Relevant bibliographies by topics / Root Cause Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Root Cause Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 September 2024

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Journal articles on the topic "Root Cause Analysis"

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Contreras, Joemart. "Root Cause Analysis." Journal of Clinical Engineering 46, no. 2 (April 2021): 85–88. http://dx.doi.org/10.1097/jce.0000000000000456.

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Paulsen, Megan. "Root Cause Analysis." JAMA 325, no. 3 (January 19, 2021): 225. http://dx.doi.org/10.1001/jama.2020.24911.

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Burgess, David. "Root Cause Analysis." EDFA Technical Articles 22, no. 1 (February 1, 2020): 55–56. http://dx.doi.org/10.31399/asm.edfa.2020-1.p055.

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Abstract This columnn explores the idea that insights into the root cause of increasingly complex failures may be hidden in unanswered questions from past analyses, indicating that there might be more value in previous files than once thought.
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Ziegel, Eric R., P. Wilson, L. Dell, and G. Anderson. "Root Cause Analysis." Technometrics 36, no. 2 (May 1994): 230. http://dx.doi.org/10.2307/1270255.

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Dankovic, Daniel D. "Root Cause Analysis." Technometrics 43, no. 3 (August 2001): 370–71. http://dx.doi.org/10.1198/tech.2001.s623.

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Ewen, Brenda M., and Gale Bucher. "Root Cause Analysis." Home Healthcare Nurse 31, no. 8 (September 2013): 435–43. http://dx.doi.org/10.1097/nhh.0b013e3182a1dc32.

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&NA;. "Root Cause Analysis." Home Healthcare Nurse 31, no. 8 (September 2013): 443–45. http://dx.doi.org/10.1097/nhh.0b013e3182a826b9.

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McCarty, Jennifer L., Ryan M. David, Shelly Y. Lensing, Rohan S. Samant, Manoj Kumar, Rudy L. Van Hemert, Edgardo J. C. Angtuaco, and Ryan T. Fitzgerald. "Root Cause Analysis." Journal of Computer Assisted Tomography 41, no. 3 (2017): 484–88. http://dx.doi.org/10.1097/rct.0000000000000522.

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Canzonetti, Eileen M. "Root Cause Analysis." Gastroenterology Nursing 31, no. 2 (March 2008): 159. http://dx.doi.org/10.1097/01.sga.0000316549.60899.14.

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Stecker, Michael S. "Root Cause Analysis." Journal of Vascular and Interventional Radiology 18, no. 1 (January 2007): 5–8. http://dx.doi.org/10.1016/j.jvir.2006.10.004.

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Dissertations / Theses on the topic "Root Cause Analysis"

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Melo, Daniel Araújo. "ARCA - Alerts root cause analysis framework." Universidade Federal de Pernambuco, 2014. https://repositorio.ufpe.br/handle/123456789/13946.

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Submitted by Luiza Maria Pereira de Oliveira (luiza.oliveira@ufpe.br) on 2015-05-15T14:58:14Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO Daniel Araújo Melo.pdf: 2348702 bytes, checksum: cdf9ac0421311267960355f9d6ca4479 (MD5)
Made available in DSpace on 2015-05-15T14:58:14Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO Daniel Araújo Melo.pdf: 2348702 bytes, checksum: cdf9ac0421311267960355f9d6ca4479 (MD5) Previous issue date: 2014-09-08
Modern virtual plagues, or malwares, have focused on internal host infection and em-ploy evasive techniques to conceal itself from antivirus systems and users. Traditional network security mechanisms, such as Firewalls, IDS (Intrusion Detection Systems) and Antivirus Systems, have lost efficiency when fighting malware propagation. Recent researches present alternatives to detect malicious traffic and malware propagation through traffic analysis, however, the presented results are based on experiments with biased artificial traffic or traffic too specific to generalize, do not consider the existence of background traffic related with local network services or demands previous knowledge of networks infrastructure. Specifically don’t consider a well-known intru-sion detection systems problem, the high false positive rate which may be responsible for 99% of total alerts. This dissertation proposes a framework (ARCA – Alerts Root Cause Analysis) capable of guide a security engineer, or system administrator, to iden-tify alerts root causes, malicious or not, and allow the identification of malicious traffic and false positives. Moreover, describes modern malwares propagation mechanisms, presents methods to detect malwares through analysis of IDS alerts and false positives reduction. ARCA combines an aggregation method based on Relative Uncertainty with Apriori, a frequent itemset mining algorithm. Tests with 2 real datasets show an 88% reduction in the amount of alerts to be analyzed without previous knowledge of network infrastructure.
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AGUIAR, MILENA CABRAL. "ROOT CAUSE ANALYSIS: SURVEY METHODS AND EXEMPLIFATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23437@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Grandes desafios surgiram para as organizações devido ao avanço tecnológico ocorrido nos últimos tempos. Qualidade no produto não é mais um meio para se obter vantagem competitiva, mas sim uma necessidade para as organizações manterem seus clientes. Deste modo, formas para que a qualidade esteja cada vez mais presente nas organizações se fazem necessárias. Neste contexto, os objetivos do presente trabalho são estudar os principais métodos de análise de causa raiz da literatura, apresentando suas etapas, características, peculiaridades, comparação, e exemplificar a aplicação desses métodos. Uma vez conhecidos pelas organizações, a aplicação de tais métodos pode prevenir recorrência de falhas, levando às organizações a um nível superior de qualidade, aumento de produtividade, e consequentemente maior satisfação dos clientes. A pesquisa foi iniciada pelo levantamento e estudo dos métodos apontados em referências acadêmicas, Método dos 5 por quês, Diagrama de Ishikawa, Análise de Barreira de Controle, Gráfico de Fator Causal e Evento (GFCE), Análise de Árvore de Falhas (AAF) e Mapa de Causa Raiz, seguidos da apresentação de um exemplo de falha elaborado no contexto de uma indústria manufatureira metal-mecânica. Cada método foi aplicado ao problema de não conformidade. Os resultados dos métodos foram comparados e em seguida as vantagens e desvantagens dos métodos foram destacadas. Os Métodos 5 por quês, Diagrama de Ishikawa e Análise de Barreira de Controle foram considerados mais adequados para problemas considerados simples em uma organização industrial. Já para problemas considerados complexos, cujas causas raízes não são facilmente identificadas, os métodos GFCE, AAF e Mapa de Causa Raiz foram considerados os mais indicados.
Great challenges emerged for organizations due to technological advance occurred in recent times. Product quality is no longer a means to gain competitive advantage, but a necessity for organizations to keep their customers. Thus, ways in which quality is increasingly present in organizations are necessary to facilitate its implementation. In this context, the objectives of this work are to study the major methods of root cause analysis of the literature, with its stages, features, peculiarities, comparison, and exemplify the application of these methods. Once known by the organizations, the application of such methods can prevent recurrence of failures, leading organizations to a higher level of quality, increased productivity, and thus increased customer satisfaction. This research was initiated by the survey and study of methods aimed at academic references, 5 Whys, Ishikawa Diagram, Control Barrier Analysis, Event and Causal Factor Charting, Fault Tree Analysis and Root Cause Map, followed by the presentation of an example fault developed in the context of a metalworking manufacturing. Each method was applied to the problem of non-compliance. The results of both methods are compared and then the advantages and disadvantages of both methods are highlighted. 5 Whys, Ishikawa Diagram and Control Barrier Analysis were considered appropriated to simple problems in an industrial organization. In complex problems cases, which root causes are not easily identified, Event and Causal Factor Charting, Fault Tree Analysis and Root Cause Map were considered more appropriated.
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Elliott, Grant Stephen. "Improving customer service contact root-cause analysis." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/50095.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2009.
Includes bibliographical references (p. 50).
When a customer calls or e-mails customer service, a customer service agent will diagnose the issue, render a solution, and then wrap-up the call or e-mail. For many customer service departments, this wrap-up process requires the agent to classify the reason the customer contacted customer service. Typically, this classification is done by assigning a code that describes the reason for a contact. Additionally, if a contact requires a concession, the agent will classify the reason the customer requires a concession, and select an appropriate code. These codes are used by the various business teams within the company to identify and correct failures in their processes. Therefore, these codes should drive down to the root cause for a contact or concession to allow for efficient correction. Possessing codes that do not clearly identify the root cause for a contact are of little or no use for the company. Additionally, the codes must be developed in such a way that they can be accurately chosen by either the agent or the customer. Having agents select the wrong code not only obscures the true cause for a contact, but also creates additional work due to the process involved in determining the correct code. This thesis looks at the challenges inherent in developing a list of codes that both provides clear insight into the root cause for customer contacts, and can be accurately selected by the customer service agent or the customer.
by Grant Stephen Elliott.
S.M.
M.B.A.
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Liu, Changlin. "Root Cause Localization for Unreproducible Builds." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1595524817828183.

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Josefsson, Tim. "Root-cause analysis throughmachine learning in the cloud." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-340428.

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It has been predicted that by 2021 there will be 28 billion connected devices and that 80% of global consumer internet traffic will be related to streaming services such as Netflix, Hulu and Youtube. This connectivity will in turn be matched by a cloudinfrastructure that will ensure connectivity and services. With such an increase in infrastructure the need for reliable systems will also rise. One solution to providing reliability in data centres is root-cause analysis where the aim is to identifying the root-cause of a service degradation in order to prevent it or allow for easy localization of the problem.In this report we explore an approach to root-cause-analysis using a machine learning model called self-organizing maps. Self-organizing maps provides data classification, while also providing visualization of the model which is something many machine learning models fail to do. We show that self-organizing maps are a promising solutionto root-cause analysis. Within the report we also compare our approach to another prominent approachs and show that our model preforms favorably. Finally, we touch upon some interesting research topics that we believe can further the field of root-cause analysis
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Medidi, Prasadbabu. "Waste in Lean Software Development : A Root Cause Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4238.

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Context: Removal of wastes is a crucial area in lean software development. It has been found that there was little evidence on root causes of wastes in lean software development. Root causes from the state of practice had not being investigated. Furthermore, relations between wastes were now successfully exposed through root cause identifications process. Objectives: The objective of this study was to perform an in-depth investigation to identify causes which lead to wastes in Lean software development process in the context of medium to large software development. To this end, researcher also identified relationships that exist between wastes. Methods: The researcher conducted Literature review to look for evidence on waste related activities offered in peer-reviewed literature. Furthermore, the author conducted seven semi-structured interviews and used Grounded Theory method for both literature and interview data analysis. Results: The researcher identified three categories of factors of wastes. Namely, Technical, Non-technical and Global software product development. In the technical category, factors relating to different technical aspects to build a product such as required resource issues, solving complexity issues among others were identified. Similarly, factors relating to people knowledge, management issues as well as factors that bothered on communication, coordination and temporal distance were identified as non-technical and global software product development respectively. For all seven kinds of wastes the root causes were identified.
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Ellis, Kathryn. "Improving root cause analysis of bacteriological water quality failures." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/5701/.

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Pereira, Rosangela de Fátima. "A data-driven solution for root cause analysis in cloud computing environments." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-03032017-082237/.

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The failure analysis and resolution in cloud-computing environments are a a highly important issue, being their primary motivation the mitigation of the impact of such failures on applications hosted in these environments. Although there are advances in the case of immediate detection of failures, there is a lack of research in root cause analysis of failures in cloud computing. In this process, failures are tracked to analyze their causal factor. This practice allows cloud operators to act on a more effective process in preventing failures, resulting in the number of recurring failures reduction. Although this practice is commonly performed through human intervention, based on the expertise of professionals, the complexity of cloud-computing environments, coupled with the large volume of data generated from log records generated in these environments and the wide interdependence between system components, has turned manual analysis impractical. Therefore, scalable solutions are needed to automate the root cause analysis process in cloud computing environments, allowing the analysis of large data sets with satisfactory performance. Based on these requirements, this thesis presents a data-driven solution for root cause analysis in cloud-computing environments. The proposed solution includes the required functionalities for the collection, processing and analysis of data, as well as a method based on Bayesian Networks for the automatic identification of root causes. The validation of the proposal is accomplished through a proof of concept using OpenStack, a framework for cloud-computing infrastructure, and Hadoop, a framework for distributed processing of large data volumes. The tests presented satisfactory performance, and the developed model correctly classified the root causes with low rate of false positives.
A análise e reparação de falhas em ambientes de computação em nuvem é uma questão amplamente pesquisada, tendo como principal motivação minimizar o impacto que tais falhas podem causar nas aplicações hospedadas nesses ambientes. Embora exista um avanço na área de detecção imediata de falhas, ainda há percalços para realizar a análise de sua causa raiz. Nesse processo, as falhas são rastreadas a fim de analisar o seu fator causal ou seus fatores causais. Essa prática permite que operadores da nuvem possam atuar de modo mais efetivo na prevenção de falhas, reduzindo-se o número de falhas recorrentes. Embora essa prática seja comumente realizada por meio de intervenção humana, com base no expertise dos profissionais, a complexidade dos ambientes de computação em nuvem, somada ao grande volume de dados oriundos de registros de log gerados nesses ambientes e à ampla inter-dependência entre os componentes do sistema tem tornado a análise manual inviável. Por esse motivo, torna-se necessário soluções que permitam automatizar o processo de análise de causa raiz de uma falha ou conjunto de falhas em ambientes de computação em nuvem, e que sejam escaláveis, viabilizando a análise de grande volume de dados com desempenho satisfatório. Com base em tais necessidades, essa dissertação apresenta uma solução guiada por dados para análise de causa raiz em ambientes de computação em nuvem. A solução proposta contempla as funcionalidades necessárias para a aquisição, processamento e análise de dados no diagnóstico de falhas, bem como um método baseado em Redes Bayesianas para a identificação automática de causas raiz de falhas. A validação da proposta é realizada por meio de uma prova de conceito utilizando o OpenStack, um arcabouço para infraestrutura de computação em nuvem, e o Hadoop, um arcabouço para processamento distribuído de grande volume de dados. Os testes apresentaram desempenhos satisfatórios da arquitetura proposta, e o modelo desenvolvido classificou corretamente com baixo número de falsos positivos.
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Mustafa, Mohamed. "A Model to Identify Failure & the Root Cause." Thesis, Linnéuniversitetet, Institutionen för maskinteknik (MT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-68770.

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Through identifying failure manufacturing companies compete in today’s world to gain beneficial attributes. The purpose if this thesis is to develop a model towards identifying failure and the root cause. The model developed to identify failure and the root cause toward it, which should result it decrease in failure time (nonfunction machine). The developed model has tested and analyzed in a manufacturing company. The model has been established through studies based on preventive and predictive maintenance: FMEA & RCA.
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Siekkinen, Matti. "Root cause analysis of TCP throughput : methodology, techniques, and applications." Nice, 2006. http://www.theses.fr/2006NICE4037.

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The interest for the research community to measure the Internet has grown tremendously during the last couple of years. Thisncrease of interest is largely due to the growth and expansion of the Internet that has been overwhelming. We have experienced exponential growth in terms of traffic volumes and number of devices connected to the Internet. In addition, the heterogeneity of the Internet is constantly increasing: we observe more and more different devices with different communication needs residing in or moving between different types of networks. This evolution has brought up many needs - commercial, social, and technical needs - to know more about the users, traffic, and devices connected to the Internet. Unfortunately, little such knowledge is available today and more is required every day. That is why Internet measurements has grown to become a substantial research domain today. This thesis is concerned with TCP traffic. TCP is estimated to carry over 90% of the Internet's traffic, which is why it plays a crucial role in the functioning of the entire Internet. The most important performance metrics for applications is typically throughput, i. E. The amount of data transmitted over a period of time. Our definition of the root cause analysis of TCP throughput is the analysis and inference of the reasons that prevent a given TCP connection from achieving a higher throughput. These reasons can be many: application, network, or even the TCP protocol itself. This thesis comprises three parts: methodology, techniques, and applications. The first part introduces our database management system-based methodology for passive traffic analysis. In that part we explain our approach, the InTraBase, which is based on an object-relational database management system. We also describe our prototype of this approach, which is implemented on PostgreSQL, and evaluate and optimize its performance. In the second part, we present the primary ontributions of this thesis: the techniques for root cause analysis of TCP throughput. We introduce the different potential causes that can prevent a given TCP connection to achieve a higher throughput and explain in detail the algorithms we developed and used to detect such causes. Given the large heterogeneity and potentially large impact of applications that operate on top of TCP, we emphasize their analysis. The core of the third part of this thesis is a case study of traffic originating from clients of a commercial ADSL access network. The study focuses on performance analysis of data transfers from a point of view of the client. We discover some surprising results, such as poor overall performance of P2P applications for file distribution due to upload rate limits enforced by client applications. The third part essentially binds the two first ones together: we give an idea of the capabilities of a system combining the methodology of the first part with the techniques of the second part to produce meaningful results in a real world case study
L'intérêt pour la métrologie de l'Internet s'est beaucoup accru ces dernières années. Ceci est en grande partie dû à la croissance de l'Internet en termes de volumes de trafic et de nombre de machines reliés à l'Internet. Cette évolution a sucité beaucoup d'envies - du point de vue commercial, social, et technique - d'en savoir plus au sujet des utilisateurs et du trafic Internet en général. Malheureusement, il y a peu de connaissances de ce type disponibles aujourd'hui. C'est pourquoi la métrologie de l'Internet est devenue un domaine substantiel de recherches. Cette thèse porte sur l'analyse du trafic TCP. On estime que TCP transporte 90% du trafic Internet, ce qui implique que TCP est une pièce essentielle dans le fonctionnement de l'Internet. La métrique de performance la plus importante pour les applications est, dans la plupart des cas le débit de transmission ; c'est-à-dire la quantité des données transmises par périodes de temps. Notre objectif est l'analyse du débit de transmission de TCP et l'identification des raisons qui empêchent une connexion TCP d'obtenir un débit plus élevé. Ces raisons peuvent être multiples: l'application, le réseau, ou même le protocole TCP lui-même. Cette thèse comporte trois parties. Une première partie sur la méthodologie, une seconde sur techniques d'analyse de TCP, et une dernière qui est une application de ces technique. Dans la première partie, nous présentons notre méthodologie basée sur un système de gestion de base de données (DBMS) pour l'analyse passive de trafic. Nous expliquons notre approche, nommée InTraBase, qui est basée sur un système de gestion de base de données orienté objet. Nous décrivons également notre prototype de cette approche, qui est implémenté au dessus de PostgreSQL, et nous évaluons et optimisons ses performances. Dans la deuxième partie, nous présentons les contributions principales de cette thèse: les techniques d'analyse des causes du débit de transmission TCP observé. Nous présentons les différentes causes potentielles qui peuvent empêcher une connexion TCP d'obtenir un débit plus élevé et nous expliquons en détail les algorithmes que nous avons développé pour détecter ces causes. Etant donné leur hétérogénéité et leur impact sur le débit TCP, nous accordons une grande importance aux applications au dessus de TCP. La troisième partie de cette thèse est une étude de cas du trafic des clients d'un réseau d'accès commercial d'ADSL. L'étude se concentre sur l'analyse des performances des transferts de données d'un point de vue client. Nous démontrons quelques résultats étonnants, tel le fait que les performances globalement faibles des applications pair-à-pair sont dues aux limitations du débit de transmission imposées par ces applications (et non à la congestion dans le réseau)
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Books on the topic "Root Cause Analysis"

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. Root Cause Analysis. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573.

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Latino, Kenneth C., Mark A. Latino, and Robert J. Latino. The PROACT® Root Cause Analysis. First edition. | Boca Raton, FL : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9781003055013.

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Andersen, Bjørn. Root cause analysis: Simplified tools and techniques. Milwaukee, WI: ASQ Quality Press, 2000.

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1968-, Fagerhaug Tom, ed. ASQ pocket guide to root cause analysis. Milwaukee, WI: ASQ Quality Press, 2013.

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1968-, Fagerhaug Tom, ed. Root cause analysis: Simplified tools and techniques. 2nd ed. Milwaukee, Wis: ASQ Quality Press, 2006.

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Joint Commission on Accreditation of Healthcare Organizations., ed. Root cause analysis in health care: Tools and techniques. 3rd ed. Oakbrook Terrace, Ill: Joint Commission on Accreditation of Healthcare Organizations, 2005.

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Joint Commission on Accreditation of Healthcare Organizations. Root cause analysis in health care: Tools and techniques. 2nd ed. Oakbrook Terrace, IL: Joint Commission on Accreditation of Healthcare Organizations, 2003.

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Wilson, Paul F. Root cause analysis: A tool for total quality management. Milwaukee, Wis: ASQC Quality Press, 1993.

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Joint Commission on Accreditation of Healthcare Organizations. Root cause analysis in health care: Tools and techniques. Oakbrook Terrace, Ill: Joint Commission on Accreditation of Healthcare Organizations, 2000.

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C, Latino Kenneth, ed. Root cause analysis: Improving performance for bottom-line results. 2nd ed. Boca Raton, FL: CRC Press, 2002.

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Book chapters on the topic "Root Cause Analysis"

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Opportunity Analysis: “Mindfulness”." In Root Cause Analysis, 65–86. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-5.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Introduction to the PROACT® Root Cause Analysis (RCA) Work Process." In Root Cause Analysis, 1–17. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-1.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Communicating Findings and Recommendations." In Root Cause Analysis, 183–201. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-10.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Tracking for Bottom-Line Results." In Root Cause Analysis, 203–19. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-11.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "The Role of Human Error in Root Cause Analysis: Understanding Human Behavior." In Root Cause Analysis, 221–52. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-12.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Do Human Performance “Learning Teams” Make RCA Obsolete?" In Root Cause Analysis, 253–63. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-13.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Is There a Direct Correlation between Reliability and Safety?" In Root Cause Analysis, 265–78. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-14.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Automating Root Cause Analysis: Introducing PROACTOnDemand®." In Root Cause Analysis, 279–99. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-15.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Case Histories." In Root Cause Analysis, 300–323. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-16.

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Latino, Mark A., Robert J. Latino, and Kenneth C. Latino. "Introduction to the Field of Root Cause Analysis." In Root Cause Analysis, 19–38. Fifth edition. | Boca Raton : CRC Press is an imprint of Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429446573-2.

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Conference papers on the topic "Root Cause Analysis"

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Song, Zhigang, Jochonia Nxumalo, Manuel Villalobos, and Sweta Pendyala. "Root Cause Analysis for Pin Leakage." In ISTFA 2016. ASM International, 2016. http://dx.doi.org/10.31399/asm.cp.istfa2016p0217.

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Abstract Pin leakage continues to be on the list of top yield detractors for microelectronics devices. It is simply manifested as elevated current with one pin or several pins during pin continuity test. Although many techniques are capable to globally localize the fault of pin leakage, root cause analysis and identification for it are still very challenging with today’s advanced failure analysis tools and techniques. It is because pin leakage can be caused by any type of defect, at any layer in the device and at any process step. This paper presents a case study to demonstrate how to combine multiple techniques to accurately identify the root cause of a pin leakage issue for a device manufactured using advanced technology node. The root cause was identified as under-etch issue during P+ implantation hard mask opening for ESD protection diode, causing P+ implantation missing, which was responsible for the nearly ohmic type pin leakage.
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Grusho, Alexander A., Nick A. Grusho, Michael I. Zabezhailo, Elena E. Timonina, and Vladimir V. Senchilo. "Metadata For Root Cause Analysis." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0267.

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The paper is devoted to the task of finding the root cause of anomaly in a distributed information and computing system. An approximate approach is considered to detect implicit anomalies with accuracy to the object (of a component of the technical device, a node of a network infrastructure, an application or of an information resource). The approximate solution is based on the use of integral parameters that allow you to identify an anomaly, but do not allow you to indicate its cause. To work with such methods for determining the root causes of anomalies, auxiliary data is required, which is called metadata in the work. The work describes a metadata construction algorithm and shows ways of using metadata to build an object in which the root cause of the anomaly is located. An approximate solution to the problem of finding the root cause of an anomaly with a help of quickly computable values of integral parameters is necessary to reduce the time of interruption of work processes due to implicit anomalies. It is assumed that small subsystems and nodes are easier to replace than to delve into the study of the cause.
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Clark, Wade, Jeff Cook, and Chris Madden. "Root Cause Analysis of Passenger Side Door System." In IEEE/ASME/ASCE 2008 Joint Rail Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/jrc2008-63074.

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This paper presents a real-world application of Root Cause Analysis methods that were applied to analyze and resolve a side door problem that was occurring on a particular fleet of coach cars operated by Amtrak. The foundation of a Root Cause Analysis program is a disciplined engineering process designed to identify physical, human and latent roots of chronic or sporadic problems. This paper presents the engineering challenges encountered while conducting root cause analysis of a complex coach car side doors problem. The coach car side doors system exhibited undesirable characteristics when in service. Mechanical staff received multiple reports that during station stops all of the open side doors would unexpectedly close even though the train speed was at zero. During this unexpected side door closing sequence all of the obstacle detection systems were disabled presenting a potential hazard to the traveling public. The side doors Root Cause Analysis team found the Physical Root cause of failure to be located in the door system software code — a finding that was never suspected following years of trouble with the side doors. As a result of this finding, the door manufacturer corrected the software and issued revised software to be installed fleet-wide. The Human and Latent Root causes were addressed through the development of a standard operating procedure and training for conductors and crew on the proper operation of side doors. The Root Cause Analysis method proved to be a powerful and productive process for achieving a solution to a chronic passenger side door problem.
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Park, Y. H., and Michael Cournoyer. "Root Cause Analysis of Glovebox Glove Failure." In ASME 2007 Pressure Vessels and Piping Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/pvp2007-26121.

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The Nuclear Materials Technology (NMT) Division has the largest inventory of glovebox gloves at Los Alamos National Laboratory (LANL). Consequently, the minimization of unplanned breaches of the glove material, typically resulting in glove failures, is a significant safety concern in the daily operations in NMT Division facilities. To investigate processes and procedures that minimize unplanned breaches in the glovebox, information on glovebox glove failures has been compiled from formal records and analyzed using statistical methods. Based on these research results, the next step of the research is to identify root causes of glove failures and the actions adequate to prevent recurrence. In this paper, root cause analysis was conducted for a cleanup breach case study to demonstrate the computerized root cause analysis process. Based on analysis results, effective recommendations were generated.
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Leszak, Marek, Dewayne E. Perry, and Dieter Stoll. "A case study in root cause defect analysis." In the 22nd international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/337180.337232.

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Nishimura, Keisuke, Yuichi Sugiyama, Yuki Koike, Masaya Motoda, Tomoya Kitagawa, Toshiki Takatera, and Yuma Kurogome. "RCABench: Open Benchmarking Platform for Root Cause Analysis." In Workshop on Binary Analysis Research. Reston, VA: Internet Society, 2023. http://dx.doi.org/10.14722/bar.2023.23004.

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Iyeh, Emmanuel, and Grant Adams. "Offshore Pipeline Failure Root Cause Analysis." In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/188717-ms.

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Toman, Jaka. "Literature Review on Root Cause Analysis." In 31st Bled eConference: Digital Transformation – From Connecting Things to Transforming Our Lives, June 17 – 20, 2018, Bled, Slovenia. Univerzitetna založba Univerze v Mariboru / University of Maribor Press, 2018. http://dx.doi.org/10.18690/978-961-286-170-4.48.

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Nelms, C. Robert. "The problem with root cause analysis." In 2007 IEEE 8th Human Factors and Power Plants and HPRCT 13th Annual Meeting. IEEE, 2007. http://dx.doi.org/10.1109/hfpp.2007.4413215.

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Hudon, C., M. Levesque, D. H. Nguyen, C. Millet, and F. Truchon. "Root cause analysis of generator failures." In 2012 IEEE International Symposium on Electrical Insulation (ISEI). IEEE, 2012. http://dx.doi.org/10.1109/elinsl.2012.6251457.

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Reports on the topic "Root Cause Analysis"

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Armstrong, M. E., W. L. Cecil, and K. Taylor. Root cause analysis handbook. Office of Scientific and Technical Information (OSTI), March 1988. http://dx.doi.org/10.2172/6069992.

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Miller, Miles C. Root Cause Analysis Methodology. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256855.

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Gravois, Melanie C. ROOT CAUSE ANALYSIS PROGRAM MANUAL. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/914500.

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Aronin, Benjamin S., John W. Bailey, Ji S. Byun, Gregory A. Davis, Cara L. Wolfe, Thomas P. Frazier, and Patricia F. Bronson. Expeditionary Combat Support System: Root Cause Analysis. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada571034.

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Balaban, Harold S., Paul M. Kodzwa, Andrew S. Rehwinkel, Gregory A. Davis, and Patricia F. Bronson. Root Cause Analysis for the ATIRCM/CMWS Program. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada555310.

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Inaba, K. Programmatic root cause analysis of maintenance personnel performance problems. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/6302194.

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Brown, R. L. Trending and root cause analysis of TWRS radiological problem reports. Office of Scientific and Technical Information (OSTI), July 1997. http://dx.doi.org/10.2172/312809.

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Byun, Ji S., Caolionn L. O'Connell, and Patricia F. Bronson. Chemical Demilitarization - Assembled Chemical Weapons Assessment (ACWA): Root Cause Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada549911.

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Ousley, Jase, Ram Mohan, Melinda Strevig, Katherine Skelton, Marin Kress, J. Farrar, and Alan Kennedy. Hurdles to beneficial use of dredged material : root cause analysis. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48256.

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This technical note (TN) summarizes high points of an internal review of US Army Corps of Engineers (USACE) dredging and dredged material management practices, specifically beneficial use of dredged material (BUDM), that USACE manages from various navigation channels and ports around the nation.
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Stratton, R. C., and D. B. Jarrell. Towards the development of multilevel-multiagent diagnostic aids. [Root-cause analysis]. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/6128963.

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