Corrective Action System Research Articles

Root cause prediction for failures in semiconductor industry, a genetic algorithm–machine learning approach

Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.

Reliability Modeling and Analysis of a Diesel Engine Design Phase Based on 4F Integration Technology

As one of the most important components within a vehicle, diesel engines have high requirements for reliability due to the harsh operating environments. However, previous studies have mainly focused on the reliability assessment of diesel engines, while less research has been conducted on the modeling of the diesel engine reliability analysis and its management. For this reason, this paper proposes a comprehensive method for reliability analysis and its management based on the use of 4F integration technology in the early stages of diesel engine design. First of all, an expert group used FEMCA (failure mode, effects and criticality analysis) and FHA (functional hazard analysis) to find the most harmful level of fault mode. At the same time, a new method for the repair of dynamic fault trees to find the weak links at the component level was developed. Finally, a FRACAS (fracture report analysis and corrective action system) was used during the above analysis process. By applying this method to the reliability assessment of a diesel engine in the design stage, the problems of failure information feedback and the reuse of failure information in the actual reliability assessment can be solved.

Critical failure factors for continuous improvement methodologies in the Irish MedTech industry

PurposeThe main objective of this study is to investigate what are the critical success factors that exist for continuous improvement (CI) methodology deployment in the Irish medical technology (MedTech) industry. The research will, in particular, seek to establish if the highly regulated nature of the global MedTech industry is an additional critical failure factor (CFF) for the deployment of CI methodology. The study involves the analysis of the benefits, challenges, CFFs and tools most utilised for the application to the deployment of CI methodologies in the Irish medical device (MD) industry.Design/methodology/approachA quantitative survey was utilised in this study. The main participants were made up of senior quality professionals working in operational excellence, quality consultants, quality directors, quality engineers, quality managers and quality supervisors working in both manufacturing and service sectors from Irish MD companies. A total of 94 participants from the Irish MedTech industry responded to the survey.FindingsThe main finding of this study is that 42% of participants perceived that a highly regulated environment was a CFF to CI, whilst 79% of respondents utilised Lean Six Sigma in their organisations, and productivity and financial factors were found to be the highest reasons for CI deployment amongst the Irish MedTech industry. The top CFFs highlighted for CI in regulated industries were fear of extra validation activity, compliance versus quality culture and a regulatory culture of being “safe”. Another relevant finding presented in this paper is that just over 48% of participants felt that CI tools are very strongly integrated into the industries quality management systems (QMSs) such as the corrective and preventative action system, non-conformance and audit systems.Research limitations/implicationsAll data collected in the survey came from professionals working for Irish indigenous and multinational MedTech companies. It is important to highlight that n = 94 is a low sample size, which is enough for a preliminary survey but reinforcing the limitation in terms of generalisation of the results. A further study on a wider European and global scale as well as a comparison with the highly regulated pharma industry would be informative.Originality/valueThe authors understand that this is the very first research focussed on the CFFs for CI in the MedTech/MD manufacturing industry with a specific focus on the highly regulated nature of the industry as a potential CFF. The results of this study represent an important first step towards a full understanding of the applicability and use of CI in the medical-device-manufacturing industries on a global scale.

Barriers and Enablers for Continuous Improvement Methodologies within the Irish Pharmaceutical Industry

This study aims to investigate the barriers that exist when implementing continuous improvement methodologies, such as Lean Six Sigma (LSS), within the Irish Pharma industry. The main finding of this study is that 45% of participants perceived that a highly regulated environment could be a barrier to continuous improvement implementation, while 97% of respondents utilised Continuous improvement (CI) methods, such as Lean, Six Sigma, and LSS, within their organisations. While the International Conference of Harmonisation integrates CI into its Pharmaceutical Quality Systems (PQS) regulations, the highest motivation for CI implementation amongst the Irish Pharma industry is to improve Productivity and Quality. The main obstacles highlighted for CI implementation in Pharma attributed to stringent regulatory regimes were fear of extra validation activity, a compliance versus quality culture, and a regulatory culture of being “safe”. Another relevant finding presented in this paper is that participants CI LSS tools are very strongly integrated into the pharma industries corrective and preventative action system, deviations, and internal audit systems. Limitations of the research are that all the data collected in the survey came from professionals working for multinational Pharmaceutical companies based in Ireland. The authors understand that this is the first research focused on the barriers and status of CI initiatives in the pharmaceutical industry. The results of this study represent an important step towards understanding the enablers and obstacles for the use of continuous improvement methodologies in pharmaceutical manufacturing industries on a global scale.

Development and Application of Computerized Risk Registry and Management Tool Based on FMEA and FRACAS for Total Testing Process.

Background and Objectives: Risk management is considered an integral part of laboratory medicine to assure laboratory quality and patient safety. However, the concept of risk management is philosophical, so actually performing risk management in a clinical laboratory can be challenging. Therefore, we would like to develop a sustainable, practical system for continuous total laboratory risk management. Materials and Methods: This study was composed of two phases: the development phase in 2019 and the application phase in 2020. A concept flow diagram for the computerized risk registry and management tool (RRMT) was designed using the failure mode and effects analysis (FMEA) and the failure reporting, analysis, and corrective action system (FRACAS) methods. The failure stage was divided into six according to the testing sequence. We applied laboratory errors to this system over one year in 2020. The risk priority number (RPN) score was calculated by multiplying the severity of the failure mode, frequency (or probability) of occurrence, and detection difficulty. Results: 103 cases were reported to RRMT during one year. Among them, 32 cases (31.1%) were summarized using the FMEA method, and the remaining 71 cases (68.9%) were evaluated using the FRACAS method. There was no failure in the patient registration phase. Chemistry units accounted for the highest proportion of failure with 18 cases (17.5%), while urine test units accounted for the lowest portion of failure with two cases (1.9%). Conclusion: We developed and applied a practical computerized risk-management tool based on FMEA and FRACAS methods for the entire testing process. RRMT was useful to detect, evaluate, and report failures. This system might be a great example of a risk management system optimized for clinical laboratories.

Early failure active elimination system research for electromechanical product based on meta‐action

AbstractFrequent early failures have been the key factor restricting the reliability improvement of electromechanical product, and also reducing its market competitiveness. Most of the current early failure elimination methods are passive elimination technologies after a failure. Their purpose is to quickly restore the production capacity of products, which is mainly used to improve the reliability of the products, and cannot fundamentally improve the quality of the manufactured product. To address those problems, this paper first analyzed the causes of early failures in the production stage of electromechanical product, and studied the corresponding early failure elimination mechanism; second, according to the FRACAS (Failure Report Analysis and Corrective Action System), an early failure elimination method for electromechanical product was proposed based on the meta‐action unit; finally, in order to ensure the smooth implementation of early failure elimination measures, a set of early failure active elimination guarantee management system was established. In addition, this paper also introduced the specific implementation process of the proposed method. The proposed method is applied to engineering, and the results verify its correctness. The proposed method provides a theoretical basis and technical support for the elimination of electromechanical product early failure.

Risk assessment of the total testing process based on quality indicators with the Sigma metrics.

Background Evidence-based evaluation of laboratory performances including pre-analytical, analytical and post-analytical stages of the total testing process (TTP) is crucial to ensure patients receiving safe, efficient and effective care. To conduct risk assessment, quality management tools such as Failure Mode and Effect Analysis (FMEA) and the Failure Reporting and Corrective Action System (FRACAS) were constantly used for proactive or reactive analysis, respectively. However, FMEA and FRACAS faced big challenges in determining the scoring scales and failure prioritization in the assessment of real-world cases. Here, we developed a novel strategy, by incorporating Sigma metrics into risk assessment based on quality indicators (QIs) data, to provide a more objective assessment of risks in TTP. Methods QI data was collected for 1 year and FRACAS was applied to produce the risk rating based on three variables: (1) Sigma metrics for the frequency of defects; (2) possible consequence; (3) detection method. The risk priority number (RPN) of each QI was calculated by a 5-point scale score, where a value of RPN > 50 was rated as high-risk. Results The RPNs of two QIs in post-analytical phase (TAT of Stat biochemistry analyte and Timely critical values notification) were above 50 which required rigorous monitoring and corrective actions to eliminate the high risks. Nine QIs (RPNs between 25 and 50) required further investigation and monitoring. After 3 months of corrective action the two identified high-risk processes were successfully reduced. Conclusions The strategy can be implemented to reduce identified risk and assuring patient safety.

Errors of Clinical Laboratory and Its Impact on Patient Safety

The aim of this study is to analyse the potential risks of medical laboratory activities in all processes: Strategic, operational (pre-preanalytical, preanalytical, analytical, postanalytical and post-postanalytical) and support. Also, we value the impact of these risks in the patient safety. The methodology used in this study to identify and estimate the possible failure modes was the Failure Model and Effects Analysis (FMEA). The real failures then were registered in the same processes according to the methodology Failure Reporting Analysis and Corrective Action System (FRACAS). Moreover, it used the basis of available information of the laboratory quality system. The Risk Priority Number (RPN) with FMEA and FRACAS was calculated for every laboratory processes and it was made a comparative of the results obtained with both methodologies. Based on these results, we made the risk map in medical laboratory. These results allowed us identifying critical points in all laboratory processes and prioritize the control of these points. Furthermore, it helped to select preventive or corrective action that should be incorporated in the laboratory improvement planning and risk management.

Risk Management in Clinical Laboratory: from Theory to Practice

Abstract Clinical laboratory tests ensure approximately 70% of the medical decisions, so that the time until the release of the results and its accuracy are critical for the diagnosis and the efficiency of the treatment [1]. Risk management involves both the anticipation of what could happen erroneous and the assessment of errors’ frequency as well as the consequences or the severity of the effects caused by it, and finally to decide what can be done in order to reduce the risk to an acceptable clinical level. For this reason, organizations should not see the risk management as a compliance issue, but as an integral part of the decision-making process. EP23-A is a guideline of CLSI that introduces the risk management principles in the clinical laboratory and encourages the laboratories to develop plans of risk management which are addressed to the risks of each laboratory. EP18-A2 proposes 2 techniques for identifying and controlling the errors in the laboratory: FMEA (Failure Mode and Effects Analysis) and FRACAS (Failure Reporting, Analysis and Corrective Action System). The European Committee of Experts and Management of Safety and Quality in Health Care proposed to use the quality indicators to identify the critical stages of each process, thus being possible to assess continuously the medical processes with the aim of identifying the errors when they occur. This review summarizes the principles of the risk management in the clinical laboratory, thus it can achieve its aims to report valid, accurate and reliable test results

Banking and Insurance Recapitalization During the 2007-2009 Credit Crisis

This paper explores the feedback loop between the intensity of financial intermediary recapitalization and a number of measures of credit and liquidity risk. We cover the entire 2007–2009 subprime crisis with a sample that includes the 97 largest banks and insurance companies on both sides of the Atlantic. We model the intensity of recapitalization by implementing a scale measure inspired by the Corrective Action system used by U.S. banking regulators. The proposed ordering starts with inaction, followed by dividend cuts, to issues of new equity, to bailouts. Our risk measures include toxic asset exposures, credit default swap spreads, and Merton-style probabilities of default. We show that there is a monotone relationship between the intensity of risk and the intensity of recapitalization across all firms and jurisdictions in the sample. We are able to simplify the large heterogeneity of regulations and business models across sectors and countries within a fairly simple and general classification scheme. This scheme distinguishes between individual bailouts, targeted at bankruptcy prevention, and more nuanced collective bailouts, which were more related to the prevention of systemic risk arising from potentially solvent institutions.

우리나라 적기시정조치제도의 현황 및 개선방안

The prompt corrective action (PCA) system was introduced for the first time in Denmark in 1994 when CBSBA was enacted. The USA also introduced the System in 1991 in order to deal with a lot of troubled depository institutions by the Federal Deposit Insurance Corporation Improvement Act (FDICIA) of 1991. The PCA system of USA has been a successful and desirable model for many other countries. Korea introduced the PCA system in 1992 by the Monetary and Financial Operation Committee in the Bank of Korea as a prudential regulatory measure for the banking institutions. Just before the Financial and Currency Crisis in 1997, the System was implemented very well for the financial institutions such as banks, securities companies, and insurance companies. During the Crisis, the PCA system carried out the important role of restructuring measures for the many troubled financial institutions. Afterwards, the Korean PCA system has been reformed and improved gradually. Recently the System has played an important role in restructuring the troubled mutual savings banks in Korea. However, there are still some areas to be reformed and improved in order that the Korean PCA system works more properly and efficiently. This paper suggests three points. First, the purpose of the Korean PCA includes to minimize the possibility of putting the Public Fund into troubled financial institutions. Second, the forbearance (suspension) of the PCA should be abolished. Third, the power of the Financial Services Commission on the PCA should be checked by the Korea Deposit Insurance Corporation which manages deposit insurance funds in Korea.

Reliability growth planning for electronics design under distributed manufacturing paradigm

This paper proposes an integrated reliability management programme for faster time-to-market electronics products which are designed under the distributed manufacturing chain. Our objective is to resolve the reliability issue across the product life cycle from design, manufacturing, integration, and field deployment. The idea is to bridge the reliability information gap between the manufacturer and the customers through an effective failure tracking and corrective action system. Engineers can leverage the field information to carry out reliability analysis, implement corrective actions, and monitor the reliability growth rate. The system consists of four modules: 1) stochastic-based reliability prediction tools; 2) real-time failure mode rate charts; 3) corrective action effectiveness functions; 4) reliability monitoring mechanism. The proposed reliability management system will be illustrated through the design and deployment of automatic test equipment. The study shows that reliability growth for capital-intensive equipment can be approached from the entire life cycle. This is particularly important for new technologies or products developed in the faster time-to-market environment.

THE RELIABILITY OPERATING MODEL OF AIRLINE

The reliability operating model of airline is considered. The Reliability control program of airline is proposed. The main systems of Reliability control program (data collection system, data analysis system, performance standards, corrective action system, presentation of data) are considered. The schema of aircraft, components and technical dispatch reliability definition process is proposed. The graphics and tables of aircraft and compo-nents reliability are shown

Process-Oriented Development of Failure Reporting, Analysis, and Corrective Action System

Although failure reporting, analysis, and corrective action system (FRACAS) has two management perspectives, its tasks and related information, the previous researches and applications mainly have focused on the data management. This study is to develop a process-oriented FRACAS which supports the operation of the failure-related activities. The development procedures are (1) to define the reporting and analysis tasks, (2) to define the information to be used at each task, and (3) to design a computerized business process model and set the attributes such as durations, rules, and document types. This computerized FRACAS process can be activated in a business process management system (BPMS) which employs the enactment functions, deliver tasks to the proper workers, provide the necessary information, and alarm the abnormal status of the tasks (delay, incorrect delivery, cancellation). Through implementing the prototype system, improvements are found for automation of the tasks, prevention of disoperation, and real-time activity monitoring.

Cross-border banking and financial stability in the EU

This paper examines the implications that alternative regulatory structures may have for resolving failed banking institutions. Emphasis on the European Union (EU), which is both economically and financially large and has several features relating to cross-border banking in the form of direct investment that may heighten the problems we consider. To ensure the efficient resolution of bank failures with minimum, if any, credit and liquidity losses a four step program should be followed. This includes prompt legal closure of institutions before they become economically insolvent, prompt identification of claims and assignment of losses, prompt reopening of failed institutions, and prompt re-capitalizing and re-privatization of failed institutions. These policies together with a prompt corrective action system could be voluntarily adopted through the use of deposit insurance premium discounts as an incentive.

Dynamic rather than static performance measures are needed to improve patient safety

Selected performance measures have been proposed to reduce clinical laboratory errors as a means of reducing medical errors. Yet, whereas this static list of measures are all quality-related, they are not all patient-safety related. For example, the specimen rejection rate is actually measuring a laboratory's ability to detect errors, which is a good thing. Moreover, a static list does not account for new errors or an improved error rate for items on the list. A dynamic list of patient-safety errors, informed by a FRACAS (Failure Review and Corrective Action System) overcomes these objections, since the list of performance measures is periodically refreshed by error data from the clinical laboratory. While new to clinical laboratories, FRACAS has been successfully used in the medical device industry.

Effective Training Yields Learning and Growth

Effective Training Yields Learning and Growth

Reliability Engineering Practice in the Light Duty Dodge Ram Truck Chassis Program

AbstractIn this paper, we demonstrate how we set up and executed an integrated reliability engineering process with the engineering team in the Light Duty (LD) Dodge Ram (DR) Truck chassis program. Organizationally, the LD DR chassis team consists of core engineering groups and supporting representatives from other related disciplines. The reliability engineer is a member of the team and he is the reliability advocate and leader. The integrated reliability engineering process was customized and implemented in the LD DR chassis program. Many of the tools developed by the company corporate quality office, such as design failure modes and effect analysis (DFMEA), design verification plan and reporting (DVP&R), and finite element analysis (FEA), were used in supporting the reliability engineering process. An array of technical enablers such as Test Matrix and ReliUp were also developed for supporting the implementation of the reliability engineering process. In the execution, the reliability engineer led the engineering team to set up reliability targets, develop reliability work plans, facilitate up‐front design analysis, review and integrate reliability test planning. The reliability engineer also set up the failure reporting, analysis and corrective action system (FRACAS) and managed reliability growth with the team. From the implementation, we have learned several things: (1) an integrated engineering team is crucial in order to develop a product better, quicker and cheaper; (2) a good team leader is the key to product reliability; (3) a capable reliability engineer is the catalyst to a reliability engineering process; (4) the best culture in which to achieve reliability is the delicate balance between ‘inside‐out’ and ‘outside‐in’; and (5) achieving reliability is far more important than measuring reliability. Copyright © 2004 John Wiley & Sons, Ltd.

Practical Reliability Assurance Through Modelling and DRACAS

ABSTRACTIn recent years the subject of reliability in its truest sense (numerical evaluation) has been under considerable criticism by some Reliability and Maintainability (R&M) commentators. This is considered to be due in part to poor application by many practitioners and a lack of understanding of the benefits of many of the reliability engineering techniques available. This is particularly true of the understanding of the value of R&M modelling by managers and decision makers within the UK Ministry of Defence (MoD). The author considers that it is important that the R&M community re-establish the reasons for and benefits of R&M modelling and “the numbers game” as it is sometimes called by its detractors. This paper is particularly focused on the recent successful application of a rigorous mechanism to assist contractual agreement of R&M (or availability) performance by combination of an availability Model with an effective Data Reporting, Analysis and Corrective Action System (DRACAS).

Using a learning curve approach to reduce laboratory errors

Hospital laboratories have error rates that are too high and in some cases may be responsible for adverse patient treatment. This paper introduces reliability growth management (RGM), which is based on learning curve theory, as a method to improve laboratory error rates. RGM is widely used in the defense and automotive industry to solve problems when resources are limited and knowledge about the product and/or process is incomplete. An example of RGM, which was used to improve the reliability of instrument assay systems in the medical diagnostics industry is presented. RGM is a closed-loop process that entails creating a goal and event model, classifying events with failure review and corrective action system (FRACAS), tracking progress and predicting completion with Duane analysis. Results achieved by RGM were far better than those obtained by previously used methods. RGM techniques can be transferred to hospital laboratories to reduce laboratory error rates. The advantages of RGM compared to other quality initiatives such as ISO 9000 and Six Sigma are discussed.