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Concept: Statistical process control


The application of Preventive Maintenance (PM) and Statistical Process Control (SPC) are important practices to achieve high product quality, small frequency of failures, and cost reduction in a production process. However there are some points that have not been explored in depth about its joint application. First, most SPC is performed with the X-bar control chart which does not fully consider the variability of the production process. Second, many studies of design of control charts consider just the economic aspect while statistical restrictions must be considered to achieve charts with low probabilities of false detection of failures. Third, the effect of PM on processes with different failure probability distributions has not been studied. Hence, this paper covers these points, presenting the Economic Statistical Design (ESD) of joint X-bar-S control charts with a cost model that integrates PM with general failure distribution. Experiments showed statistically significant reductions in costs when PM is performed on processes with high failure rates and reductions in the sampling frequency of units for testing under SPC.

Concepts: Statistics, Statistical significance, Quality, Probability, Official statistics, Process management, Process capability, Statistical process control


The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical process control (SPC) were used to evaluate the quality indicators of mechanized peanut sowing. The variables had normal distributions and were stable from the viewpoint of SPC. The best performance for peanut sowing density, normal spacing, and the initial seedling growing stand was found for clayey soil followed by sandy soil and then silty soil. Sandy or clayey soils displayed similar results regarding sowing depth, which was deeper than in the silty soil. Overall, the texture and the moisture of clayey soil provided the best operational performance for mechanized peanut sowing.

Concepts: Soil, Erosion, Silt, Sand, Clay, Loess, Statistical process control, W. Edwards Deming


The use of statistical process control (SPC) charts in healthcare is increasing. The general advice when plotting SPC charts is to begin by selecting the right chart. This advice, in the case of attribute data, may be limiting our insights into the underlying process and consequently be potentially misleading. Given the general lack of awareness that additional insights may be obtained by using more than one SPC chart, there is a need to review this issue and make some recommendations. Under purely common cause variation the control limits on the xmr-chart and traditional attribute charts (eg, p-chart, c-chart, u-chart) will be in close agreement, indicating that the observed variation (xmr-chart) is consistent with the underlying Binomial model (p-chart) or Poisson model (c-chart, u-chart). However, when there is a material difference between the limits from the xmr-chart and the attribute chart then this also constitutes a signal of an underlying systematic special cause of variation. We use one simulation and two case studies to demonstrate these ideas and show the utility of plotting the SPC chart for attribute data alongside an xmr-chart. We conclude that the combined use of attribute charts and xmr-charts, which requires little additional effort, is a useful strategy because it is less likely to mislead us and more likely to give us the insight to do the right thing.

Concepts: Scientific method, Control theory, Process control, Control system, Control engineering, Conservatism, Control chart, Statistical process control


A remarkable improvement in patient positioning was observed after the implementation of various process changes aiming to increase the consistency of patient positioning throughout the radiotherapy treatment chain. However, no tool was available to describe these changes over time in a standardised way. This study reports on the feasibility of Statistical Process Control (SPC) to highlight changes in patient positioning accuracy and facilitate correlation of these changes with the underlying process changes.

Concepts: Scientific method, Patient, Control theory, Process control, Control system, Control engineering, Process management, Statistical process control


Successful prevention of pressure ulcers is the end product of a complex series of care processes including, but not limited to, the assessment of vulnerability to pressure damage; skin assessment and care; nutritional support; repositioning; and the use of beds, mattresses, and cushions to manage mechanical loads on the skin and soft tissues. The purpose of this review was to examine where and how Statistical Process Control (SPC) measures have been used to assess the success of quality improvement initiatives intended to improve pressure ulcer prevention. A search of 7 electronic bibliographic databases was performed on May 17th, 2017, for studies that met the inclusion criteria. SPC methods have been reported in 9 publications since 2010 to interpret changes in the incidence of pressure ulcers over time. While these methods offer rapid interpretation of changes in incidence than is gained from a comparison of 2 arbitrarily selected time points pre- and post-implementation of change, more work is required to ensure that the clinical and scientific communities adopt the most appropriate SPC methods.

Concepts: Scientific method, Evaluation methods, Quality, Control theory, Process control, Control engineering, Process management, Statistical process control


Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data.

Concepts: Control theory, Process control, Control system, Controller, Control engineering, Process management, Process capability, Statistical process control


In this second article in the quality improvement (QI) methods series, we discuss how data are best displayed and analyzed in QI projects while focusing on some similarities with and differences from traditional clinical research. We demonstrate why displaying data over time on a run or control chart is superior to using pre-post analysis for QI studies. We introduce several types of statistical process control charts for data commonly collected during QI programs and provide guidance on how to use the proper chart. Last, we present solutions to several common data challenges in QI projects.

Concepts: Quality, Quality assurance, Control theory, Process control, Control chart, Process capability, Statistical process control, W. Edwards Deming


Although not all health care-associated infections (HAIs) are preventable, reducing HAIs through targeted intervention is key to a successful infection prevention program. To identify areas in need of targeted intervention, robust statistical methods must be used when analyzing surveillance data. The objective of this study was to compare and contrast statistical process control (SPC) charts with Twitter’s anomaly and breakout detection algorithms.

Concepts: Epidemiology, Infectious disease, Mathematics, Management, Control theory, Computer program, Process control, Statistical process control


The aims of this study were to investigate machine beam parameters using the TomoTherapy quality assurance (TQA) tool, establish a correlation to patient delivery quality assurance results and to evaluate the relationship between energy variations detected using different TQA modules. TQA daily measurement results from two treatment machines for periods of up to 4years were acquired. Analyses of beam quality, helical and static output variations were made. Variations from planned dose were also analysed using Statistical Process Control (SPC) technique and their relationship to output trends were studied. Energy variations appeared to be one of the contributing factors to delivery output dose seen in the analysis. Ion chamber measurements were reliable indicators of energy and output variations and were linear with patient dose verifications.

Concepts: Quality, Quality control, Management, Quality assurance, Control theory, Process control, Machine, Statistical process control


Purpose Due to increasing complexity, modern radiotherapy techniques require comprehensive quality assurance (QA) programmes, that to date generally focus on the pre-treatment stage. The purpose of this paper is to provide a method for an individual patient treatment QA evaluation and identification of a “quality gap” for continuous quality improvement. Design/methodology/approach A statistical process control (SPC) was applied to evaluate treatment delivery using in vivo electronic portal imaging device (EPID) dosimetry. A moving range control chart was constructed to monitor the individual patient treatment performance based on a control limit generated from initial data of 90 intensity-modulated radiotherapy (IMRT) and ten volumetric-modulated arc therapy (VMAT) patient deliveries. A process capability index was used to evaluate the continuing treatment quality based on three quality classes: treatment type-specific, treatment linac-specific, and body site-specific. Findings The determined control limits were 62.5 and 70.0 per cent of the χ pass-rate for IMRT and VMAT deliveries, respectively. In total, 14 patients were selected for a pilot study the results of which showed that about 1 per cent of all treatments contained errors relating to unexpected anatomical changes between treatment fractions. Both rectum and pelvis cancer treatments demonstrated process capability indices were less than 1, indicating the potential for quality improvement and hence may benefit from further assessment. Research limitations/implications The study relied on the application of in vivo EPID dosimetry for patients treated at the specific centre. Sampling patients for generating the control limits were limited to 100 patients. Whilst the quantitative results are specific to the clinical techniques and equipment used, the described method is generally applicable to IMRT and VMAT treatment QA. Whilst more work is required to determine the level of clinical significance, the authors have demonstrated the capability of the method for both treatment specific QA and continuing quality improvement. Practical implications The proposed method is a valuable tool for assessing the accuracy of treatment delivery whilst also improving treatment quality and patient safety. Originality/value Assessing in vivo EPID dosimetry with SPC can be used to improve the quality of radiation treatment for cancer patients.

Concepts: Cancer, Ionizing radiation, Radiation therapy, Radiobiology, Quality assurance, Control chart, Process capability, Statistical process control