Tool For Root-cause Analysis Research Articles

Determining which of the classic seven quality tools are in the quality practitioner’s RCA tool kit

The purpose of this study is to determine which of the classic seven quality tools are used by quality professionals during a Root Cause Analysis (RCA). The literature was assessed to determine which quality tools are considered to be part of the seven classic quality tools. The identified tools were then used in a survey of quality professionals in a manufacturing organization, who were asked to select the classic seven quality tools they use during an RCA. An email distribution list was used to send the survey to quality professionals at various locations around the world. The results were then analyzed statistically. The cause and effect diagram was the most commonly used quality tool during an RCA. This was distantly followed by the Pareto chart and then the control chart, followed by the Pareto chart, flow chart, histogram, scatter plot, and run chart. The classic seven quality tools have long been discussed in the literature; however, much of the literature consist of descriptions of their use and not an assessment of which tools are frequently used by quality professionals specifically for RCA. This study identified the seven main classic quality tools used by quality professionals during an RCA.

BHA Behavior-Prediction Model Offers Potential for Automated Analysis

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191780-18ERM-MS, “Field Validation of a New BHA Model and Practical Case Studies in Unconventional Shale Plays, With a Framework for Automated Analysis for Operations Support,” by J.K. Wilson, Scientific Drilling International, prepared for the 2018 SPE/AAPG Eastern Regional Meeting, Pittsburgh, Pennsylvania, USA, 7–11 October. The paper has not been peer reviewed. A new 3D drillstring model determines the static and dynamic behavior of bottomhole assemblies (BHAs) in realistic wellbores. The modeling approach provides a prediction of the BHA’s mechanical and dynamic behavior and can be used as a planning tool for BHA design, an investigative tool for root-cause analysis, or, potentially, as a real-time optimization tool for avoiding harmful operating conditions. Introduction When using any model, engineers cannot forget the limits of calculation. However, they can settle for “good enough” as long as they adhere to the following two rules: The results capture the general behavior of the system with respect to the parameters of interest. The engineer using the results fully understands the limits of the model used to obtain them. These rules are particularly important when considering highly nonlinear systems, such as the 3D behavior of BHAs. Nonlinearity arises in these systems from the large, coupled deflection that occurs in the downhole components as they move within the wellbore, and the frictional contact that results from the borehole wall restraining that movement. These inherent complexities have been a driving factor for continued research on the subject of BHA modeling for years. On the basis of the author’s own investigative studies and working experience, there appear to be three primary areas of focus within the study of BHA mechanics and dynamics: The mechanical loading on the downhole components The directional performance of various drilling assemblies The dynamic response of BHAs The purpose of the current project was to develop a BHA model that could analyze each of these topics in an efficient manner while maintaining a reasonable level of accuracy. Such a model could then be leveraged routinely to design more-robust tools and provide a reliable approach to engineering support for drilling operations. BHA Simulation Model As a response to the need to estimate downhole performance better, a mathematical model has been developed for estimating the mechanical and dynamic behavior of BHAs in realistic wellbores. It is based on a nonlinear finite-beam element and accounts for various intricacies of the downhole environment. Modeling a drilling assembly using this approach allows for the complexities of the BHA components to be easily represented as a series of beams with varying mechanical properties. The model was initially developed to analyze the behavior of vibration- inducing devices in unconventional horizontal wells, but has since been expanded to examine a multitude of different drilling•scenarios.

A novel knowledge-derived data potentizing method revealed unique liver cancer-associated genetic variants

BackgroundNext-generation sequencing (NGS) has been advancing the progress of detection of disease-associated genetic variants and genome-wide profiling of expressed sequences over the past decade. NGS enables the analyses of multiple regions of a genome in a single reaction format and has been shown to be a cost-effective and efficient tool for root-cause analysis of disease and optimization of treatment. NGS has been leading global efforts to device personalized and precision medicine (PM) in clinical practice. The effectiveness of NGS for the aforementioned applications has been proven unequivocal for multifactorial diseases like cancer. However, definitive prediction of cancer markers for all types of diseases and for global populations still remains highly rewarding because of the diversity of cancer types and genetic variants in human.ResultsWe performed exome sequencing of four samples in quest of critical genetic factor/s associated with liver cancer. By imposing knowledge-based filter chains, we have revealed a panel of genetic variants, which are unrecognized by current major genomics data repositories. Total 20 MNV-induced, 5 INDEL-induced, and 31 SNV-induced neoplasm-exclusive genes were revealed through NGS data acquisition followed by data curing with the application of quality filter chains. Liver-specific expression profile of the identified gene pool is directed to the selection of 17 genes which could be the as likely causative genetic factors for liver cancer. Further study on expression level and relevant functional significance enables us to identify and conclude the following four novel variants, viz., c.416T>C (p.Phe139Ser) in SORD, c.1048_1049delGCinsCG (p.Ala350Arg) in KRT6A, c.1159G>T (p.Gly387Cys) in SVEP1, and c.430G>C (p.Gly144Arg) in MRPL38 as a critical genetic factor for liver cancer.ConclusionBy applying a novel data prioritizing rationale, we explored a panel of previously unaddressed liver cancer-associated variants. These findings may have an opportunity for early prediction of neoplasm/cancer in liver and designing of relevant personalized/precision liver cancer therapeutics in clinical practice. Since NGS protocol is associated with tons of non-specific mutations due to the variation in background genetic makeup of subjects, therefore, our method of data curing could be applicable for more effective screening of global genetic variants related to disease onset, progression, and remission.

A DMAIC Six Sigma approach to quality improvement in the anodising stage of the amplifier production process

PurposeThe purpose of this paper is to study the anodising process of a portable amplifier production process to identify and eliminate the sources of variations, in order to improve the process productivity.Design/methodology/approachThe study employs the define-measure-analyse-improve-control (DMAIC) Six Sigma methodology. Within the DMAIC framework various tools of quality management such as SIPOC analysis, cause and effect diagram, current reality tree, etc., are used in different stages.FindingsHigh rejection rate was found to be the main problem leading to lower productivity of the process. Four types of defects were identified as main cause of rejections in the baseline process. Pareto analysis resulted in detection of the top defects, which were then analysed in details to find the root cause of the problem. Further study resulted in finding improvement measures that were discussed with the management before implementation. The process is sampled again to check the improvements, and control measures were established.Practical implicationsThe study provides a framework for implementation of DMAIC Six Sigma methodology for a manufacturing firm. The results presented are based on the data collected from the shop floor. Results and findings of the study were implemented for quality improvement of the process.Originality/valueThe study is based on an original research conducted with the objective of quality improvement in the anodising process of the production process. Besides presenting an approach to DMAIC Six Sigma methodology, an application of the current reality tree tool for root cause analysis is presented, a tool used limitedly in the Six Sigma studies. The tool finds its uniqueness in its ability to address problems relating multiple factors than isolated factors.

Self-Healing Framework for Next-Generation Networks through Dimensionality Reduction

Next-generation self-organizing networks (NG-SONs) are the key that will lead to the full automation of the network management in the forthcoming generations of cellular communications. New challenges, like the deployment of novel wireless services or the aim of operators to provide end-to-end monitoring and optimization, make it necessary to develop an innovative scheme for network management. Within SON, self-healing (SH) comprises fault detection, root cause analysis (RCA), and compensation. Within these, the automation of RCA activities is one of the key elements to reduce operational expenditures related to network management. In this article, an SH framework for next-generation networks using dimensionality reduction is proposed as the tool enabling the management of an increasingly complex network, taking advantage of both feature selection and feature extraction techniques. A proof of concept has been carried out in the context of automatic RCA in a live network. Results show that the proposed framework can effectively manage a high-dimensional environment from different data sources, eventually automating the tasks usually performed by troubleshooting experts while optimizing the performance of the RCA tool.

Delayed Recognition of Deterioration of Patients in General Wards Is Mostly Caused by Human Related Monitoring Failures: A Root Cause Analysis of Unplanned ICU Admissions.

BackgroundAn unplanned ICU admission of an inpatient is a serious adverse event (SAE). So far, no in depth-study has been performed to systematically analyse the root causes of unplanned ICU-admissions. The primary aim of this study was to identify the healthcare worker-, organisational-, technical,- disease- and patient- related causes that contribute to acute unplanned ICU admissions from general wards using a Root-Cause Analysis Tool called PRISMA-medical. Although a Track and Trigger System (MEWS) was introduced in our hospital a few years ago, it was implemented without a clear protocol. Therefore, the secondary aim was to assess the adherence to a Track and Trigger system to identify deterioration on general hospital wards in patients eventually transferred to the ICU.MethodsRetrospective observational study in 49 consecutive adult patients acutely admitted to the Intensive Care Unit from a general nursing ward. 1. PRISMA-analysis on root causes of unplanned ICU admissions 2. Assessment of protocol adherence to the early warning score system.ResultsOut of 49 cases, 156 root causes were identified. The most frequent root causes were healthcare worker related (46%), which were mainly failures in monitoring the patient. They were followed by disease-related (45%), patient-related causes (7, 5%), and organisational root causes (3%). In only 40% of the patients vital parameters were monitored as was instructed by the doctor. 477 vital parameter sets were found in the 48 hours before ICU admission, in only 1% a correct MEWS was explicitly documented in the record.ConclusionsThis in-depth analysis demonstrates that almost half of the unplanned ICU admissions from the general ward had healthcare worker related root causes, mostly due to monitoring failures in clinically deteriorating patients. In order to reduce unplanned ICU admissions, improving the monitoring of patients is therefore warranted.

Exploring the preventable causes of unplanned readmissions using root cause analysis: Coordination of care is the weakest link

ImportanceUnplanned readmissions within 30days are a common phenomenon in everyday practice and lead to increasing costs. Although many studies aiming to analyze the probable causes leading to unplanned readmissions have been performed, an in depth-study analyzing the human (healthcare worker)-, organizational-, technical-, disease- and patient-related causes leading to readmission is still missing. ObjectiveThe primary objective of this study was to identify human-, organizational-, technical-, disease- and patient-related causes which contribute to acute readmission within 30days after discharge using a Root-Cause Analysis Tool called PRISMA-medical.The secondary objective was to evaluate how many of these readmissions were deemed potentially preventable, and to assess which factors contributed to these preventable readmissions in comparison to non-preventable readmissions. DesignCross-sectional retrospective record study. SettingAn academic medical center in Amsterdam, The Netherlands. ParticipantsFifty patients aged 18years and older discharged from an internal medicine department and acutely readmitted within 30days after discharge. Main outcome measuresRoot causes of preventable and unpreventable readmissions. ResultsMost root causes for readmission were disease-related (46%), followed by human (healthcare worker)- (33%) and patient- (15%) related root causes. Half of the readmissions studied were considered to be potentially preventable. Preventable readmissions predominantly had human-related (coordination) failures. Conclusion and relevanceOur study suggests that improving human-related (coordinating) factors contributing to a readmission can potentially decrease the number of preventable readmissions.

Symptoms Versus Problems (SVP) Framework on Export Barriers to SME’S Led Electronic Products in Taiwan

Export barriers are a common constraints to SMEs when exporting goods. In 2013, Taiwan’s export sales dropped by 18.5% due to SMEs’ difficulty to explore the overseas market, thus, redirect their sales locally. The objective of this case study is to identify the internal and external barriers, and the export model practiced by one export case in Taipei, Taiwan. Findings via semi-structured interviews, field observation and analysis of unpublished company data are summarized using a new root-cause analysis tool called Symptoms versus Problems (SVP) framework for SME LED Exports. The new SVP framework discovers many symptoms leading to recognition of six major problems, namely, high operational cost, poor marketing strategy, lack of cash flow funds, talent and skill sets, government support, and too many foreign bureaucracy regulations. These problems are categorized into three groups, namely, operation, marketing and government bureaucracies. The recommendations for each problem are structured to improve product costs, branding and advertisement strategies, cash flow management, technical know-how of foreign market behaviour, sources of information and network, and managing the foreign bureaucracies. Interestingly, this study discovers another new framework that overcome LED export barriers called “House of Pillars for LED Exports” in Taiwan .

Enhancing Georgetown’s Heritage Boutique Hotel Marketing Strategies: The Case of 1881 Chong Tian Cultural Hotel, Malaysia

Organizations has longed been searching for an effective problem identification tool. This study provides an improved method to root-cause analysis tool for practitioners, integrating mixed method research with problems identification technique. It also discovers the primary causes to unfavorable performance for heritage boutique hoteliers. 1881 Chong Tian Cultural Hotel, one of the 18 heritage boutique hotels in Penang, reported a 23% financial loss in 2010, recovered in 2011, yield some profits in 2012 and 2013. However, growth in boutique hotel industry has been a challenge over the years. The sequential approach methodology, begins with sem-structured interviews, quantitative surveys on 100 general public and 56 hotel guests, and analysis on unpublished company data, were summarized using a root-cause analysis tool called the Symptoms Versus Problems (SVP) Framework that questioned on the occurance of the symptoms for the first three Tiers of the framework. The remaining symptoms and core problems are identified based on findings from all the three methods above plus field observations, concurrently. Two major symptoms are identified, specifically, unfavorable cultural service quality and ineffective marketing strategies. The unfavorable cultural service quality is due to ineffective cultural ambiance; unproductive display of heritage, cultural artifacts and infrastructure; as well as, unfavorable cultural related services. The ineffective marketing strategies are due to low technology adoption of websites and social media; and ineffective customer relationship management (CRM) implementation. Thus, the recommended solutions bring to life the practices of Qing Dynasty cultural ambiance, ceremonies, historical shows and the royale reception. Information on artifacts and infrastructure is readily available. Website development and social media updates on latest Chong Tian Hotel activities are being developed. Embracing delegates using Qing Dynasty traditions at airports and hotels can be a real experience for the growing tourists from Japan, Australia, UK and Middle East.

Evaluating Metrics for Quality

Surgical mortality is considered a benchmark for measuring quality of care. This study quantifies the incidence of death on the day of elective pediatric surgery, which generally is considered preventable and might be considered a "never" event. The authors conducted a retrospective analysis of national state inpatient databases from 1988 to 2007 that included elective pediatric surgical patients. A descriptive analysis of same-day mortality by demographics, surgical specialties, and age was performed. Of 835 880 elective pediatric surgical cases identified, 174 patients died on the day of surgery-that is, 2.1 deaths/10 000 cases. Surgical specialty mortality rates ranged from 0.06 (otolaryngology) to 17.4 (cardiothoracic surgery) deaths per 10 000 cases. Death on the day of elective pediatric surgery is rare, limiting its utility to compare performance in pediatric surgery. However, this metric may be useful at individual institutions as a case-finding tool for root-cause analysis in quality improvement efforts.

E-RCA – collation and feedback of HCAI information

Root Cause Analysis (RCA) is an analysis method which aims to identify the root causes of preventable Health care associated infections (HCAIs). It is based on the concept that problems are best solved by attempting to address and take corrective action by eliminating the root causes rather than merely addressing the immediately obvious issues. The RCA tool developed by the UK Clean, Safe Care Team is useful helping to reduce MRSA (Methicillin resistant S. aureus) bacteraemias and C. difficile infections. We had been using RCA forms developed by the Clean, Safe Care Team until 2009 but felt that we needed to modify the content of the form to make it more effective and to develop a web based form to improve compliance with the RCA process.

C. difficile infection – a tool for improving practice and reducing rates

Clostridium difficile infection (CDI) is a recognized health-care-acquired infection (HAI) and as such it is vital that hospitals do more to reduce the rates of infection. Patients, especially older people, become particularly susceptible to CDI after treatment with antibiotics. Mention of CDI on death certificates is increasing and surveillance, especially when accompanied by feedback to clinicians, has long been established as an effective tool to lower HAIs. In February 2008, a Manchester hospital began to pilot the Root Cause Analysis (RCA) tool for all patients confirmed with a CDI. After receiving feedback from clinicians on its appropriateness and suitability, the RCA tool was amended accordingly and then implemented into practice. The RCA tool has significantly improved practice and collaborative working - it has enhanced teamwork and ultimately reduced infection. This article examines how the tool was embedded in the Trust, how the change process has been managed and ultimately, how patient care has benefited as a result.

Utility of an online medication-error-reporting system

The utility of an online medication-error-reporting program was evaluated. A survey regarding the utility of Medmarx was developed and mailed to 550 hospitals and health systems that used the medication-error-reporting program. Primary contact persons were identified and asked to gather and report the necessary information to adequately characterize medication-error reporting in their institution before and after implementing the Medmarx system. Potential respondents were contacted four times during the survey period by the United States Pharmacopeia. Of the 550 surveys sent, 200 were returned and 25 were undeliverable, resulting in a response rate of 38%. The average number of medication errors reported internally increased twofold after the adoption of the Medmarx system. Pharmacy departments were most often reported as responsible for medication-safety oversight activities after Medmarx implementation. Most facilities (94%) generated reports from the medication-error database, and 75% used this information to identify 7.0+/-8.1 opportunities to improve their medication-use system annually. Most respondents believed that Medmarx played an integral role in preparing their facility for Joint Commission accreditation surveys (65%), provided a tool for root-cause analysis (71%), and helped identify problems in the medication-use process (85%). The annual costs of the subscription and staff time required to use the system was estimated to be 16,756 dollars+/-21,108 dollars. Sixty-six percent of users were satisfied with the impact the system has had on improving the medication-use process. Implementation of the Medmarx system led to an increase in the number of reported medication errors and improvements in the medication-use process.

Evaluation of Single Semiconductor Defects Using Multiple Microanalysis Techniques

AbstractNew analytical techniques are being employed to meet the stringent requirements for controlling defects in semiconductor manufacturing. While SEM/EDX continues to be an effective tool for root-cause analysis of particles and defects on the wafer surface, increasingly it is necessary to employ multiple techniques in analysis of a single defect to obtain sufficient information to resolve the problem. Until recently, this approach was impractical because the defects were too difficult to locate in standard analytical tools, especially on unpatterned wafers. But with the newly developed Mark-Assisted Defect Analysis (MADA) technique, the positioning problem has been eliminated, and it is a simple matter to employ multiple techniques on the same defect. In this paper we present results for defects analyzed by SEM/EDX, TOFSIMS, μ-ESCA, and AUGER, highlighting how these techniques compliment each other. Particular emphasis is given to the issue of the analytical technique changing the sample chemistry and influencing the results of subsequent analysis techniques.