Peran Manajemen Risiko dalam Meningkatkan Kinerja Mesin Filling Emulsion dengan Pendekatan House of Risk (HOR)

A Method for Evaluating Quality Assurance Needs in Radiation Therapy

This study assesses the effectiveness of the House of Risk (HOR) method in mitigating supply chain risks within palm oil processing plants. By integrating Failure Mode and Effects Analysis (FMEA) with House of Quality (HOQ), the method identifies and prioritizes risk factors while proposing actionable measures for risk mitigation. Findings demonstrate that adopting the HOR method contributes to decreased liquid waste generation and enhanced production efficiency. Additionally, a bibliometric analysis of scholarly publications reveals a notable growth in research focused on risk management in the palm oil sector between 2014 and 2024. This highlights a growing recognition of the importance of systematic risk assessment and management strategies in enhancing sustainability and operational resilience within palm oil processing. The study underscores the HOR method's utility as a comprehensive approach to addressing complex supply chain challenges, thereby promoting sustainable practices and operational excellence in the industry.

Manufacturing companies attach importance to Total Productive Maintenance (TPM) applications to extend equipment life and increase efficiency. The recommended applications with TPM are carried out under the pillar activities. Autonomous Maintenance (AM) pillar manage the assignments of the operators to undertake routine maintenance work on machine maintenance. Cleaning, lubrication and control activities are one of the steps of AM pillar and operators need to do them daily. However, there are a number of occupational health and safety risks that operators may face during these activities. Safety (S) pillar which serve as another TPM pillar, deal with occupational accidents and possible situations during TPM applications. AM and S pillars work together to assess the risks that may occur during AM applications. Failure Mode and Effect Analysis (FMEA) is also frequently utilized for risk assessment, but this is criticized in terms of the difficulty in reaching the common point of decision-makers’ risk assessment and equal weighting of risk factors. Therefore, it is appropriate to support with the fuzzy logic approach. In this study, the entropy-weighted fuzzy based FMEA method was utilized for identify and prioritize potential risks that may be encountered during the AM activities. Potential risks were revealed and evaluated with the FMEA team. Eleven potential risks were identified in the study. The risk factors of the assessment were weighted by the entropy method. The risk of hand injuring during cleaning the oil below material cutting saw has the highest risk priority.

The Improvement of a Novel Radiation Therapy Workflow by Failure Mode and Effects Analysis

The creation of a good product PT. DIRGANTARA INDONESIA assisted with production machines, one of which is the machine CNC MACHINE (DOUBLE CINCINNATI Milacron gantry ALUMINIUM TYPE-F), the role of a machine type-f CNC machine itself is very influential in the existing production process at PT. DIRGANTARA INDONESIA. The lack of treatment systems for the repair and replacement of components caused because the machines that is already beyond the age of many techniques, resulting in the production of the company to become unstable.In this study used methods Overall Equipment Effectiveness (OEE) and the concept of autonomous maintenance and using Failure Mode and Effect Analysis (FMEA) to determine analyze potential errors or failures, it is advisable to keep using autonomous maintenance as minor maintenance. Analysis and discussion of the results obtained OEE decline in 2014 to 2015 is 86.15% to 75.62% drop is due to the low value avalaibility, and get the highest objective value RPN 441 on a 40 point damage to component Servo Off. Solutions to make improvements to enable the Total Productive Maintenance in which includes Autonomous Maintenance. Keywords: Total Productive Maintenance (TPM), Overall Equipment Efectiveness (OEE), Autonomous Maintenance, Failure Mode and Effect Analysis (FMEA)

Targeting specimen misprocessing safety events with failure modes and effects analysis.

Risk Management: It's Not Just FMEA

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Failure mode effect and analysis (FMEA) is a well-known methodology for improving operational performance, such as the quality, cost, and delivery time of a product, process, or system. However, the success of FMEA in operational performance cannot be quantified at present. The risk priority number (RPN), which is the only indicator of FMEA, has no relationship with operational performance. For example, a reduction in RPN may not benefit operational performance, particularly if the corrective action is to enhance detection. Thus, the capability of FMEA to improve operational performance cannot be validated unless it demonstrates quantifiable operational performance. An organisation is distinct from its competitors based on its operational performance and not based on RPN. The missing operational performance measurement practice in FMEA is essential for further strengthening the FMEA methodology. This study aims to identify the most effective methodology and approach for quantifying the success of FMEA in operational performance through a systematic review and to classify FMEA articles that measured operational performance.

Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high-severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.

Fuzzy Inference Based FMEA for MR Image Based HDR Brachytherapy

Clustering and visualization of failure modes using an evolving tree

This study aims to design risk management for a vehicle rental company in East Kalimantan using the Enterprise Risk Management (ERM) approach and the House of Risk (HOR) model. The transportation industry, particularly vehicle rentals, faces various risks that can disrupt the company's operations and performance. These risks include vehicle damage, delays in spare parts delivery, and payment issues from customers. This research will design Risk Management by applying Enterprise Risk Management (ERM) using the ISO 31000:2018 approach. In this study, the HOR method is used to identify, analyze, and prioritize risks and risk agents. This research combines the Failure Mode and Effect Analysis (FMEA) and Quality Function Deployment (QFD) methods to provide an integrated framework in risk management analysis. The results of the study indicate that the implementation of structured risk management can improve the company's operational effectiveness and efficiency. The proposed mitigation strategies include enhancing vehicle maintenance, staff training, and developing better monitoring systems. By implementing ERM and HOR, it is expected to manage risks more effectively, reduce the negative impact of occurring risks, and achieve the company's objectives more efficiently.

Aim. The aim of this paper is to improve the efficiency of the failure mode and effects analysis (FMEA) method through the verification of expert judgment correctness by means of statistical methods. Therefore, the paper deals with the matters of improving the quality of products and services in various enterprises through risk-oriented approaches. Methods. For the purpose of improving the efficiency of the failure mode and effects analysis (FMEA) method, it is suggested to increase the number of experts, while making the expert evaluation an independent process, i.e. by separating the experts from each other. The resulting expert judgment is proposed to be considered as a random value. The correctness of the expert judgment is suggested to be evaluated by means of statistical criteria methods, e.g. Grubbs’ criterion methods. The proposed evaluation methods are not limited to the Grubbs’ criterion methods. This criterion can be replaced by the Cochran’s criterion or Shewhart charts. Each of the suggested methods enables more efficient estimates with lower risks in the process of service provision or product manufacture. The paper proposes statistical methods with the example of the Grubbs’ criterion. All indicators of the integral estimation of the failure mode and effects analysis are submitted to statistical verification. Results. Such data verification as part of an independent expert evaluation enables a higher reliability of expert judgment and significantly reduces the number of risks at the enterprise. Such risks may include bribing or collusion of experts involved in the performance of the failure mode and effects analysis. Independent expert judgments after expert evaluation are verified by means of statistical methods. Sharp spikes in independent expert opinions will justify repeated expert evaluation, while complete agreement of evaluations will eliminate doubts regarding the quality of the performed assessment. The use of statistical methods for the evaluation of every indicator of the integral FMEA estimation will allow increasing its reliability. A combination of those approaches enables an independent estimation as part of various projects evaluation, including the evaluation of industrial products or provided service using failure mode and effects analysis, elimination of the human factor in the estimation procedure, significant reduction of risks. Conclusions. Failure mode and effects analysis (FMEA) was performed. Additionally, the method was improved by means of independent expert assessment. The consistency of the results of such evaluation is verified by means of statistical methods. The performance of such verification of independent expert opinion is demonstrated through the Grubbs’ criterion. Expert opinion can also be verified by means of Cochran’s criterion, Shewhart charts. The proposed approach is a combination of the failure mode and effects analysis (FMEA) method and statistical methods with the example of Grubbs’ criterion.

BackgroundFailure Mode and Effect Analysis (FMEA) is a structured approach to identify and assess potential failure modes and their impact on the system being analyzed to prevent or mitigate failures...