Use Of Control Charts Research Articles

Quality control analysis of downtime and time to repair for water supply pipes

PurposeThe purpose of this paper is to investigate performance times from a quality engineering perspective for response and repair of pipes at a public utility. The objective is to scientifically determine the pipe that offers the most desirable downtime (DT) and time to repair (TTR).Design/methodology/approachFour types of water supply pipes – concrete cylinder (CC), cast iron (CI), ductile iron (DI), and polyvinyl chloride (PVC) – in prevalent use at the City and County of Honolulu Board of Water Supply were analyzed to determine the pipe type that is most consistently repaired to desired performance specifications. Data for mean downtime (MDT) and mean time to repair (MTTR) were used to evaluate the stability and capability of the repair processes for each pipe type. The analysis was completed through the use of control charts, operating characteristic (OC) curves, and process capability indices.FindingsThe results of the analysis indicated that CI pipes were the worst material in terms of DT and TTR. The control charts for MDT for all pipe types, and the MTTR for CI and CC pipes, were found to be out of statistical control, but the control charts for the MTTR of DI and PVC pipes were discovered to be in control. According to the OC curves, in which the hypothesis stated that the average MDT or MTTR was between the specification limits, there was a high tendency in all pipe types to accept the hypothesis when it was true. However, the probability of type I errors was high from operational standards at the USL level. Process capability analyses found that only CC pipes were able to meet performance design specifications; however, repair times are extremely large for CC pipes. Overall, it is recommended that CI pipes be replaced when the opportunity arises.Practical implicationsThis investigation serves to address a major query in asset management at the public utility, that of which pipes should be selected during design and procurement from a maintenance perspective. In addition, the study helps to understand the trend of DT and TTR for the various pipes.Social implicationsQuality water supply is of paramount social importance in modern cities.Originality/valueA quality engineering approach to asset management for pipe systems at public utilities that serves to add a new dimension to asset performance analysis is adopted.

Quantification of carcinogenic 4- to 6-ring polycyclic aromatic hydrocarbons in human urine by solid-phase microextraction gas chromatography–isotope dilution mass spectrometry

Polycyclic aromatic hydrocarbons (PAHs) are pollutants found in living and working environments. The aim of this study was to develop a solid-phase microextraction (SPME) gas chromatography (GC)-isotope dilution mass spectrometry method for the quantification of 10 four- to six-ring PAHs in urine samples. Seven of the selected PAHs have been classified as carcinogenic. Under the final conditions, analytes were sampled with a 100-μm polydimethylsiloxane SPME fibre for 60 min at 80 °C and desorbed in the injection port of the GC at 270 °C. Fluoranthene, pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene and benzo[ghi]perylene were separated using a highly arylene-modified phase capillary column and quantified by MS using eight deuterated PAHs as surrogate internal standards. Limits of quantification (LOQ) were in the 0.5- to 2.2-ng/L range. Validation showed linear dynamic ranges up to 340 ng/L, inter- and intra-run precisions <20%, and accuracies within 20% of spiked concentrations. Matrix effect evaluation and the use of control charts to monitor process performances showed that the isotope dilution approach allowed for the control of bias sources. Urinary PAHs were above or equal to LOQ, depending on different compounds, in 58-100% (min-max), 40-100% and 5-39% of samples from coke oven workers (n = 12), asphalt workers (n = 10) and individuals not occupationally exposed to PAHs (n = 18), respectively. Chrysene was the most abundant PAH determined with median levels of 62.6, 6.9 and <0.6 ng/L, respectively. These results show that the method is suitable for quantifying carcinogenic PAHs in specimens from individuals with different levels of PAH exposure.

Fuzzy [formula omitted] and R control charts: Fuzzy dominance approach

The statistical process control (SPC), an internationally recognized technique for improving products quality and productivity, has been widely employed throughout various industries. The SPC relies on the use of control charts to monitor a manufacturing process for identifying special causes in the process variation and signaling the necessity of a certain corrective action for the process. Since fuzzy data ubiquitously exist in the modern manufacturing process, for monitoring its sample average and variance, we propose the fuzzy X ¯ and R control charts, whose fuzzy control limits are obtained on the basis of the results of the resolution identity, a well-known theory in the fuzzy set field. By using the fuzzy dominance approach, which compares the fuzzy averages and variances to their respective fuzzy control limits, we are capable of determining whether the manufacturing process is needed to be adjusted or not. Finally, a practical manufacturing process illustrates the proposed methodologies to show the potential application in monitoring its average and variability while its fuzzy sample data are taken into consideration.

A Review and Perspective on Control Charting with Image Data

Machine-vision systems are increasingly being used in industrial applications due to their ability to provide not only dimensional information but also information on product geometry, surface defects, surface finish, and other product and process characteristics. There are a number of applications of control charts for these high-dimensional image data to detect changes in process performance and to increase process efficiency. We review the control charts that have been proposed for use with image data in industry and in some medical-device applications and discuss their advantages and disadvantages in some cases. In addition, we highlight some application opportunities available in the use of control charts with image data and provide some advice to practitioners.

Constrained economic design of S control charts for random process shifts

PurposeThe use of control charts to monitor a product quality characteristic requires the selection of their design parameters. To select feasible design parameters, the constraints related to the inspection resources available at the workstation, its configuration and the process operating parameters should be taken into account. The aim of this research is to discuss the design of Shewhart and EWMA control charts monitoring dispersion in the presence of these constraints and for processes characterized by a random shift size.Design/methodology/approachIn this paper the design of the investigated control charts has been intended in a broader sense as the design of a local inspection procedure constrained by a set of resource/environmental process factors characterizing the workstation configuration. An economic objective pursuing the minimization of the total inspection cost has been considered.FindingsConstraining the design of a control chart is an essential issue to find chart design parameters that can be effectively implemented by quality practitioners. The influence of the process‐operating parameters has been modelled and some guidelines have been suggested through the use of contour plots.Originality/valueThe economic design of control charts has been scarcely implemented by quality practitioners due to the difficulty of adapting them to the multifaceted complexity and constraints present within manufacturing environments. This paper tries to contribute to the existing literature by improving the available mathematical models through modelling the actual workstation configuration and resource allocation; furthermore, for random shift processes it investigates the performance of the Shewhart and EWMA control charts monitoring process dispersion.

Statistical process control charts for ophthalmology

Dear Editor,In this era of evidence-based medicine, ophthalmologistscontinually strive to integrate the most advanced medicaland surgical knowledge into routine clinical practice.However, do we have any real-time methods to statisticallymonitor processes of care in our practices and revealavenues for improvement? Over the past several years, theuse of statistical process control (SPC) charts, a toolkitdeveloped in engineering for process improvement, hasgained popularity in healthcare. Though SPC charts aretypically used in primary care settings, specialties includingcardiology and pulmonology have begun to utilize thesetools to assess both patient outcomes and the effectivenessof quality improvement interventions into clinical processes[1, 2].There are several types of SPC charts. XmR charts andp-charts are frequently used in the healthcare setting [2].These charts characterize the incidence of disease longitu-dinally over a given period of time. They can be used inconjunction with g-charts, which are designed to monitorthe number of days between incidence of disease. Interpre-tation of a g-chart enables the user to predetermine howmuch variation (i.e., the interval days between cases) ispermissible before case frequency becomes excessive, thuscreating a goal to guide quality improvement measures.SPC charts not only monitor incidence over time but canalso be used to calculate points of statistically significantvariation—‘special cause’—in the clinical process [2].Initially, physicians must determine the normal clinicaloutcome variation for a given procedure. Benchmarks fornormal variation can be foundintheliterature.Forexample, assume that at a particular clinic there have beenapproximately 3–4 cases of endophthalmitis each year forthe past 10 years, and that 6,000–8,000 surgeries have beenperformed each year. If one were to track this data over adecade, it would most likely be the case that the data wouldbe in normal (‘common cause’) variation. Normal variationis stable and predictable. However, if there is a ‘specialcause’ signal, it denotes that there has been a change to theprocess or system either above the upper control limit(UCL) or below the lower control limit (LCL). Specialcause indicates that the incidence of cases has exceedednormal variation in a given time (e.g., seven cases peryear). In the case of endophthalmitis occurrence, if thespecial cause signal occurs below the LCL, this lets oneknow that there is something positive occurring in theclinical process and that one must continue to do it in orderto continue such progress. As one can discern, qualitativejudgment must be used to decide on a clinically acceptablelevel of complications when trying to determine if a processis to be continued or if changes to the process arewarranted.There have been only two reports in the ophthalmologicliterature exploring the use of control charts [3, 4]. Both

Real-time operational feedback: daily discharge rate as a novel hospital efficiency metric

BackgroundPart of delivering quality care means providing it in a timely, efficient manner. Improving the efficiency of care requires measurement. The selection of appropriate indicators that are valid and responsive...

On the economic-statistical design of control charts constrained by the inspection workstation configuration

A key issue for the use of control charts in a manufacturing process is the selection of their design parameters. At the moment of chart design, it is important to consider the inspection resources available at the workstation and the process operating parameters, which can strongly constrain the search for optimal chart design parameters. In this paper, the constrained economic-statistical designs of EWMA and Shewhart charts monitoring the sample mean are compared: the SPC inspection activities are locally self-managed by a worker demanded also to perform the manufacturing activities at the workstation. The shift to the out-of-control condition for the monitored quality characteristic is assumed as a random variable. A benchmark of 64 process scenarios has been generated to investigate the influence of several process operating parameters and workstation configuration. Finally, the accuracy of the expected inspection cost estimation has been computed for several distributions fitting the random shift behaviour.

Hybrid Control Charts for Active Control and Monitoring of Concrete Strength

Concrete is the most widely used material in construction industries. Therefore, it is necessary to understand and when possible, improve the efficiency of a concrete production process. Statistical process control is generally applied to gain information about variation in the manufacturing process. Control charts can be implemented to monitor the various processes involved in the production, the delivery and construction of concrete. When historical data are available on various projects, better insight into operational procedures can be obtained through the use of control charts. This paper studies a series of statistical analysis procedures to analyze the compressive strength of concrete. The proposed method is based on combining the cumulative sum (CUSUM) control chart and a run chart (CUSUM-run chart) for early detection of shifts in the process mean. The combined charts address both the consumers' and the producers' perspectives. The CUSUM-run chart can aid the consumer in making decisions about accepting or rejecting a strength test. In addition, the producers (concrete manufacturers) can use the chart to determine if the monitored process is out-of-control and subsequently attempt to identify the possible causes for the out-of-control situation. By identifying assignable causes of the out-of-control process, the producer can then improve the manufacturing process by reducing product variation, unnecessary waste, or over-designed concrete mixtures. The CUSUM-run chart is also beneficial in that it can often indicate when the strength of mixture is less than the minimum acceptable level very quickly. The delay in detecting an unacceptable strength can result in more penalties, project delays and increased associated costs.

Influence of QA/QC procedures on non-sampling error in deposition monitoring in forests

A Working Ring Test (WRT) was organised in the framework of the EU Regulation (EC) No 2152/2003 ("Forest Focus") and of the UN/ECE Program "ICP Forests" to evaluate the overall performance of the laboratories monitoring atmospheric deposition and soil solution in European Forests. Seven natural samples of atmospheric deposition and soil solutions and 5 synthetic solutions were distributed to 52 laboratories, which analysed them using their routine methods. Thirteen variables are considered in this paper: pH, conductivity, calcium, magnesium, sodium, potassium, ammonium, sulfate, nitrate, chloride, total alkalinity, total dissolved nitrogen and dissolved organic carbon. For each variable, the relative standard deviation of the results was evaluated, after outlier rejection, to estimate the analytical error of the measurements. The results are evaluated considering the Quality Assurance/Quality Control (QA/QC) procedure included in the ICP Forests monitoring manual: consistency check of the data and use of control charts and internal standards. A Data Quality Objective (DQO) is defined for each of the variables and the number of data meeting the DQOs are discussed in relation to the QA/QC procedures adopted. Although 38% of the results did not meet the DQO, the laboratories adopting QA/QC procedures produced a larger proportion of results meeting the objective and a consistent part of the outliers could be detected a posteriori checking analyses consistence.

Optimal Performance for Clinical Microbiologists and their Interaction with Infection Control Staff

Due to changes in the current health care environment, clinical microbiologists are increasingly called on to optimize their services and to interact optimally with various other staff in the hospital. This article focuses on (i) improving the performance of microbiology laboratories by the use of control charts in process control and (ii) improving interaction between clinical microbiologists and infection control staff. Also included are updates on what is occurring in legislation in areas where clinical microbiologists and practitioners of infection control interact.

Caracterização das perdas e distribuição de cobertura vegetal em colheita mecanizada de soja

O conceito de controle de qualidade nas operações inserido na agricultura é viabilizado por incidir diretamente nos principais objetivos do processo produtivo: retorno econômico e aumento da produtividade. A colheita mecanizada normalmente é realizada sem que haja controle efetivo para que a variabilidade das perdas fique dentro de padrões aceitáveis. Esta pesquisa teve o objetivo de determinar e caracterizar as perdas e a distribuição da cobertura vegetal após a colheita mecanizada da soja, por meio de ferramenta de controle estatístico de processo (cartas de controle). A média da perda de grãos total foi próxima do limite superior aceitável para a cultura da soja, apresentando alta variabilidade entre os pontos, tornando o processo fora de controle. A distribuição de cobertura vegetal manteve-se em processo controlado, com maior variabilidade onde o relevo foi mais inclinado. A utilização das cartas de controle foi eficiente na identificação dos pontos fora de controle e na avaliação da qualidade do processo de colheita.

Application of Control Charts to an Educational System

Gathering information or collecting data is the norm in school systems across the nation. Using that data to make informed decisions should necessitate the use of statistical tools. One such tool, developed by Walter A. Shewhart at Bell Laboratories in 1924, was the ‘Control Chart,’ a means of determining whether a process had been operating in a state of statistical control or operating in the presence of special causes of variation warranting corrective action. Use of control charts has long been an industry practice. As a school district interested in continuous quality improvement, Beloit Turner explored the application of control charts to a number of instructional and non-instructional areas early on in a restructuring project. This article looks at five such explorations.

Improving Access to Pediatric Subspecialty Care

We originally examined the effectiveness of strategies, proven successful in improving appointment availability in primary care, at a large tertiary-care academic medical center. We then sought to describe the reasons for the initial failure of these strategies. Clinics participating in an access improvement initiative were matched to control clinics. Intervention clinics used a variety of techniques to improve access. Run charts were used to determine the impact of the interventions on appointment availability. Linear models, control charts, and other graphic displays were used to understand the relationship among supply, demand, and appointment availability. Access did not improve in intervention clinics. Neither a linear models approach nor the use of control charts resulted in a simple tool to help clinics better understand the relationship among supply, demand, and days to third next available appointment. However, the development of a single clinic chart that incorporated supply and demand, plus estimates of future supply and demand, made it clear that current supply would not be able to meet demand. This helped teams focus their efforts on improving supply. Use of detailed data-based tools to guide choices of interventions, coupled with new and explicit institutional expectations for physician attendance at clinics, appears to be a promising strategy for enhancing access.

The use of control charts in monitoring postcataract surgery endophthalmitis

To apply the control chart method to retrospectively analyse the variation in postcataract surgery endophthalmitis. The endophthalmitis data between 01/07/97 and 30/06/07 (10 years) at the Royal Gwent Hospital were analysed using the control chart method. Certain criteria related to the control chart were employed to detect unusual variations and early outbreaks of endophthalmitis. A total of 21 032 cases of cataract were performed in that period, with a mean annual incidence of 0.16%. The control chart could provide a reliable method to detect an early outbreak of endophthalmitis before it was suspected and acted on. The chart provided visual information of the altering trends of endophthalmitis cases and allows unusual variations to be detected early. The control chart is potentially a sensitive method to detect anomaly in the cases of postcataract surgery endophthalmitis. Its control limits are dynamic and factor in recent performance to detect any unusual variation.

Current research on profile monitoring

In many applications the quality of a process or product is best characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is used to understand and to check the stability of this relationship over time. At each sampling stage one observes a collection of data points that can be represented by a curve (or profile). In some calibration applications, the profile can be represented adequately by a simple linear regression model, while in other applications more complicated models are needed. The purposes of this paper are to review recent research on the use of control charts to monitor process and product quality profiles and to encourage further research in this area.

Monitoring patients using control charts: a systematic review

To systematically review the uses control charts to monitor clinical variables in individual patients. Systematic searches of MEDLINE, CINAHL, Embase and five other databases yielded 74 studies, of which seven met our inclusion criteria of using control charts to monitor clinical variables for disease at an individual patient level. Included articles were reviewed independently by two reviewers. Data were extracted on study design clinical condition or disease being monitored, clinical variable or marker, measurement method, outcome measure and any changes in clinical indicator identified in the articles. Control charts were applied to four conditions--hypertension, asthma, renal function post-transplant and diabetes. Studies fell into two categories. Three studies sought to determine the 'performance' of control charts in comparison with existing 'gold standard methods' in terms of sensitivity and specificity based on moderate sample sizes (n = 35-45). This category of studies found control charts to be simple, low-cost, effective tools with good sensitivity and specificity characteristics and concluded in favour of control charts. The other four studies were individual patient case-studies in which the use of control charts to monitor clinical variables was associated with a positive impact on patient and carer experience albeit anecdotally and with varying degrees of attention. Control charts appear to have a promising but largely under-researched role in monitoring clinical variables in individual patients. Furthermore, rigorous evaluation of control charts is required.

Case-mix and the use of control charts in monitoring mortality rates after coronary artery bypass.

BackgroundThere is debate about the role of crude mortality rates and case-mix adjusted mortality rates in monitoring the outcomes of treatment. In the context of quality improvement a key purpose of monitoring is to identify special cause variation as this type of variation should be investigated to identify possible causes. This paper investigates agreement between the identification of special cause variation in risk adjusted and observed hospital specific mortality rates after coronary artery bypass grafting in New York hospitals.MethodsCoronary artery bypass grafting mortality rates between 1994 and 2003 were obtained from the New York State Department of Health's cardiovascular reports for 41 hospitals. Cross-sectional control charts of crude (observed) and risk adjusted mortality rates were produced for each year. Special cause variation was defined as a data point beyond the 99.9% probability limits: hospitals showing special cause variation were identified for each year. Longitudinal control charts of crude (observed) and risk adjusted mortality rates were produced for each hospital with data for all ten years (n = 27). Special cause variation was defined as a data point beyond 99.9% probability limits, two out of three consecutive data points beyond 95% probability limits (two standard deviations from the mean) or a run of five consecutive points on one side of the mean. Years showing special cause variation in mortality were identified for each hospital. Cohen's Kappa was calculated for agreement between special causes identified in crude and risk-adjusted control charts.ResultsIn cross sectional analysis the Cohen's Kappa was 0.54 (95% confidence interval: 0.28 to 0.78), indicating moderate agreement between the crude and risk-adjusted control charts with sensitivity 0.4 (95% confidence interval 0.17–0.69) and specificity 0.98 (95% confidence interval: 0.95–0.99). In longitudinal analysis, the Cohen's Kappa was 0.61 (95% confidence interval: 0.39 to 0.83) indicating good agreement between the tests with sensitivity 0.63 (95% confidence interval: 0.39–0.82) and specificity 0.98 (95% confidence interval: 0.96 to 0.99).ConclusionThere is moderate-good agreement between signals of special cause variation between observed and risk-adjusted mortality. Analysis of observed hospital specific CABG mortality over time and with other hospitals appears to be useful in identifying special causes of variation. Case-mix adjustment may not be essential for longitudinal monitoring of outcomes using control charts.

Multiple retailer supplier performance: An exploratory investigation into using SPC techniques

The relationship between buyers and suppliers is the cornerstone of all supply networks. This exploratory investigation seeks to examine whether an existing approach developed for, and used extensively in the manufacturing quality milieu, could be adapted to the dyadic relationships found at the supply network interface between food multiple retailers and their suppliers. This investigation evaluates the concept of the use of control charts to examine supplier's delivery performance into the retailer's distribution centres and to relate this to on-shelf-availability in the retailer's stores. It was found that these control charts could be adapted to monitor the supplier/retailer interface and that the results could potentially be used to monitor and manage the buyer/supplier relationship effectively.

The role of control charts in promoting organisational learning

PurposeThe purpose of this paper is to examine the theoretical interface between statistical thinking, the statistical method of the control chart, and contemporary theories of organisational learning in regard to processes and their improvement.Design/methodology/approachTheoretical discussion results in an integrated model showing how statistical thinking and methods relate to organisational learning. This is supported by findings from a food industry research project following a design of: exploration (stage 1); theory development (stage 2); and theory testing/refinement (stage 3) incorporating surveys, case studies and key informant interviews.FindingsEmpirical evidence shows that statistical techniques such as the control chart can be of benefit to organisations for creating process improvement and organisational learning, providing the charts are utilised to actively convert the data they contain into information and knowledge about the process. Four distinct categories of use of control charts were observed which impacted on the effectiveness with which the charts were able to achieve this.Research limitations/implicationsThe findings have come from a study conducted only on the food industry. The implications are generalisable, however, to the wider industry context.Practical implicationsFindings illustrate problems with control chart application and the vital role of statistical thinking in ensuring that maximum benefit is derived from the charts. We argue that statistical thinking is a fundamental prerequisite to achieving effective double loop learning when using control charts as a basis for process monitoring and improvementOriginality/valueStatistical thinking and knowledge management are both growing areas of interest within the quality management and process improvement literature. The paper examines their interrelationship.