among all processes. By implementing the Theory of Constraints, the company can identify major constraints and establish improvement priorities to enhance production flow smoothness. It is expected that improvements in bottleneck processes will increase production efficiency, balance capacity among workstations, and support more optimal achievement of production targets. Keywords: bottleneck; capacity; manufacturing; production performance; theory of constraints   Abstrak. PT XYZ sebuah perusahaan manufaktur furnitur kayu, menjadi lokasi penelitian ini yang menggunakan metode Theory of Constraints (TOC) untuk menganalisis kinerja produksi dan mengidentifikasi bottleneck. Perusahaan menghadapi ketidakseimbangan kapasitas antar stasiun kerja yang menyebabkan target produksi belum terdengar secara optimal. Pengumpulan data meliputi observasi langsung dan wawancara dengan pihak terkait di area produksi. Analisis dilaksanakan dengan membandingkan kapasitas yang dibutuhkan terhadap kapasitas yang tersedia pada setiap stasiun kerja produksi. Hasil penelitian menunjukkan bahwa proses pembahanan solid, machining, sanding, assembling, painting, dan packing memiliki kapasitas yang tersedia yang masih mampu memenuhi kebutuhan produksi, sehingga termasuk kategori non-bottleneck. Sebaliknya, stasiun kerja pembahanan panel diidentifikasi sebagai bottleneck utama karena memiliki tingkat beban kerja tertinggi di antara seluruh proses. Dengan penerapan Theory of Constraints, perusahaan dapat mengidentifikasi kendala utama dan menentukan prioritas perbaikan untuk meningkatkan kelancaran aliran produksi. Diharapkan perbaikan pada proses bottleneck dapat meningkatkan efisiensi produksi, menyeimbangkan kapasitas antar stasiun kerja, serta mendukung pencapaian target output perusahaan secara lebih optimal. Kata kunci: bottleneck; kapasitas; kinerja produksi; manufaktur; theory of constraints

The governance of sand and gravel mining requires an integrated assessment of technical planning, occupational safety, environmental control, reclamation, and regulatory compliance. This study evaluated the governance of CV. Ria Bersaudara, a sand and gravel mining company located in Pasar Surulangun, Rawas Ulu District, Musi Rawas Utara Regency, using the Good Mining Practice framework. The study used a descriptive evaluative approach based on field observation, document review, indicative resource estimation, equipment productivity analysis, occupational safety and environmental risk assessment, and compliance mapping. The results show that the company has a basic legal foundation through an exploration mining permit covering 12.5 ha; however, several governance components require improvement. The indicative prospective area was approximately 2.50 ha, with an effective follow-up area of 1.80 ha and an estimated indicative resource of 21,600 m³ or 35,640 tons. Productivity analysis indicated that excavator capacity reached about 61 m³/hour, while one dump truck only transported about 10.3 m³/hour, creating a haulage bottleneck if the truck fleet is insufficient. Safety implementation was also not optimal, with personal protective equipment compliance estimated at only 55%. The study recommends validating permit documents, strengthening technical exploration data, improving drainage and sediment control, enforcing safety procedures, implementing progressive reclamation, and establishing daily operational records.

Keterbatasan fasilitas loading bay pada perusahaan industri kawat baja berpotensi menyebabkan antrian kendaraan, peningkatan waiting time, dan bottleneck pada aktivitas bongkar muat kontainer impor dan ekspor. Penelitian ini bertujuan untuk menganalisis efektivitas optimalisasi loading bay terhadap peningkatan kinerja bongkar muat kontainer. Penelitian menggunakan metode deskriptif komparatif dengan pendekatan kuantitatif dan studi kasus. Analisis dilakukan menggunakan teori antrian (queueing theory) dengan model antrian M/M/1 berdasarkan data operasional periode Oktober 2025 dan April 2026 yang diperoleh melalui observasi, dokumentasi, dan studi literatur. Hasil penelitian menunjukkan bahwa rata-rata waktu kontainer dalam sistem mengalami penurunan dari 818,07 menit / kontainer pada Oktober 2025 menjadi 401,32 menit / kontainer pada April 2026 atau turun sebesar 50,95%. Selain itu, waiting time pada periode Oktober 2025 lebih tinggi dibandingkan April 2026 akibat ketidakseimbangan antara tingkat kedatangan dan kapasitas pelayanan. Kebaruan penelitian ini terletak pada penerapan model antrian M/M/1 sebagai alat analisis untuk mengevaluasi dampak optimasi loading bay terhadap efisiensi bongkar muat pada industri kawat baja. Berbeda dengan penelitian terdahulu yang umumnya berfokus pada pengukuran kinerja antrian, penelitian ini mengintegrasikan analisis parameter antrian sebelum dan sesudah optimasi sehingga mampu menunjukkan pengaruh langsung perbaikan operasional terhadap kinerja sistem.Penelitian ini menyimpulkan bahwa optimasi penggunaan loading bay dan pengelolaan sistem antrian mampu meningkatkan efisiensi operasional bongkar muat secara signifikan. Kontribusi ilmiah penelitian ini adalah memberikan bukti empiris mengenai efektivitas model M/M/1 dalam mendukung evaluasi dan pengambilan keputusan operasional pada fasilitas loading bay, serta menyediakan referensi bagi industri manufaktur yang memiliki keterbatasan kapasitas pelayanan dalam upaya mengurangi bottleneck dan meningkatkan produktivitas logistik.

Penelitian ini dilakukan di UD. XYZ yang bergerak di bidang produksi sandal jepit dengan permasalahan utama berupa ketidakseimbangan lintasan produksi yang menyebabkan terjadinya bottleneck, tingginya waktu menganggur, serta belum tercapainya target produksi perusahaan. Penelitian ini bertujuan untuk menganalisis dan memperbaiki keseimbangan lintasan produksi menggunakan metode Ranked Positional Weight (RPW) agar efisiensi produksi dapat meningkat. Metode penelitian dilakukan melalui pengamatan langsung terhadap proses produksi, pengukuran waktu kerja, pengujian keseragaman dan kecukupan data, perhitungan waktu siklus, waktu normal, dan waktu baku, kemudian dilanjutkan dengan analisis keseimbangan lintasan menggunakan metode RPW. Hasil penelitian menunjukkan bahwa penerapan metode RPW mampu memperbaiki performansi lintasan produksi dengan mengurangi jumlah stasiun kerja dari 7 menjadi 4 stasiun kerja. Selain itu, nilai line efficiency meningkat dari 46,10% menjadi 80,68%, balance delay menurun dari 53,90% menjadi 19,32%, dan smoothness index menurun dari 15,86 menjadi 4,80. Hasil tersebut menunjukkan bahwa metode RPW mampu menghasilkan pembagian beban kerja yang lebih merata, mengurangi waktu menganggur, serta meningkatkan efisiensi dan kelancaran proses produksi perusahaan

This study aims to analyze the workload of sewing operators within a single production line and to equalize the workload using the line balancing method at PT. Pan Brothers. The garment industry requires high production efficiency; therefore, balanced workload distribution among operators is necessary to ensure optimal production processes. This research used an observational approach with data collection through direct observation of the sewing process, literature study, and documentation of production data. The research population consisted of 36 operators on the observed production line, analyzed through time study activities. The analyzed data included cycle time, takt time, and work processes at each workstation. The results showed that several processes had cycle times exceeding the takt time, specifically in processes number 2, 5, 6, 12, 17, 23, and 26, which caused bottlenecks and workload imbalance among operators. The calculation results indicated that the actual process time reached 520 seconds per product, while the ideal time based on takt time was 287 seconds per product, resulting in a time difference of 233 seconds or about 44% longer than the standard time. The implementation of the line balancing method was carried out by redistributing work elements from processes with high workloads to processes with remaining work capacity. The improvement results showed that the workload among operators became more balanced, the production process became more efficient, and the potential for bottlenecks could be minimized, enabling the company to achieve its production targets.

Prescription waiting time is a key indicator of pharmacy service quality that directly influences patient satisfaction and hospital efficiency. Data from the Outpatient Pharmacy Installation of Pelabuhan Jakarta Hospital show average waiting times of 30.49 minutes for non-compounded medications and 35.93 minutes for compounded medications. Although these figures are still within national standards, they have not met the hospital’s internal targets, indicating inefficiencies that require systematic improvement.This study aimed to analyze prescription waiting times and identify waste in outpatient pharmacy services using Lean Hospital and Continuous Improvement approaches. A qualitative analytical method with a case study design was applied. Data collection involved participatory observation, in-depth interviews with key informants (head of pharmacy, pharmacists, and technicians), and document review. Analysis utilized Lean tools such as Value Stream Mapping (VSM), identification of Value Added (VA) and Non-Value Added (NVA) activities, bottleneck analysis, fishbone diagram, and Failure Mode and Effects Analysis (FMEA).The findings revealed that service processes were dominated by non-value-added activities, especially during prescription receipt, verification, packaging, and dispensing. Major wastes included waiting, motion, and overprocessing. Contributing factors were uneven staff distribution, suboptimal e-prescribing systems, incomplete prescriptions, and lack of standardized procedures. The proposed future state VSM demonstrated potential improvements in reducing waiting time and enhancing service efficiency.

PT. XYZ faces administrative challenges in implementing the Contractor Safety Management System (CSMS), which has been managed manually from risk assessment and pre-qualification to contractor selection resulting in validation delays, document duplication, and limited monitoring. This study aims to: (1) identify the constraints of the manual CSMS at PT. XYZ; (2) design an electronic-CSMS (e-CSMS) application tailored to internal user needs; (3) analyze the contribution of e-CSMS to document management effectiveness; and (4) evaluate the design's alignment with the Occupational Health and Safety Management System (SMK3) principles as per Government Regulation No. 50 of 2012. The method employed is Research and Development (preliminary stage) using a descriptive qualitative approach through interviews, observations, and document studies. The design results include: (i) a unified digital form integrating procurement data input, risk assessment entries (severity and probability), and document upload and assessment for pre-qualification/selection in a single workflow; (ii) a monitoring dashboard with filtering and export functions; and (iii) internal access settings within PT. XYZ's HSSE portal. Functionally, this design addresses the main bottlenecks of the manual process by creating a more concise, structured, and documented verification workflow, thereby making document management more efficient and traceable. In terms of compliance, e-CSMS supports the implementation of SMK3 elements in the administrative domain of policy establishment, planning, and execution of OHS plans through consistent recording and verification mechanisms. Development recommendations include expansion to field implementation phases, automation of risk-level calculations, and an option for preliminary data entry by contractors with final verification remaining with the internal team.

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing environments.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

The limitation of physical memory (RAM) is a primary constraint hindering optimal performance in modern operating systems, especially when running large applications or performing intensive multitasking, often resulting in crashes and high latency. This research aims to quantitatively analyze the effectiveness of Virtual Memory (VM) implementation as a solution to this RAM constraint on the Windows 10 operating system, focusing on VM’s impact on CPU performance, GPU performance, and multitasking response. The methodology employed is a controlled experiment using industry-standard benchmarks: Cinebench R20 (CPU), Unigine Heaven (GPU), and response time measurements in intensive multitasking scenarios. Experimental results demonstrate that VM activation improves CPU/GPU performance by up to 5% and accelerates multitasking response time by up to 15%, confirming VM's effectiveness in mitigating memory bottlenecks. Nevertheless, this study also identifies potential performance overhead stemming from excessive paging and swapping processes, which trigger the phenomenon of Thrashing. Therefore, the research recommends a dual optimization strategy to achieve maximum and stable performance: software optimization via the Least Recently Used (LRU) algorithm to suppress page faults, supported by hardware optimization including the use of an SSD for the swap file and increased RAM capacity.

The integration of blockchain technology with Multi-Agent Reinforcement Learning (MARL) presents a promising solution for optimizing resource allocation and ensuring security in decentralized network environments, particularly in 5G and 6G network slicing. This research proposes a model that combines the security features of blockchain with the adaptive, decentralized decision-making capabilities of MARL. Blockchain ensures the integrity and transparency of resource allocation by providing a secure, tamper-proof ledger for transaction validation, while MARL allows agents to dynamically allocate resources based on real-time network conditions. The simulation results demonstrate significant improvements in resource allocation efficiency, fairness among users, and resilience to cyberattacks. By combining these two technologies, the proposed model overcomes many of the challenges posed by traditional centralized systems and offers an enhanced, secure, and fair solution for resource distribution in future mobile networks. However, scalability remains a challenge, especially in large-scale networks where transaction processing and consensus overhead can create bottlenecks. Additionally, training complexity in MARL models presents computational challenges, particularly in highly dynamic network environments. The model's performance trade-offs, including the balance between high security and system overhead, are also discussed. Future research should focus on optimizing blockchain consensus mechanisms to improve scalability and enhancing MARL model training techniques to reduce computational costs and improve real-time decision-making. This integration holds significant potential for revolutionizing resource allocation in 5G and 6G networks, enabling more efficient, secure, and fair management of network resources in the increasingly complex and decentralized digital ecosystem

This study examines the influence of ship maintenance and spare-part availability on the operational continuity of the AHTS Transko Andalas, operated by Pertamina Marine Solutions. Using a qualitative-descriptive approach with supportive quantitative indicators, data were collected through semi-structured interviews, questionnaires, onboard observations, and document analysis. The Spare-Parts Readiness Index (SPRI) and Planned Maintenance Compliance (PMC) were applied to measure vessel preparedness. Results revealed that both maintenance and spare-part availability positively and significantly impact operational continuity, though challenges remain in procurement delays, critical maintenance compliance, and workforce competence in predictive diagnostics. Thematic analysis highlighted procurement bottlenecks, maintenance execution gaps, and human resource limitations as recurring barriers. Findings contribute to maritime operations management, shipping sustainability, and vocational education by demonstrating the interdependence of technical and human factors in sustaining offshore vessel reliability. The study emphasizes the urgency of integrated procurement reforms, condition-based maintenance, and workforce training to achieve sustainable maritime operations.

In manufacturing industries, machining processes play a critical role in ensuring product quality, precision, and production efficiency. However, in the production of swing arm parts, the machining process has been identified as a bottleneck due to its non-optimal cycle time. One of the main issues contributing to this inefficiency is the disorganized handling of circlip inner parts. These components are often scattered without a designated placement system, which creates significant difficulties for operators when retrieving and installing circlips onto the swing arm. Such abnormalities disrupt workflow continuity, extend production time, and reduce overall productivity. To address this challenge, a circlip feeder machine was designed as a supporting device to assist operators and streamline the machining process. The design emphasizes efficiency, integration, and systematic operation by utilizing readily available workshop materials. The developed feeder machine is equipped with a robust frame construction and has a storage dimension capable of accommodating up to 200 circlips. In addition, mechanical analysis demonstrates that the feeder structure can withstand a maximum applied force of 31,475 N, ensuring durability and reliability during operation. The introduction of this circlip feeder machine directly impacts the production process by reducing operator workload, minimizing delays caused by disorganized parts, and ensuring faster and more accurate installation of circlips. Consequently, the overall machining cycle time is shortened, thereby improving production flow and enhancing the efficiency of swing arm part manufacturing. Beyond immediate time savings, the use of the feeder machine contributes to better resource utilization, reduced ergonomic strain on operators, and improved consistency in product quality. This study highlights the significance of simple yet effective mechanical innovations in overcoming production bottlenecks and optimizing manufacturing processes in automotive component industries.

Warfarin (commonly known by its trade name, Coumadin) is an oral anticoagulant that has been widely used for the prevention and treatment of thromboembolic disorders. Despite its clinical benefits, warfarin therapy is complicated by a very narrow therapeutic index and wide inter-individual variability in dose requirements. This variability represents a major challenge for clinicians, as inappropriate dosing may lead to serious adverse outcomes such as bleeding or thrombotic events. A growing body of evidence suggests that genetic polymorphisms are among the most important factors contributing to this variability, particularly those involving the Vitamin K Epoxide Reductase Complex Subunit 1 (VKORC1) gene. VKORC1 encodes a key enzyme that functions as a bottleneck in the vitamin K cycle, playing an essential role in the regeneration of reduced vitamin K (VKH). This active form of vitamin K is required for the γ-carboxylation of vitamin K–dependent clotting factors, including prothrombin and other coagulation proteins. Polymorphisms within VKORC1 can significantly alter the enzyme’s expression and activity, thereby modifying an individual’s sensitivity to warfarin. One of the most clinically relevant variants is the -1639G>A (rs9923231) polymorphism, which reduces VKORC1 transcription and subsequently decreases enzyme activity. Patients carrying the A allele often exhibit increased sensitivity to warfarin and therefore require lower maintenance doses compared to those with the G allele. Understanding these genetic influences not only improves our knowledge of warfarin pharmacogenomics but also highlights the importance of personalized medicine in anticoagulant therapy. Incorporating VKORC1 genotyping into clinical practice could optimize dose prediction, minimize adverse events, and enhance the safety and effectiveness of warfarin therapy. This narrative review aims to provide an in-depth discussion of the complex role of VKORC1 in vitamin K metabolism and its impact on warfarin sensitivity, thereby underscoring the critical relevance of genetic factors in guiding individualized anticoagulation therapy.

This study aims to improve business process efficiency in the PLN Mobile application through the implementation of Business Process Model and Notation (BPMN). PLN Mobile is a digital innovation by PT PLN (Persero) that allows customers to access services such as bill payment, outage reporting, and service requests. However, various challenges remain, including slow processing times, lack of system integration, and inefficient workflows. The research was conducted at PT PLN (Persero) UP2D S2JB using observation, interviews, and literature review. The results indicate that modeling business processes using BPMN effectively maps workflows, identifies bottlenecks, and offers digital-based solutions. The application of BPMN has been shown to increase transparency, reduce input errors, and accelerate customer service processes. This study recommends ongoing optimization through enhanced analytics features, system security, and regular training for operational staff.

The port is a vital infrastructure in the global logistics chain that connects sea and land transportation. The efficiency of cargo loading and unloading operations at the port is a critical factor influencing the smooth flow of goods and overall logistics costs. In this context, the use of Business Intelligence (BI) as an integrated technological system to support data collection, analysis, and presentation is becoming increasingly important for optimizing data-driven decision-making in port environments. XYZ Port, as one of the strategic ports in Indonesia, faces challenges in managing complex loading and unloading operations with increasing cargo volumes. The implementation of a BI dashboard offers a solution to visualize operational data in real-time, identify bottlenecks, and optimize resource allocation. This study aims to implement a BI dashboard for cargo handling operations at XYZ Port. The specific objectives include identifying information needs in cargo handling operations and developing a BI dashboard that suits the characteristics of port operations. The findings indicate that the dashboard effectively supports real-time decision-making and enhances operational efficiency. The study also highlights the importance of addressing technological infrastructure, human resource readiness, and system integration challenges in BI implementation.

This research aims to determine whether the budget formulation of the Merah Putih Village Cooperative is in accordance with the norms and statutory regulations as stipulated in Law Number 25 of 1992 concerning Cooperatives, particularly in relation to Article 33 paragraph (4) of the 1945 Constitution, which emphasizes the principle of independence in the national economy. The results of this study focus on optimizing the establishment of the Merah Putih Village Cooperative to support the formation of national-scale cooperatives that function not only as savings and loan institutions driving the economy, but also address the challenges and risks that may arise from the establishment of the Merah Putih Cooperative. First, the issue of economic scale is crucial. Operations at the village level are often limited by available resources and market reach. Second, human resource capacity is a determining factor. The management of funds amounting to IDR 3–5 billion requires financial, business, operational, and marketing management skills that are not always present in every village. Experience with Village-Owned Enterprises (BUMDes) shows that one of their main challenges is finding local residents who are truly skilled managers. Third, elite capture or control by local elites can become a bottleneck. In villages with weak governance, influential figures may abuse their authority to control the cooperative for personal gain. Similar occurrences are often found in grant programs from Ministries/Agencies or the private sector, where key individuals dominate and exploit these resources for their own interests. Fourth, the risk of fraud.