Failure Mode And Effect Analysis Analysis Research Articles

Optimization of treatment workflow for 0.35T MR-Linac system.

This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.

The risk-based operational safety strategy for Perkin-Elmer/Cetus DNA Thermal Cycler PCR systems

Introduction: this article was conducted in the field of polymerase chain reaction (PCR) test safety, where it is essential to understand the contribution and impact of risk analysis methods to improve test safety medical diagnosis at the institutional level. In this context, the current study focuses on failure modes and effects analysis (FMEA) applied to the Perkin-Elmer/Cetus deoxyribonucleic acid (DNA) thermocycler.Objective: the objective of this study is to provide practitioners with a comprehensive overview of the application of FMEA to improve the operational safety of Perkin-Elmer/Cetus DNA thermocyclers used in medical diagnostics in ataxic patients.Methods: to carry out this study, a methodology based on an exhaustive review of the scientific literature available in academic databases and relevant sources was used. Previous studies on FMEA analysis of DNA thermocyclers in common and expanded forms were collected and analyzed.Results: the results obtained demonstrated that FMEA is an effective tool to identify and evaluate possible failure modes of Perkin-Elmer/Cetus DNA thermal cyclers. Several defects were identified and their consequences, causes and classification based on their severity and frequency were determined. Likewise, corrective actions are proposed to reduce the identified risks.Conclusions: early identification of possible failure modes and implementation of corrective actions can reduce risk and guarantee the quality of the diagnoses carried out and therefore improve operational safety.

Identification of Advantages and Limitations of Current Risk Assessment and Hazard Analysis Methods when Applied on Autonomous Agricultural Machineries

Highlights The three main types of risk assessment and hazard analysis techniques applied on autonomous agricultural machines are: (1) Informal Group Analysis; (2) Hazard Analysis and Risk Assessment (HARA); and (3) Failure Mode and Effects Analysis (FMEA). Replicability is the main advantage of FMEA and HARA, while cost effectiveness is the main advantage of Informal Group Analysis. Subjectivity and the requirement for prior knowledge (data) are the main weaknesses of FMEA, HARA, and Informal Group Analysis when applied to novel and revolutionary autonomous agricultural machines. Abstract. In the last ten years, the development of automated agricultural machinery has seen noteworthy advancements. Nevertheless, the successful commercialization of these technologies depends critically on their ability to operate safely. This study evaluated the advantages and limitations of current risk assessment and hazard analysis methods currently used to ensure the safety of autonomous agricultural machines. An online survey containing 18 questions was distributed to 711 participants identified as potential individuals who are currently working or have worked on autonomous agricultural machines to determine the type and frequency of risk assessment and hazard analysis methods applied on autonomous agricultural machines, examine the advantages and limitations of each method, and investigate the perceived effectiveness of each method. Frequency analysis was used to determine the most and least utilized risk assessment and hazard analysis methods. The advantages and limitations of each risk assessment and hazard analysis approach were compared. Descriptive statistics (counts, means, medians, percent) and frequency analysis of the variables were used. The three main types of risk assessment and hazard analysis techniques applied to autonomous agricultural machines. The methods are (a) Informal Group Analysis (e.g., Brainstorming), (b) Hazard Analysis and Risk Assessment (HARA), and (c) Failure Mode and Effects Analysis (FMEA). Replicability is perceived as the main advantage of FMEA and HARA, while cost-effectiveness is the main advantage of Informal Group Analysis. The need to have pre-existing data of the autonomous agricultural machine at hand to be able to perform risk assessment and subjectivity are the main limitations of FMEA, HARA, and Informal Group Analysis dealing with novel and revolutionary autonomous agricultural machines. Industry experts do not believe that the risk assessment and hazard analysis procedures now used are reliable and efficient enough to guarantee the safety of autonomous agricultural tractors. This study reveals important information about the current state of risk assessment and hazard analysis methods in the context of autonomous agricultural machinery. This knowledge can inform future research, policy development, and industry practices to ensure the safety of autonomous agricultural machines. Keywords: Agricultural machine, Autonomous, FMEA, HARA, Hazard analysis, Informal Group Analysis, Risk assessment, Safety, Survey.

Evaluasi Kinerja Mesin Bending Hidrolik Menggunakan Metode Overall Equipment Effectiveness (OEE), Failure Mode and Effect Analysis (FMEA), dan Fault Tree Analysis (FTA)

PT Hari Mukti Teknik is a company engaged in the Manufacturing Industry that produces laundry machines and other mechanical engineering. The problem that occurs in the production process at PT Hari Mukti Teknik is the performance of the bending machine which has a high downtime value of an average of 994 minutes per month. The purpose of this study is to evaluate the performance of the bending machine by knowing the level of effectiveness of the performance of the bending machine using the OEE method, knowing the causes of losses that affect the level of effectiveness of machine performance, using the six big losses method and analyzing losses using the Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods to propose improvement efforts. Based on the results of data processing, the average Overall Equipment Effectiveness (OEE) value of bending machines in one year is 84.21%, with the largest losses being equipment failure losses of 7.34%. Proposed improvements based on FMEA and FTA analysis are to carry out periodic maintenance, make a predictive maintenance list, carry out periodic re-checks, carry out periodic lubrication, check environmental cleanliness, and replace damaged components.

Failure Mode and Effects Analysis Prior to the Introduction of AI Generated GTVs for Brain Metastases in the Clinical Workflow

AI autosegmentation of organs-at-risk (OARs) is common practice at many radiotherapy clinics. Despite the abundance of gross tumor volume (GTV) autosegmentation algorithms, adoption in clinical care has been slow due to the high risk associated with errors in GTV delineation. Here we present a failure mode and effects analysis (FMEA) to evaluate the risk associated with introducing AI derived GTVs in patients treated with stereotactic radiosurgery (SRS). An AI GTV autosegmentation algorithm for brain metastases was developed in-house based on a V-Net 3D CNN. Registered CT and MR images and a contour of the brain are input into the software and all identified lesions are returned in a DICOM-RT structure set. Following algorithm evaluation, a workflow was developed to enable AI GTV autosegmentation to be introduced clinically for every SRS patient. The following steps were added to existing procedures: 1) workflow to send CT/MR and brain structure to external server, 2) autosegmentation run on the server, 3) AI GTV structures with a standard nomenclature added to existing OAR structure set, and 4) MD review, editing, and approval of AI GTVs. After successfully completing the physics evaluation testing of the new process, we formed a team of 10 faculty and staff including physicists, residents, physicians, and planners to perform the FMEA prior to clinical implementation. The team met to map the process, identify potential failure modes, and score their frequency of occurrence, severity, and detectability. A 3-point scale (1, 3, or 5) was used to simplify the scoring process. Occurrence was defined as rare, sometimes, or often; severity as low, medium, or high; and detectability as obvious, possible, or challenging. The risk probability numbers (RPNs) were calculated and the steps in the process with the highest RPNs were flagged for further discussion. The FMEA team completed their process map and analysis primarily in 4 meetings. The process map began with acquisition of the patients CT simulation scan and ended with physician approval of final volumes for treatment planning. We identified 17 process steps and 72 possible failure modes, of which 26 were associated with the new workflow. Eighteen failure modes had an RPN greater than 30 (highest risk score in at least one category) and were flagged to assess mitigation strategies. Five were unique to the new AI GTV workflow and mitigation strategies will be designed prior to clinical use. Those involved risks related to inaccurate AI GTV contours, false positives, and an incomplete review stemming from over-reliance by team members on AI. AI is increasingly being employed at every step of radiotherapy to automate and streamline processes. The FMEA analysis resulted in the identification of the riskiest parts of using AI GTV autosegmentation. This can be an effective tool in the development of checks to ensure that GTV autosegmentation methods can be safely introduced in support of patient care.

Challenges experienced during pharmacy automation and robotics implementation in JCI accredited hospital in the Arabian Gulf area: FMEA analysis-qualitative approach

BackgroundPharmacy automation and robotics implementation are essential aspects of healthcare facilities. It streamlines the medication dispensing process and significantly reduces medication errors. However, implementing automation and robotics in pharmacies comes with its challenges. We aim to detect and rectify potential dangers in the pharmacy workflow by utilizing the Failure Mode and Effects Analysis (FMEA) methodology; this is expected to augment performance and increase profitability. Materials and methodsIn this study, we conducted an FMEA analysis using a qualitative approach to identify the challenges experienced during pharmacy automation and robotics implementation in a Joint Commission International(JCI) accredited hospital in the Arabian Gulf area. The pharmacy processes and procedures were mapped in a Flow chart to visualize the pharmacy workflow, including highlighting the risks that were found. Then these risks were arranged as Potential failure modes and added to the table as 9 main points for each RPNs were calculated, and then the 9 points were prioritized for the action plans. ResultsVia applying traditional Risk Priority Number (RPN) FMEA, the Pharmacy board identified the process stages marked risky failure modes through several FMEAs, calculating the total RPNs at the implementation phase. It revealed several challenges, including staff training, technical issues, and inadequate communication. Furthermore, the study resulted in corrective and intervention steps. ConclusionPharmacy automation and robotics implementation is a complex process that requires proper planning, preparation, and execution. The FMEA approach effectively identifies potential problems and evaluates their impact on the pharmacy system. Nine major failure modes appeared to be risky stages with high RPN scores. Therefore, multiple interventions were done during the implementation to enhance the knowledge of challenges faced during the implementation of the automation process and solve it. Future studies should address the identified challenges and develop strategies to mitigate them.

A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems

Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure.

Risk prioritization using a modified FMEA analysis in industry 4.0

To assess the reliability of production systems, a Failure Mode and Effects Analysis (FMEA) is usually performed to determine how reliable a system is in its entirety. In order to ensure smooth production operations and timely delivery of orders, it is necessary to ensure continuity of operations without failure and interruption. Industry 4.0 (I4.0) can provide so many benefits, but it also introduces new challenges because of its technical components such as sensors and actuators. In this paper, we propose an FMEA model and structure quantitative risk assessment metrics for a yogurt-filling system in an I4.0 environment. We use the risk priority number (RPN), which is usually a product of three factors: severity (S), occurrence (O), and detection (D) ratings. In this study, we use also a fourth factor, namely, dependency (D2). We compare RPN scores for traditional components and I4.0 components. The application method of the FMEA model was presented in detail, and its practicality was verified and demonstrated. To get valid results, a team of experts was utilized. Then Pareto method was used together with the FEMA approach to prioritize the risks that need more attention. After that, risk prevention steps were introduced. Results showed that four components are the most important ones. Results also showed that some of the components of I4.0, such as proximity sensors and color detection sensors were found with the largest priority. A better performance was achieved following the enhancements suggested by the experts' team. A reduction in RPN of 55% has been achieved by implementing the upgrades, indicating an increase in system reliability.

ANALISIS KUALITAS PRODUK PENYAMAKAN KULIT DENGAN METODE SQC dan FMEA

PT Adi Satria Abadi is a company engaged in tanning. The problem faced by the company is the presence of defective leather during the goat and sheep tanning production process. From these problems, the purpose of this study was to identify the level of product defects, analyze the factors that cause defects, and make recommendations for improvements to improve the quality of goat and sheep tanning products. Data processing in this study uses the SQC (Statistical Quality Control) and FMEA (Failure Mode and Effect Analysis) methods. Based on the identification results, it was found that there were 1,507 holes for holes, 1,066 for thick defects, 1,061 for louse defects, and 1,086 for size defects. The results of the SQC analysis still have uncontrolled data based on the control chart which is on size defects with a total of 1,086 defects, while for hollow defects, too thick defects, and tick defects are under control. The results of the FMEA analysis show that the order of completion with the first priority is spotting bugs with an RPN value of 36. The suggested improvements to the company are by directly selecting raw materials and supervising employees.
 
 Keywords: Quality, Product Defects, Seven Tools, SQC, FMEA.

Risk management in humanitarian supply chain based on FMEA and grey relational analysis

The COVID-19 pandemic has disrupted the humanitarian supply chain management (HSCM) necessary for delivering emergency items during the disaster. The combined effects of climate change and the pandemic uncover the vulnerabilities of humanitarian supply chain operations and highlight the importance of risk management. This study aimed to identify priority risk factors and proposed mitigating risk strategies of a local government that is at the forefront of relief operations. It used Grey Relational Analysis (GRA) method to validate the Failure Mode and Effect Analysis (FMEA) approach in identifying priority issues relating to the supply chain risks. This paper reveals that the results of FMEA and GRA are almost similar.

Analysis of Quality Control Using the Failure Mode And Effect Analysis (FMEA) Method in the Welding Process in the Feed Drum Project

PT. Xyz is a steel fabrication company located in Gresik. One of their projects undertaken by PT. XYZ is a Feed drum Project, the welding process is the most important stage in this project. At the time of product testing or testing there were still many welding points that were defective so that the product did not pass the trial, the trial was carried out by hydrotest where this test was carried out with water as the test medium to determine the strength of a material and find out whether there was a leak or not and as well as a visual check. The types of defects found in the feed drum project are spatter, overlap, porosity, undercut, incomplete fusion, slag, bad String Bead (Weaving Fault), start stop, pin hole. The research uses the FMEA (Failure Mode Effects and Analysis) the method which aims to identify and analyze the occurrence of defects. In the results, the highest RPN value is found in the Incomplate Fusion type of defect with a value of 384 so that it becomes a priority in carrying out control and repair .

Perbaikan Kualitas Produksi Roti Tawar Dengan Metode Failure Mode and Effect Analysis (FMEA) dan SQC di Tefa Bakery Politeknik Negeri Jember

One of the food industry markets that are currently in tight competition is the bread industry. Teaching Factory (Tefa) Bakery Politeknik Negeri Jember develops white bread products and always strives to maintain product quality, but there is still data in the field that shows product defects in bread which cause the product to be unfit for sale. Product defects that occur during the production process are quite large, namely 27%. This study aims to Politeknik Negeri Jember, especially in the production process of bread. The research methods include research preparation (observation, and literature review), problem identification, data collection and data analysis, data analysis using the Failure Mode and Effects Analysis (FMEA) method. The results of the FMEA analysis of the failure mode "Too long during the fermentation process in the proofing machine" has the highest RPN value of 64, then the failure mode "Unclean equipment" has the second highest RPN value of 34. Recommendations for improvement for the failure mode "Too long during processing fermentation in the proofing machine" means that the proofing machine is given an alarm so that when the dough enters the machine and performs fermentation, the workers immediately set an alarm and wait for the alarm to sound and the recommendation for improvement for the failure mode "Unclean equipment" is to make a more regular schedule for cleaning the pan.

Development of Maintenance Management Strategy Based on Reliability Centered Maintenance for Marginal Oilfield Production Facilities

The present work adopted Reliability Centered Maintenance (RCM) methodology to evaluate marginal oilfield Early Production Facility (EPF) system to properly understand its functional failures and to develop an efficient maintenance strategy for the system. The outcome of the RCM conducted for a typical EPF within the Niger Delta zone of Nigeria provides an indication of equipment whose failure can significantly affect operations at the production facility. These include the steam generation unit and the wellhead choke assembly, using a risk-based failure Criticality Analysis. Failure Mode and Effect Analysis (FMEA) was conducted for the identified critical equipment on a component basis. Each component of the equipment was analyzed to identify the failure modes, causes and the effect of the failure. The outcome of the FMEA analysis aided the development of a robust maintenance management strategy, which is based on an optimized mix of corrective, preventive and condition-based monitoring maintenance for the marginal oilfield EPF.

Integration of FMEA method and overall equipment effectiveness to increase effectiveness of TS analyzer machine

Total Sulfur Analyzer (TS Analyzer) is one of the machines in the laboratory to test the total sulfur content contained in coal samples. Technically, specifications and use are regulated by international standards. In 2020, the downtime of these tools was very high, causing the productivity of surveyors to decrease. Low productivity will have an impact on the company's losses. The productivity of this equipment and machines is crucial to maintaining the company's success in its business processes. This study aims to measure the effectiveness of the operational performance of the TS Analyzer machine in the laboratory, analyze the causative factors and make improvements. This research uses the integration method of Failure Mode And Effect Analysis (FMEA) and Overall Equipment Effectiveness (OEE). Based on the analysis of the causes of the problem, the low OEE value is caused by the high breakdown loss. Based on the FMEA analysis, it was found that the operator was wrong in operating the machine with RPN 343, electrical instability with RPN 343, unstable temperature with RPN 343 and improper thermocouple installation. The results showed that the OEE value of the TS Analyzer machine has increased after the improvement.

Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model

In the development of safety-critical systems, it is important to perform failure modes and effects analysis (FMEA) to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an automatic FMEA tool that integrates the model-based design (MBD), FMEA, and simulated fault injection techniques in a single environment. The automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool: First, the automatic FMEA tool automatically generates FMEA reports, unlike the traditional spreadsheet-based FMEA tools. Second, the automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the automatic FMEA were compared to those of the legacy FMEA.

Electron flash-RT program in clinical setting for human translation.

e13596 Background: A FLASH-RT program was established at Dartmouth-Hitchcock Medical Center in minimally-modified clinical setting by joint efforts of biomedical engineering, radiation oncology, radiation biology and medical physics teams. Various projects on dosimetry, chemical sensing, molecular profiling, software/hardware development, and translational studies have been conducted. The aim is to share logistical considerations and experience on running a FLASH-RT program to support institution-wide academic activities with an ultimate goal of treating human patients with FLASH-RT in 2022. Methods: A linac was converted in the clinical setting by qualified engineers to deliver an ultra-high dose rate (UHDR) electron beam. Routine safety and dosimetry checks were done by physicists for every reversible conversion. Long-term record-keeping and retrospective surveys were carried out to demonstrate the feasibility, safety, stability and accuracy of this dual-purpose (FLASH and conventional RT) approach. Comprehensive failure mode and effects analysis (FMEA) has been completed to systematically evaluate safety related considerations. A treatment planning system (TPS) has been developed in Varian Eclipse to facilitated comparative studies. The FLASH-capable linac has been utilized as shared resource to support institution-wide academic activities as well as normal clinical treatments. Results: With its safety (no accident or FLASH-related malfunction), flexibility (> 100 conversions in 2 years), reliability (̃6000 hours in flash mode and ̃5x105 Gy accumulative dose delivered at isocenter) and accuracy (̃5% conversion-to-conversion variations) demonstrated by commissioning, long-term user experience and comprehensive FMEA analysis, the FLASH-RT platform has been actively utilized for researches in six major categories 1) FLASH beam dosimetry; 2) real-time beam delivery monitoring and control; 3) oximetry and chemical sensing; 4) preclinical/translational small/large animal treatment with tumor control and normal tissue complication endpoints, 5) treatment plan and delivery optimization; 6) the design of phase I/II trials. Key findings in each category will be reported. Conclusions: A FLASH-RT program in clinical setting is established at Dartmouth with joint efforts, promoting collaborative projects to advance FLASH-RT to clinical treatment. The system has been reliably utilized for over two years for mechanistic as well as translational studies and support a phase I/II trial treating cutaneous lymphoma with eFLASH-RT.

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

REORGANIZATION OF THE ASSEMBLY STATION IN THE PRODUCTION PROCESS OF THE SLIDING FLOOR FOR RELOADING RAMPS IN THE CONTEXT OF IMPROVING THE QUALITY OF THE FINISHED PRODUCT

The paper presents the definitions of the term quality quoted in the literature on the subject. FMEA (Failure Mode and Effects Analysis) methodology was presented. A reloading ramp was characterized as the subject of the study. A cause-and-effect analysis of non-compliance in the finished product was performed using the Pareto-Lorenz diagram, Ishikawa diagram and brainstorming. The FMEA analysis of the assembly table indicated the elements of the workstation causing the most frequently occurring non-conformities. Based on the obtained results, corrective actions were proposed to reorganize the assembly station. The implemented activities made it possible to reduce the critical RPN coefficients for the elements of the assembly table and to shorten the time necessary to make one piece of the finished product.

Process improvement design at PT URW using failure mode and effect analysis

PT URW is a company engaged in the textile sector that processes yarn into semi-finished fabrics. Such is the case that PT URW has a problem with its waste materials. To address this, the researcher has implemented the method of failure mode and effect analysis (FMEA). In data collection and processing, the researcher defined the problem, collected data, took measurements using the critical waste method, and interpreted the data into the Pareto Chart. The area that had the greatest value was continued to the analysis stages of critical waste analysis, FMEA analysis, and alternative solutions. The research was concluded by providing corrective solutions based on the potential causes that had been found in the previous stage and by validating the formed planned improvements.

Implementasi Metode Overall Equipment Effectiveness Berbasis Six Big Losses Guna Mengevaluasi Efektivitas Mesin Packing Semen

Integrated maintenance management is an important factor in maintaining the stability of machine operations in manufacturing companies. PT AAAA Factory Gresik is a cement manufacturing company, where the Section Division of Gresik Finish Mill Packer Operation has a problem of downtime cement packing machine 40 kg and causes production delay. This study aims to evaluate the effectiveness of the 40kg cement packing machine as well as to calculate losses due to unproductiveness of the packing machine. Overall Equipment Effectiveness (OEE), Six Big Losses, and Failure Mode and Effect Analysis (FMEA) were used as research method. This study found that OEE value for January-December 2019 of 41.25% are considered as lower percentage in compare with the ideal value of international standards which is 85%. Moreover, the largest average losses are found, namely reduce speed losses, idle and stoppages minor losses and defects in process with percentage in following order, 4.49%, 4.49% and 2.76%.Result from FMEA analysis have been identified 3 highest Risk Priority Number (RPN), namely transportation with an RPN value of 294, the second RPN value of 288 is a blockage in the packing machine, finally the engine button is jammed with an RPN value of 252. The proposed improvement scenario is the start of the engine operating operators checking and cleaning machines regularly and the marketing division expands the sales market.