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This study was conducted at PT. Asmin Bara Bronang, Sepan Uring Village, Kapuas Tengah District, Kapuas Regency, Central Kalimantan Province, with the aim of analyzing the productivity and influencing factors of the PC 300 excavator in coal getting activities to support the achievement of production targets. The objective of this research is to analyze the actual productivity and the factors affecting it in Sector 7 coal getting operations. The research method used field observation with a quantitative descriptive analysis approach. The results show that the productivity of the PC 300 excavator ranges from 127.12 to 224.29 tons/hour, with an average of 173.98 tons/hour. In several conditions, the productivity is still below the company’s target of 180 tons/hour. The analysis indicates that productivity is influenced by material conditions, particularly the Hardgrove Grindability Index (HGI) value of 47, which reflects relatively harder material with coarser particle size, and a moisture content of 22.71%, causing the material to be sticky and cohesive, thereby affecting the bucket filling process. In addition, operational factors such as bottom loading patterns, limited number of tailgate dump trucks, and suboptimal selection of dump truck types also contribute to productivity performance.  Based on the findings, it can be concluded that the productivity of the PC 300 excavator has not consistently met the company’s target. Therefore, improvement efforts are required, including the implementation of top loading methods, increasing the number of tailgate dump trucks, optimizing the selection of hauling equipment, and controlling material conditions that affect the digging process.    

The study analyzes the implementation of the 5S method (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) in improving shoe production quality at the Assembly section of PT X through observation, interviews, and documentation. Involving supervisors, production leaders, and operators, the study aimed to assess the impact of 5S on production processes. The results showed that the implementation of 5S successfully created a more organized, cleaner, and standardized work environment. These improvements led to a reduction in potential work errors, increased process efficiency, and the establishment of an employee disciplinary culture. Employees became more aware of maintaining cleanliness and orderliness, which contributed to a smoother workflow and better quality control. The findings align with theories of quality management, organizational behavior, and operations management, demonstrating the benefits of 5S in enhancing operational effectiveness. This study confirms that integrating the 5S method into industrial practices, particularly during internships, is an effective strategy to encourage continuous improvement and enhance production quality in the industry.

The shoe manufacturing industry demands a planned, integrated, and consistent production system in maintaining product quality. One of the crucial stages is the gluing process between the outsole (rubber) and the midsole (phylon), which determines the adhesive strength, comfort, and durability of the shoe. This study aims to analyze the implementation of internship activities in a series of gluing processes in the Stockfit Department of PT X. The research method uses a qualitative descriptive approach with data collection techniques through direct observation, involvement in production activities, and systematic field recording during the internship period. The analysis was carried out by linking the field findings with the operations management theory of Heizer and Render, the Crosby concept of quality management, and the principles of continuous improvement from Deming and Imai. The results of the study show that the gluing process has been systematically arranged, starting from material adjustment, primary application, the use of two types of glue, the rubber cutting process, to the final pasting. However, problems were found such as dependency between processes, inconsistency in glue thickness, as well as the influence of machine conditions and operator skills. These problems have the potential to reduce production efficiency and the quality of the final product. Therefore, it is necessary to strengthen process control and apply the principle of continuous improvement to improve overall production performance.

Operational risk management is an essential aspect in maintaining the stability and sustainability of micro-enterprises operating in the culinary sector, particularly in the fast food industry, which relies heavily on production processes, raw material quality, and human resource skills. This study aims to analyze the types of operational risks that arise in the daily operations of the Kebab Capadocia MSME in Boyolali Regency using a qualitative approach through in-depth interviews and field observations. The COSO Enterprise Risk Management (ERM) Framework was used as the primary theoretical foundation to map risks based on five components: risk identification, risk assessment, control activities, information and communication, and monitoring. The results indicate that the business faces four main risk categories: production process risks, human resource risks, food hygiene and safety risks, and raw material distribution risks. Implemented controls are still informal and not supported by standard operating procedures (SOPs), resulting in recurring risks that impact product quality. These findings contribute to the MSME risk management literature and offer practical recommendations for strengthening operational structures based on systematic risk management.

This study aims to analyze the transformative impact of digitalization and technology in the manufacturing sector on improving operational efficiency, particularly in budgeting and resource utilization, as well as to identify the main barriers to technology adoption. Using a Literature Review and Case Study Analysis of secondary data (journals, company reports, and industry publications), it was found that digitalization and Automation supported by Artificial Intelligence (AI) fundamentally transform budgeting functions. This transformation has been shown to improve budget accuracy by up to 50% (reducing human errors) and process efficiency by up to 25%, turning budgets from static documents into adaptive and predictive control tools. Positive impacts are also observed in operations through increased production capacity (revenue surge) and the implementation of Predictive Maintenance, which reduces expenditure and asset downtime, in line with the principles Cost Efficiency and Lean Manufacturing. Nevertheless, the adoption of advanced technology faces significant obstacles, namely high initial capital investment and skill gaps among the workforce. It is concluded that the success of digitalization heavily depends on strategic budget planning to overcome capital barriers and adequate allocation of funds for Human Resource (HR) training to support effective collaboration between humans and machines.

Production planning and inventory control are critical aspects of operations management, as they directly influence cost efficiency, resource utilization, and the continuity of the production process. Ineffective planning and inventory decisions may lead to excessive costs, production delays, or imbalances between supply and demand. The complexity of these problems, which often involve multi-period horizons and multi-stage decision-making processes, has encouraged the application of quantitative optimization methods, one of which is dynamic programming. This study aims to analyze and synthesize the application of dynamic programming in production planning and inventory control through a Systematic Literature Review (SLR) approach. The SLR process was conducted by systematically identifying, selecting, and analyzing 15 relevant national journal articles published between 2015 and 2024 and obtained from various recognized scientific databases. The reviewed literature indicates that dynamic programming is effective in supporting optimal decision-making by determining appropriate production quantities and inventory levels, minimizing total production and holding costs, and managing fluctuating demand conditions. In addition, this method helps reduce the risks associated with overstock and stockouts by considering sequential decision structures. However, the findings also reveal several limitations of dynamic programming, including high computational complexity, strong dependence on deterministic data assumptions, and limited flexibility in handling high levels of uncertainty. These constraints suggest the need for further methodological development or integration with other approaches to enhance practical applicability.