Risk Priority Research Articles

Reducing overnight charges in the loading process of finished goods using FMEA method at PT. XYZ

As a part of the supply chain, the logistics department plays a crucial role in the operational activities of a company. Like other departments, the logistics department also incurs costs that do not add value, such as overnight charges. Failure in the loading of finished goods process can result in a dwell time exceeding 18 hours per vehicle, leading to overnight charges. This occurrence can be prevented by identifying risks and failure modes using Failure Mode and Effect Analysis (FMEA). The FMEA methodology is widely utilized across various industries, including manufacturing, for product development. Additionally, FMEA is frequently employed in failure analysis in service industries, food services, construction, mining, agriculture, and healthcare. The research was conducted to identify failure modes and potential causes in the loading of finished goods process. In PT. XYZ, there are five finished goods storage areas. From the process of weigh-in time, loading finish goods, and weigh-out time, 25 activities were analyzed, and seven activities with the highest Risk Priority Number (RPN) were selected for solutions. The activity with the highest RPN, scoring 512, was the queuing of trucks exceeding loading capacity. The why-why analysis technique was employed to assist in finding solutions. From the seven activities with the highest RPN, 9 solution plans were derived. The recommendation to reduce queue trucks is to organize and monitor the vehicles dispatched by the Freight Forwarder. As a result, the dwell time per vehicle decreased from 9.23 hours to 6.36 hours, and the overnight charges reduced from 148,145 USD to 12,345 USD

Penilaian Risiko menggunakan Metode Failure Mode and Effects Analysis (FMEA) terkait Kontaminasi Silang pada Area Pengemasan di Industri Farmasi “XYZ”

Cross-contamination occurs due to other previously produced products, other rooms or areas producing different products, and particles from machinery or equipment. Quality risk management can be a preventive measure in reducing the potential for cross-contamination that occurs in the pharmaceutical industry. One part of quality risk management is risk assessment. One of the most common and effective methods is the Failure Mode and Effect Analysis (FMEA) method. This risk assessment is carried out using the stages of risk identification, risk analysis, and risk evaluation using categorization based on the Risk Priority Number (RPN). Based on the risk assessment using the FMEA method regarding cross-contamination in the Packaging area in the "XYZ" pharmaceutical industry, it can be concluded that the results of observations and interviews with operators and Packaging Supervisors identified 19 risks which were categorized as low, medium, high, and very high. Based on the results of the risk assessment obtained, the risk of cross-contamination that needs to be controlled is those with an RPN value of 30 ≤ RPN ≤ 60 (medium); 75 ≤ RPN ≤ 100 (high); RPN ≥ 150 (very high). Therefore, the "XYZ" pharmaceutical industry needs to determine and implement risk control measures. By adequately controlling the potential risk of cross-contamination, it will ensure the safety and quality of the products produced.

Development of modified fuzzy FMEA method in environmental risk assessment of earth dams

The investigations have shown that the construction of the dam and its related facilities have significant physical–chemical and ecological effects on the ecosystem. Failure Mode and Effects Analysis (FMEA) is a technique for ranking risks in projects to construct dams, but it has many deficiencies and ambiguities. Therefore, to prevent the shortcomings of the classical method, the modified fuzzy inference system (MFIS-FMEA) method has been used by creating a two-stage model to more accurately assess the risk of Eyvashan Dam. First, all the considered indicators are weighted using the Shannon entropy method, and the environmental risk is prioritized using the Fuzzy OWA method. In this study, two-stage fuzzy reasoning and a Max–Min combination rule are used. When severity (SEV) and occurrence (OCC) variables are combined, the critical risk index (RCI) values are predicted in the first stage. RCI and detection index (DET) input are then used to predict the MFIS-RPN in the second stage. The results of the risk priority number (RPN) in the MFIS-RPN method are much more accurate and serious than the FIS-RPN method due to the two-stage nature and the use of new language terms. The results of the proposed MFIS-RPN technique show that the highest RPN was obtained with immediate action in the dam construction phase for soil erosion and soil pollution and in the dam operation phase for aquatic and water pollution. Therefore, due to the increase in risk score, it is necessary to take immediate and more accurate monitoring during the construction and operation phases.

Analysis Of Defects In Laminated Doors Using Failure Mode And Effect Analysis (FMEA) And Fault Tree Analysis (FTA)

Quality control is an crucial part of the production process because product quality must meet established standards. Based on drawback that happen in the laminating door production process, prioritizing different failure types is essential to reduce the occurrence of defects in the same production process. To enhance product quality and minimize defects, consider implementing by using Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA). The purpose of this study is to identify the types of factors causing product defects in the laminating door production process and provide recommended actions in the improve phase to minimize the number of defects. This study analyzes six types of defects in laminating door products. The results show that operator errors are the dominant cause, influenced by environmental conditions and an unsupportive work system. Based on the Risk Priority Number (RPN) values, the defects with the highest values are glue not sticking (RPN 294) caused by machine factors, cracks (RPN 252) by method factors, groove size not meeting specifications (RPN 245) by man factors, peeling paint (RPN 180) by method factors, scratches on the laminate surface (RPN 175) by man factors, and imprecise joints (RPN 120) by man factors. Improvement suggestions include operator training, machine maintenance, replacement of inadequate equipment, selection of appropriate raw materials, and implementation of clear Standard Operating Procedures.

Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.

Fuzzy FMEA-Resilience Approach for Maintenance Planning in a Plastics Industry ‎

The productivity and efficiency of industrial systems are highly affected by failures and machine breakdowns. Further, in asset-intensive industries, unexpected failures are considered the primary source of operational risk. In response, the maintenance department strives to calculate reliable estimates of the risk levels associated with such failures and develop resilient maintenance strategies that enable it to respond effectively to equipment failures. The research developed a framework for integrating fuzzy failure mode and effects analysis (FFMEA) with resilience engineering (RE) concepts for maintenance planning. The framework consists of four main stages: FFMEA, Risk iso-surface (RI), resilience assessment, and maintenance planning. In FFMEA, multiple sub-factors were considered for each main risk factor and evaluated using fuzzy logic. Then, in the RI stage, the risk priority number (RPN) was calculated through a fuzzy approach that considered the order of the importance of the main three risk factors. The fuzzy resilience assessment was applied through a survey of fifty-one questions related to the main four RE potentials to determine the need for resilient maintenance strategies. Finally, the RPN-Resilience diagram was employed to classify maintenance activities into six main maintenance strategies. A case study from a production line of plastic bags was used for illustration. The main advantage of the proposed FFMEA is that it divides the main risk criteria into sub-criteria to increase the accuracy of risk assessment and evaluate resilience potentials under fuzziness. In conclusion, the integration of the risk-resilience evaluation is a valuable tool for effectively planning maintenance activities.

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

A Fuzzy Decision Support System for Risk Prioritization in Fine Kinney-based Occupational Risk Analysis

Analyzing Occupational Health and Safety (OHS) risks requires prioritizing risk, a vital step in hazard and effect analysis (HEA). Currently, there is no existing approach to determine the priority of risk in HEA-based occupational risk analysis that incorporates interactive risk factors and spherical fuzzy risk information. This paper presents a novel approach to address the constraints by introducing a fresh framework for evaluating job-related hazards using HEA by combining the spherical fuzzy set (SFS), the multi-attributive border approximation area comparison (MABAC) technique, and the Ordered Weighted Averaging (OWA) operator, a more sophisticated approach is achieved within this framework. The SFS is utilized in this framework to depict the more ambiguous and unsure data given by specialists, offering a more efficient approach to handling the fuzzy risk information, encompassing the non-membership degree and hesitation. In addition, an advanced MABAC method is used to prioritize occupational risk. Afterward, we provide a practical instance of utilizing the MABAC technical-oriented risk prioritization approach for evaluating potential occupational dangers in risk analysis.

Combining the analytical hierarchy process, fuzzy expert systems, and the exponential risk priority number for the holistic evaluation of innovation projects in manufacturing

ABSTRACT Manufacturing companies operate in a complex environment and need efficient manufacturing processes to remain competitive. Therefore, evaluation methods are essential for decision makers when selecting manufacturing innovation projects (MIPs). However, most approaches are not suitable for strategic use and do not consider all relevant evaluation dimensions. To address this issue, this work presents an approach to evaluate and select MIPs holistically, considering potential, effort, and risk. The approach enables the analysis of the strategic impact of an MIP using a fuzzy expert system, and further evaluates the implementation effort and risk using a combination of the Analytical Hierarchy Process and the Exponential Risk Priority Number. The approach was developed using the results of a systematic literature review and expert-based methods. Finally, the approach was validated in an industrial case study and enabled a transparent evaluation of the strategic potential, effort, and risk of two MIPs, leading to informed project selection.

A method for empirically validating FMEA RPN scores in a radiation oncology clinic using physics QC data.

In failure modes and effects analysis (FMEA), the components of the risk priority number (RPN) for a failure mode (FM) are often chosen by consensus. We describe an empirical method for estimating the occurrence (O) and detectability (D) components of a RPN. The method requires for a given FM that its associated quality control measure be performed twice as is the case when a FM is checked for in an initial physics check and again during a weekly physics check. If instances of the FM caught by these checks are recorded, O and D can be computed. Incorporation of the remaining RPN component, Severity, is discussed. This method can be used as part of quality management design ahead of an anticipated FMEA or afterwards to validate consensus values.

Using the Failure Mode and Effect Analysis Tool to Improve the Automatic Stop Order Process

Introduction Automatic stop orders (ASOs) in computerized prescription order entry (CPOE) systems predefine the length of treatment. This can improve resource use for select therapies (e.g., empirical antibiotics). However, root cause analysis of dose omission errors identified inappropriate ASO-directed termination of medications without prescriber notification. This quality improvement initiative aimed to identify potential failures of the medication ASO processes to develop a new workflow and anticipate issues that may arise after implementation. Methods A failure mode and effect analysis (FMEA) was conducted following Institute of Healthcare Improvement guidance. A multidisciplinary ASO-FMEA team reviewed the existing workflow. Failure modes, risk priority numbers (RPNs), and interventions were identified and assessed. The RPNs calculated for the proposed new workflow (assuming all recommendations were implemented) were compared with those of the existing workflow. Results Eight failure modes, 17 effects, and 31 causes were identified in the five workflow steps (mean RPN 365.4; median 280). Specific, measurable, achievable, realistic, and time-bound interventions were proposed. Assuming successful implementation of all recommendations, the RPNs of the proposed workflow (mean 117.6; median 112) were significantly lower (p < 0.05). Conclusion When modifying existing CPOE systems, FMEA may identify possible failures that can be addressed before the implementation of a new process. This may prevent errors, improving medication safety. Regardless, continuous audit and monitoring are required to ensure the effectiveness of implemented changes.

A novel sequential risk assessment model for analyzing commercial aviation accidents: Soft computing perspective.

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.

Analisa Cacat Produk Dan Kerusakan Mold Pada Proses Injection Molding Dan Tindakan Perbaikan Di PT. Patco Elektronik Teknologi

This study examines product defects and mold damage in the injection molding process and corrective actions at PT. Patco Elektronik Teknologi. This type of research is field research with a qualitative approach. Researchers use primary and secondary data sources. Data collection techniques through observation, interviews, and documents related to product defects and mold damage in the injection molding process and corrective actions. The data were analyzed using data reduction, data presentation, and conclusion drawing methods. The results of the study show that injection molding has many advantages when compared to other plastic manufacturing methods, but in the process there are various defects in the plastic products produced. Based on the FMEA analysis, a number of product defects occur due to several factors, such as Bending, Gas Mark, Silver, Flash, Under Cut, Crack, Flow Mark, Scratches, Burn Mark, and Short Mold. Gas Mark has the highest risk priority number (RPN) value of 75. The problem of defects due to gas marks occurs in the housing panel components produced by PT. Patco Elektronik Teknologi. The company took corrective action NG Gas Mark on the housing panel product by means of mold maintenance and gas vent making by the mold shop for the cavity and ejector pin parts. After the gas vent making process was carried out, defects due to gas marks were greatly reduced in the housing panel products produced by PT. Patco.

Implementation of Failure Mode and Effect Analysis (FMEA) for centrifugal pump maintenance in water supply distribution system

A centrifugal pump is an essential mechanical device in the Regional Drinking Water Company (PDAM) to distribute sterile water from the reservoir to local community houses by flowing the fluid through the pipeline. However, improper and inadequate maintenance of centrifugal pumps can result in component damage, including bearings that are broken, ripped, or damaged mechanical seals. Therefore, for the water supply to continue smoothly, proper maintenance needs to be carried out so that the engine's performance will be maintained and last longer. This study aims to conduct a maintenance analysis on centrifugal pumps utilized by the PDAM of Madiun City. The Failure Modes and Effects Analysis (FMEA) method was employed to assess the potential failure of a system, design, process, or service by implementing a systematic approach to analyze damage and devise maintenance strategies. The FMEA analysis revealed that the shaft of the centrifugal pump component had the highest Risk Priority Number (RPN) value, which amounts to 294. The wearing ring had the lowest RPN value among the centrifugal pump components, with a value of 54. The Pareto diagram results classified six components: shaft, clutch, stuffing box, motor, impeller, and mechanical seal to recommend preventative maintenance action. Additionally, three other components suggested for corrective maintenance were the bearing, packing, and wearing rings

Risk management of radioiodine treatment in differentiated thyroid cancer

IntroductionPatient safety is paramount in providing quality healthcare and constitutes a global concern for healthcare systems. Radioiodine treatment to patients with well-differentiated thyroid cancer is not without risks. The aim of this study is to identify, evaluate and mitigate the risks associated with this procedure. Materials and methodsA single-centre descriptive study was conducted in which risk management was carried out by establishing a risk map using FMEA methodology. ResultsBased on the process map 6 sub-processes and 23 failure modes in the three phases of the treatment process were analysed. According to risk priority number (RPN), the sub-process with the highest risk was administrative management (RPN 82), followed by treatment per se and post-treatment imaging (both with RPN 70). An overall process RPN of 300 (156 pre-treatment, 74 treatment and 70 post-treatment) was obtained. Failures directly related to the patient pose a high risk. The implementation of verification systems, performing tasks earlier and providing quality medical information are the most relevant preventive measures to be implemented. ConclusionsThe application of the FMEA methodology in the risk management for radioiodine treatment is a valuable tool for improving the quality and safety of this process. The risk map has been able to identify failures at different stages, assess their causes and effects, prioritise the risks identified and implement preventive and corrective measures that can be monitored, ensuring the effectiveness of the actions taken.

Quantifying the Risk of Technology-Driven Health Disparities in Radiation Oncology

PurposeNew technologies are continuously emerging in radiation oncology. Inherent technological limitations can result in healthcare disparities in vulnerable patient populations. These limitations must be considered for existing and new technologies in the clinic to provide equitable care. Materials and MethodsWe created a health disparity risk assessment metric inspired by failure mode and effects analysis (FMEA). We provide sample patient populations and their potential associated disparities, guidelines for clinics and vendors, and example applications of the methodology. ResultsA disparity risk priority number (dRPN) can be calculated from the product of three quantifiable metrics: the percent of patients impacted (P), the severity of the impact of dosimetric uncertainty or quality of the radiation plan (S), and the clinical dependence on the evaluated technology (C). The dRPN can be used to rank the risk of sub-optimal care due to technical limitations when comparing technologies and to plan interventions when technology is shown to have inequitable performance in the patient population of a clinic. ConclusionThe proposed methodology may simplify the evaluation of how new technology impacts vulnerable populations, help clinics quantify the limitations of their technological resources, and plan appropriate interventions to improve equity in radiation treatments.

Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications

Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis. Notably, eight failure modes were reduced, including seven with severity factors ≥7, indicating the effect on patients, and two with Risk Priority Number value >125, which assesses relative risk level. Efficiency, measured by mouse clicks, showed zero clicks with the automatic workflow. This automation illustrated improvement in both safety and efficiency of workflow

Analysis and Improvement of the Risk Assessment System Based on the Risk Priority Number

Analysis and Improvement of the Risk Assessment System Based on the Risk Priority Number

Transforming place-based management within watersheds in Fiji: The watershed interventions for systems health project

Watersheds offer opportunities for place-based interventions to transform systems health via preventative versus reactive approaches to management that achieve multiple co-benefits for public and environmental health. The Watershed Interventions for Systems Health in Fiji (WISH Fiji) project embraced participatory knowledge co-production and action-oriented research to identify risks to public and ecosystem health, prioritize interventions to address risks, and monitor responses of the system to interventions. We used screening filters and local knowledge to collaboratively identify five watersheds for action with high prior incidence of water-related diseases (Fiji’s “three plagues” of leptospirosis, typhoid and dengue) and high risk to downstream environmental health. We reviewed literature to identify disease risk factors, evaluated overlaps with risks for downstream environmental impact, and designed 13 instruments to collect information about baseline risk. Following consultations to obtain free, prior and informed consent, we enrolled 311 households across 29 communities. We synthesized data to identify key risks at the household, community, and landscape level, which were communicated to community water and resource management committees and government leaders as part of developing water and sanitation safety plans for each community. Local committees identified 339 priority risk reduction actions across nine main categories: animal management; drainage; health systems surveillance; hygiene; integrated planning; land use management; sanitation systems; waste management; and water systems. As of October 2022, 154 interventions were implemented in the five watersheds across different risk categories and scales. While we can track changes to factors that reduce risk of water-related disease and improve environmental health, direct evaluation of impacts to public health is limited due to poor geolocation of case records. The WISH Fiji project is a model of cross-sectoral coordination that efficiently progresses multiple Sustainable Development Goals, but scaling requires sustained investment in interventions to realize full benefits, particularly for nature-based solutions that exhibit lagged responses.

Production risk management of prawn crackers using fuzzy FMEA and fuzzy AHP methods

Prawn crackers hold a significant position within Indonesia's agro-industrial product landscape. The industrial process involved in their production is fraught with potential risks that could result in substantial losses for companies. PT. XYZ, a prominent prawn cracker producer in Indonesia, grapples with various risks, including non-compliant raw material supplies, machinery and equipment breakdowns, product defects, and process failures. Hence, there is an imperative need for comprehensive risk management involving further analysis, identification of potential failure modes, and formulation of mitigation strategies to curb losses. This research aims to ascertain the priority of potential failure modes and their corresponding mitigation strategies in the prawn cracker production process at PT. XYZ. The study employs risk analysis through the Fuzzy Failure Mode and Effect Analysis (Fuzzy-FMEA) method and determines mitigation strategies using the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) method. The findings reveal 11 identified failure modes in PT. XYZ's prawn cracker production process, with machinery and equipment damage emerging as the top-priority failure mode, giving a Fuzzy Risk Priority Number (FRPN) value of 7.620. The recommended mitigation strategy involves intensifying inspections of production machinery and equipment, both before and after use by workers, with a weight of 0.368.