Individual Control Charts Research Articles

Application of Quality Control Charts for Early Detection of Flood Hazards

Flooding is a serious and devastating hazard that many countries face regularly. Timely detection of these changes can help us manage rainwater and hence flooding. Numerous statistical tools are available for the early detection of these changes. In this regard, application of control charts is an effective choice toward monitoring natural events. In this study, we used self-proposed and existing control charts as a means of early detection of changes in the rainfall data of Pakistan as a case study. The proposed methodology covered two aspects: one to enhance the ability of Shewhart control charts toward detection of small or moderate changes in the behavior of natural events, and another to offer skewness correction based on individual control chart under runs rules for managing these natural events – especially when collected data follows unknown skewed distribution. Results elucidate that control charts structures were efficient in early detection and prediction of floods during 2010. Our results are in accordance with the theoretical results of existing studies, and we propose similar methods to be used for meteorological purposes.

Indirect impact of violent events on emergency department utilization and disease patterns

BackgroundThe health effects of war and armed conflict on casualties and mental health of those directly exposed has been well described, but few studies have explored the indirect health effects of violent events. This paper assesses the indirect health impact of several violent events that took place in Beirut in 2013–2014 on ED visit utilization and disease patterns.MethodsAs tracked by media reports, there were 9 violent events in Beirut during 2013–2014. We compared visits to the Emergency Department of a major medical center during weeks when violent events happened and weeks without such events (the preceding week and the same week in preceding years). After re-coding de-identified data from the medical records of 23,067 patients, we assessed differences in the volume of visits, severity index, and discharge diagnoses. Individual control charts were used to analyze ED visit trends post-event.ResultsComparisons of weeks with violent events and weeks without such events indicate that the socio-demographic characteristics of patients who visited the Emergency Department were similar. Patients seen during violent weeks were significantly more likely to be admitted to the hospital, and less likely to present with low acuity complaints, indicating greater complexity of their conditions. The discharge diagnoses that were significantly higher during violent event weeks included anxiety disorders, sprains, and gastritis. Daily ED visits dropped post events by 14.111%, p < 0.0001.ConclusionsThe results indicate that violent events such as bombs, explosions, and terrorist attacks reverberate through the population, impact patterns of ED utilization immediately post-event and are associated with adverse health outcomes, even among those who are not directly affected by the events.

Loading time monitoring via individual control charts

In quality control we find techniques that come from different fields, because the objective is to reduce the variability in quality characteristics, these are control and adjustment of processes. In this work, the individual control charts (Xind and Rm) were used to monitor the loading time of a truck measured during 60 consecutive days of a cargo transport company in Rio Grande do Sul. The results showed that the process is out of control both in average and variability, requiring a systematic monitoring in order for the company to maintain the quality of services provided to its customers.

2273-PUB: Translational Analytics Clustering Type 2 DM Patients Treated on Wholesome Millet in Reducing HbA1c

Aim: To study the effect of whole grain millet based food in type 2 DM patients on HbA1c reduction. Methodology: The contexts of the datasets of patients were studied as they arrive at Coimbatore Diabetes Foundation for over a period of 3 years, were classified based on initial visit according to the HbA1c results. High risk &amp;gt; 8%, moderate risk 7% ≤ 8% and low risk &amp;lt; 7%, among 100 patients, were further clustered (as 1,2 and 3 containing 41, 49 and 10 patients respectively) based on age, BMI, FBS, PPBS, and HbA1c, using K-Mean cluster. The translational analytics focus on the conclusions of the datasets from the passive deduction scope to active prescriptive scalability for real time practice on the effect of wholesome millet diet (WMD), millets are small seeded grasses grown as a cereal crops. This approach of real time application of clinical practice in fundamental research that can be easily practiced for treatment of DM, despite the natural limitation of hereditary and aging process. The patients who were following WMD were tested for HbA1c periodically. The Translational analytics: The Interval Plot and individual control chart with the analysis of variance examines the significance of the reduction in the average level of HbA1c% for each cluster. Findings: 1. Significant reduction of HbA1c between first and second visit from 11.7 to 7.4% (at P value = 0) for cluster group 3 after a lapse of 106 days. This was sustained in the 4th visit up to 480 days 2. In cluster 2 the average reduction from 8.9 to 7.1% in the interval of 79 days and further significant reduction was visible in 4 th visit at 6.6% (at P value 0) up to 335 days. 3. In cluster 1 the significant reduction of HbA1c was noticed at the fourth visit at 6.8% from 7.4% after a lapse of 361 days (P value at 0.007). Disclosure S. Vairamuthu: None. B. Sekar: None. S. Marappan: None. R. Ananthasubramani: None.

Parameters affecting the mechanical digging of peanut crops from three different shaped plots

The mechanical harvesting efficiency of agricultural crops affects operational quality and production costs while being dependent on plot shape, operation planning, and management. Therefore, this study evaluated the operational performance of a mechanical tractor-digger set in the digging of peanut crops planted in different shaped plots. The three trapezoidal, triangular and irregular shaped plots had linear lengths of 812 m (P1), 322 m (P2) and 248 m (P3), respectively. The parameters, effective and operational field capacities; digging and management efficiencies; maneuvering and machine downtimes, including the operator personal needs and displacement in the tracks were determined and used to obtain operational efficiency. The yield of the experimental rural area, in Barrinha, SP, is 3200 kg of peanut ha-1. The experimental design followed the assumptions of statistical process control, and individual value control charts were plotted for each studied variable. The digging operation was monitored and divided into machine downtime, operational time, maneuvering time, and total time. These parameters were then used to calculate the operational capacities and efficiencies of the harvesting operation. It is concluded that plot size and shape affect the operational capacity. Effective and operational capacities are higher in longer plots, lengthwise, while maneuvering times are also shorter. Shorter plots result in digging efficiency losses. Give the best or optimal condition based on results.

A New Approach to Statistical Process Control: Identification of Outliers in Yield Maps

The tools of precision agriculture are of utmost importance in the Brazilian agribusiness, enabling increases in yields and reducing production costs. The use of harvest monitoring systems makes it possible due the possibility to identify pontual problems in an area, however, it becomes necessary to be working properly so it does not acquire incorrect information. Therefore, the purpose with this study was to propose a new approach to identify discrepant points in harvesting maps using statistical process control, as well as to define the best multiple of the standard deviation to identificate these points. The work was conducted during the soybean harvesting at São Geronimo farm in an area of 38 hectares in the municipality of Candido Mota, located in the the state of São Paulo. For gathering information, it was used a Stara crop monitoring system (model Topper Maps) set to record information during harvest in each three second. The productivity data were used to generate an individual control chart to identify points that were out of control so they could be removed. Two standard deviation multiples, that presented an average productivity closer to the average real productivity of the area, were selected. The multiples of the deviations that came closest were the 2σ and 3σ. Two multiples of standard deviation presented an average yield closer to the average real yield of the area. Individual control charts can be used to set control limits and identify possible discrepancies. The multiple of standard deviation 3σ presented information with greater reliability.

Leakage Monitoring in Static Sealing Interface Based on Three Dimensional Surface Topography Indicator

Leakage directly affects the functional behavior of a product in engineering practice, and surface topography is one of the main factors in static seal to prevent leakage. This paper aims at monitoring the leakage in static sealing interface, using three-dimensional (3D) surface topography as an indicator. The 3D surface is measured by a high definition metrology (HDM) instrument that can generate millions of data points representing the entire surface. The monitoring approach proposes a series of novel surface leakage parameters including virtual gasket, contact area percentage (CAP), void volume (VV), and relative void volume (SWvoid) as indicators. An individual control chart is adopted to monitor the leakage surface of the successive machining process. Meantime, based on the Persson contact mechanics and percolation theory, the threshold of leakage parameter is found using finite element modeling (FEM). Experimental results indicate that the proposed monitoring method is valid to precontrol the machining process and prevent leakage occurring.

Statistical Process Control pada Pengujian Kestabilan Alat Pengukur Kadar Glukosa Darah Non-Invasif

Stability of a measuring tool is very important. It is necessary to get measurement results that have high precision. This research was conducted to see the stability of Non-Invasive Blood Glucose Measurement Tool. Data of this research is data research from "Pengembangan dan Uji Klinis Purwarupa Alat Pemantauan Kadar Glukosa Darah Non-Invasif Institut Pertanian Bogor". This research used analysis of statistical process control using Individual Moving Range control chart. The results obtained indicate stability process on each respondent. However, among respondents who are one with other respondents has an unstable process despite glucose levels the blood of the respondents is the same.

Detection and monitoring of defects on three-dimensional curved surfaces based on high-density point cloud data

The surface quality of three-dimensional (3-D) curved surfaces is one of the most important factors that can directly influence the performance of the final product. This paper presents a systematic approach for detection and monitoring of defects on 3-D curved surfaces based on high-density point cloud data. Firstly, an algorithm to remove outliers and a boundary recognition algorithm are proposed to divide the entire 3-D curved surface including millions of measured points into multiple sub-regions. Secondly, two new evaluation indexes based on wavelet packet entropy and normal vector are explored to represent the features of the multiple sub-regions to determine whether the sub-regions are out-of-limit (OOL) of specifications. Thirdly, three quality parameters representing quality characteristics of a curved surface are presented and their values are calculated based on the clusters of OOL sub-regions. Finally, three individual control charts are presented to monitor the three quality parameters. As long as any quality parameter is out of the control range, the manufacturing process of the curved surface is determined to be out-of-control (OOC). The results of a case study show that the proposed approach can effectively identify the OOC manufacturing process and detect defects on 3-D curved surfaces.

Use of control charts with regression analysis for autocorrelated data in the context of logistic financial budgeting

The aim of this paper is to explore whether the use of control charts with regression analysis is an effective way to evaluate financial budget requests (autocorrelated data) in the transport logistics sector. First, the variables are selected. Second, a regression analysis is performed to model the financial variables. Third, three types of traditional control charts are tested (individuals, CUSUM and EWMA), using simulation to monitor the regression scaled residuals. The results show that the individual control chart of 2.7-sigma offers an appropriate performance for the context of this study. This paper provides new evidence regarding a type of variable and context not reported in the literature. In addition, it proposes a control chart approach of scaled regression residuals, with two differentiators: (1) residuals offer better practical interpretation and (2) regressions do not incorporate the time variable, as traditionally occurs, but a missionary process variable (loading units) and a control one.

Combining process and product information for quality improvement

This paper considers a two-step monitoring scheme that is used to jointly monitor process and product characteristics for manufacturing quality improvement. The two-step monitoring scheme uses an X¯ chart to continuously monitor the mean of the process characteristics. When the process sample mean falls outside of control limits, a warning signal triggers an np chart to check product quality. If the number of nonconforming products exceeds a pre-determined threshold, corrective actions are initiated to restore the manufacturing process back to normal. Otherwise, the process will continue with X¯ chart monitoring individually. We derive an economic model for the two-step monitoring scheme, which considers general cases where sampling costs for both process and product quality characteristics are involved. It is shown that the two-step monitoring scheme includes the traditional process and product control charts as special cases. We compare the two-step monitoring scheme with the individual process and product control charts, respectively, in terms of economic efficiency. Analytical conditions are derived under which the two-step monitoring scheme is preferred against the individual charts in practice. Numerical examples are provided to demonstrate the effectiveness and sensitivity of the comparison.

A control-chart-based queueing approach for service facility maintenance with energy-delay tradeoff

Maintenance planning and energy consumption control are critical issues in facility operations management. In practice, the energy consumption of a facility, which will be affected by the operation condition, is closely connected with the associated maintenance policy. Specifically, for an energy-consuming service system, though a frequent maintenance activity can keep the facility in a good condition with low energy consumption, it makes the delay time longer and leads to a poor customer experience. In this paper, we study a single-server queueing system with different energy consumption levels in the associated running states to address the conflict between energy consumption and customer delay. Two types of maintenance activities are implemented for the server, i.e., the planned maintenance and the reactive maintenance. The planned maintenance is adopted based on a frequency parameter at the beginning of an idle period, and the reactive maintenance is initialized by the Shewhart’s individual control chart (condition-based maintenance). To capture the energy-delay tradeoff, our objective is to develop an optimal maintenance policy that minimizes the long-run expected total cost of the system under a customer waiting time constraint. Numerical experiments are conducted to analyze the problem, in which useful managerial insights are obtained for the optimal maintenance policy. The results demonstrate the robustness of the proposed maintenance model, its advantage over the model without control chart, and its applicability in general situations.

Analysis and control of variability by using fuzzy individual control charts

The detection of changes in a process within shortest time provides significant benefits in terms of cost and quality. When considering the cost which would show up because of delays in identifying variability, detecting the deviation in the process accurately and quickly has a great importance for investors. In this paper, return volatility in the Borsa Istanbul-30 index (BIST-30) has been analyzed and a fuzzy control chart for individual measurements (FCCIM) has been proposed for use in determining and controlling in the variables of the BIST-30 index. For this purpose, firstly exponential smoothing method is used to forecast the variability of stock price of BIST-30 index by using MINITAB statistical software, and then a fuzzy control chart for individual measurements (FCCIM) which are fuzzy individual control chart (FICC) and fuzzy moving range control chart (FMRCC) with fuzzy control rules have been developed to be used in determining the variability of the process. For this aim, some fuzzy rules have been defined by using Ms EXCEL in fuzzy control chart for individual measurements. A real case application from Istanbul Stock Exchange for BIST-30 has been managed to check the effectiveness of suggested fuzzy control charts.

Drinking water quality control: control charts for turbidity and pH

Water treatment processes are required to be in statistical control and capable of meeting drinking water specifications. Control charts are used to monitor the stability of quality parameters by distinguishing the in-control and out-of-control states. The basic assumption in standard applications of control charts is that observed data from the process are independent and identically distributed. However, the independence assumption is often violated in chemical processes such as water treatment. Autocorrelation, a measure of dependency, is a correlation between members of a series arranged in time. The residuals obtained from an autoregressive integrated moving averages (ARIMA) time series model plotted on a standard control chart is used to overcome the misleading of standard control charts in the case of autocorrelation. In this study, a special cause control (SCC) chart, also called a chart of residuals from the fitted ARIMA model, has been used for turbidity and pH data from a drinking water treatment plant in Samsun, Turkey. ARIMA (3,1,0) for turbidity and ARIMA (1,1,1) for pH were determined as the best time series models to remove autocorrelation. The results showed that the SCC chart is more appropriate for autocorrelated data to evaluate the stability of the water treatment process, since it provides a higher probability of coverage than an individual control chart.

A Neural Network based Individual Control Chart

A Neural Network based Individual Control Chart

Improving Emergency Department Door to Doctor Time and Process Reliability: A Successful Implementation of Lean Methodology.

The aim of this study is to determine the effectiveness of using lean management methods on improving emergency department door to doctor times at a tertiary care hospital.We performed a before and after study at an academic urban emergency department with 49,000 annual visits after implementing a series of lean driven interventions over a 20 month period. The primary outcome was mean door to doctor time and the secondary outcome was length of stay of both admitted and discharged patients. A convenience sample from the preintervention phase (February 2012) was compared to another from the postintervention phase (mid-October to mid-November 2013). Individual control charts were used to assess process stability.Postintervention there was a statistically significant decrease in the mean door to doctor time measure (40.0 minutes ± 53.44 vs 25.3 minutes ± 15.93 P < 0.001). The postintervention process was more statistically in control with a drop in the upper control limits from 148.8 to 72.9 minutes. Length of stay of both admitted and discharged patients dropped from 2.6 to 2.0 hours and 9.0 to 5.5 hours, respectively. All other variables including emergency department visit daily volumes, hospital occupancy, and left without being seen rates were comparable.Using lean change management techniques can be effective in reducing door to doctor time in the Emergency Department and improving process reliability.

APPLICATION OF THE GROSS ERROR ANALYSIS TO DISORDERS IDENTIFICATION IN MANUFACTURING PROCESS—A CASE STUDY

The article contains a description of the procedures to be followed in the analysis of the stability of the manufacturing processes based on an Individual Control Chart (XI) and a Moving Range (MR) Control Chart. In the following part of the article, we present the three-sigma rule and the confidence intervals for the average, which enable the elimination of random errors caused by, e.g. an improper conducting of a measurement.Summary of the theoretical considerations is a study case in which survey data from a company, producing car seat frames, are used. The conducted study indicates that the elimination of Gross Error is significant to identify and eliminate the variability of production processes.

Inventory Control System Based on Control Charts to Improve Supply Chain Performances

Inventory replenishment rules have been recognized as a major cause of the bullwhip effect and inventory instability in multi-echelon supply chains. There is a trade-off between bullwhip effect and inventory stability where mitigating the bullwhip effect through order smoothing might increase inventory instability. Therefore, there is a substantial need for inventory control policies that can cope with supply chains dynamics. This paper attempts to formulate an inventory control system based on a statistical control chart approach to handle the trade-off between order variability amplification and inventory stability. The proposed replenishment system, namely IR-SPC, incorporates individual control charts to control both the inventory position and the placed orders. A simulation study has been conducted to evaluate and compare the IR-SPC with a generalized order-up-to that has order smoothing mechanism. The comparisons showed that the IR-SPC outperforms both the smoothing order-up-to policy and the Min-Max inventory policy in terms of bullwhip effect and inventory performances. (Received in June 2013, accepted in January 2014. This paper was with the authors 1 month for 2 revisions.)

Bernoulli Cumulative Sum (CUSUM) control charts for monitoring of anesthesiologists' performance in supervising anesthesia residents and nurse anesthetists.

We describe our experiences in using Bernoulli cumulative sum (CUSUM) control charts for monitoring clinician performance. The supervision provided by each anesthesiologist is evaluated daily by the Certified Registered Nurse Anesthetists (CRNAs) and/or anesthesia residents with whom they work. Each of 9 items is evaluated (1 = never, 2 = rarely, 3 = frequently, 4 = always). The score is the mean of the 9 responses. Choosing thresholds for low scores is straightforward, <2.0 for CRNAs and <3.0 for residents. Bernoulli CUSUM detection of low scores was within 50 ± 14 (median ± quartile deviation) days rather than 182 days without use of CUSUM. The true positive detection of anesthesiologists with incidences of low scores greater than the chosen "out-of-control" rate was 14 of 14. The false-positive detection rate was 0 of 29. This CUSUM performance exceeded that of Shewhart individual control charts, for which the smallest threshold sufficiently large to detect 14 of 14 true positives had false-positive detection of 16 of 29 anesthesiologists. The Bernoulli CUSUM assumes that scores are known right away, which is untrue. However, CUSUM performance was insensitive to this assumption. The Bernoulli CUSUM assumes statistical independence of scores, which also is untrue. For example, when an evaluation of an anesthesiologist 1 day by a CRNA had a low score, there was an increased chance that another CRNA working in a different operating room on the same day would also give that same anesthesiologist a low score (P < 0.0001). This correlation among scores does affect the Bernoulli CUSUM, such that detection is more likely. This is an advantage for our continual process improvement application since it flags individuals for further evaluation by managers while maintaining confidentiality of raters.

Individual T2 Control Chart for Compositional Data

The usual Hotelling T2 control chart is not appropriate for monitoring processes where the quality characteristic is a mixture. The composition of mixtures are vectors of positive elements that represent parts of a whole, to which standard multivariate techniques are not appropriate due to their restricted sample space. There are many applications where a mixture is monitored against time, such as in the chemical industry, product composition, impurity profile, or gas components analysis. In this paper, a multivariate control chart for individual compositional observations based on the T2 statistic is proposed and compared with the typical one in terms of average run length. We show how results are more consistent with compositional data nature and illustrate implementation in a real-world example.