STABILITY ANALYSIS OF THE TECHNOLOGICAL PROCESS OF MECHANICAL WOOD PROCESSING USING CONTROL CHARTS

Background Screening for sexually transmitted infections (STIs) such as gonorrhea, chlamydia, syphilis and hepatitis C (among men who have sex with men) are part of core performance measures and a reporting requirement for every Ryan White HIV/AIDS program. Dartmouth Health's Ryan White HIV program screening rates for these STIs as of August 2022 were ranging from 38% to 44%. The aim of this quality improvement initiative was to improve the screening rates to 80% by June 2024.Figure 1DH-Bedford Clinic Process Flow MapHighlighted in green are parts of the process where observation of clinical encounter and retrospective chart review were conducted. Methods We used principles of models for improvement recommended by Institute of Healthcare Improvement (IHI), clinical microsystem approach and other quality improvement (QI) techniques in this project. The project had two phases. The initial phase comprised of an audit of the program through observation of clinical encounters, semi-structured interviews and retrospective chart review, while the implementation phase involved three quality interventions: (1) Presentation of initial chart audit findings and educational session about STI guidelines, (2) Use of STI screening cards (main intervention), (3) Practice feedback and regular updates. Analysis and display of data were presented using statistical control process (SPC) charts to identify any significant changes in the system or processTable 1Summary of the Findings of Clinical Encounter Observation and Semi-structured Interviews to the Providers Results From August 2022 to March 2024, we increased our screening rates for gonorrhea from 38% to 66%, chlamydia from 38% to 62%, syphilis from 44% to 64% and hepatitis C (among MSM) from 39% to 82%. Several favorable special cause variations were observed in all outcome measures after the implementation phase.Figure 2p chart for Patients Being Screened for Gonorrhea This figure illustrates the proportion of patients seen in clinic each month who are screened for gonorrhea at least annually. The mean screening rates is 48%. The upper control limit is 62% while the lower control limit is 34%. There are two special cause variations (2 data points outside the UCL - red circle). Conclusion The use of quality improvement principles and methods to routinize sexual history taking can improve screening rates for sexually transmitted infections among adult patients in a Ryan White HIV program. Incorporation of the questions through the EMR is the next step for it to become more sustainable.Figure 3p chart for Patients Being Screened for Syphilis This figure illustrates the proportion of patients seen in clinic each month who are screened for syphilis at least annually. The mean screening rates is 53%. The upper control limit is 67% while the lower control limit is 39%. There are two special cause variations. First is upward trend (green circle) from 8/2023 to 2/2024. The second is a point outside the UCL in 2/2023. Disclosures All Authors: No reported disclosures

The problem faced by the Bmrsign Wallgallery company is that products are found that do not match the specifications set by the company. The types of product damage are wrong size frames, broken frames, and uneven corners. To solve this problem, the SQC method is used to reduce the causes of defects in the product. The purpose of this method is to reduce the risk of damage to the product. The object studied was Bmrsign Wallgallery with a sample of 29 days. The use of the SQC method is accompanied by analysis tools in the form of control charts (p-charts), Pareto diagrams, cause and effect diagrams (fishbone). Based on the results of data analysis implementing the Statistical Quality Control (SQC) method using the P-Chart control map, the largest value of the Upper Control Limit (UCL) can be shown as 0.272, while the Lower Control Limit (LCL) is -0.013. The results show that the number of defective products during the frame making process is still within normal control limits. The analysis using the Pareto diagram shows that the types of frame defects are dominated by broken with a percentage of 35% or a total of 66 pcs, inappropriate size shapes as much as 33% or a total of 62 pcs, and uneven corners as much as 32% or a total of 60 pcs. Based on the results of the fishbone diagram analysis, defective products are caused by workers who are not focused, work standards are not adhered to, machine maintenance is not given attention, and there is a decrease in the quality of raw materials.

Improving product quality is one of the important factors in facing industrial competition, especially in the White Crystal Sugar (GKP) production sector. PT XYZ, as one of the GKP producers, faces challenges in maintaining the consistency of its product quality according to applicable standards. This study aims to assess the quality of the production process at PT.XYZ by utilizing the Statistical Quality Control (SQC) method through the use of a p-control map (P-Chart) to measure the proportion of defective products. The analysis was conducted on 29 production samples. The results show that the production process is in statistical control, with an average defect proportion of 0.006984, where all points are between the upper control limit (UCL = 0.008769) and the lower control limit (LCL = 0.005199). The defect rate found is very small, less than 0.1%. The total defects reached 3279 kg GKP, consisting of color defects of 1155 kg (35.22%), moisture content of 1091 kg (33%), and cleanliness of 1033 kg (32%). Based on the fishbone diagram, the defects were caused by workers, machines, operational methods, and production materials. In conclusion, the application of the SQC method is effective in identifying and controlling factors that affect the quality of GKP at PT XYZ.

Consider a mass production line for a single “part” and let us center our interest on a single “dimension” of the part. There will always be some variability in the dimension because of the sum of several variables whose causes are not usually understood and whose effects might not be controllable if they were. The dimension of the part is thus a random variable, but one whose mean and variance can change with time as something goes wrong with the “machinery” producing the part. If the process is stable and only the “usual” system of chance causes is operating (which implies that the mean θ and variance σ2 of the process are constant), we say that the process is in statistical control and that it has an inherent variability σ2. If the mean or variance wanders from those stable values, we say that an assignable cause is operating, meaning that it can be understood/controlled. The term quality control refers to control of quality by any means, but statistical quality control refers to the control of quality through the use of certain statistical tools to be described shortly. The best known of those tools is the Shewhart Control Chart, which is used to demonstrate statistical control and to detect the presence of assignable causes. There are control charts for the mean, range, standard deviation, fraction defective, and number of defects per unit. Three horizontal lines plus the data make up the control charts. The 3 lines are a centerline, an upper control limit, and a lower control limit. (One of the control limits may be omitted if there is no interest in it.) The centerline is an “average” of the statistic being plotted, and the control limits are at distances of 3 standard deviations (of the statistic) above and below the centerline.

Wheat flour is a cooking ingredient made from whole wheat which undergoes processing into white powder or very fine granules. Many types of food use wheat flour as the main ingredient in the manufacturing process, such as in the manufacture of bread, noodles, and others. Therefore, wheat flour must go through many stages of processing and processing from wheat seeds to produce quality flour. One of the companies or factories that process flour into flour is PT. Cerestar Flour Mills is located in the Mabar area or the Medan industrial area (KIM). Business developments are moving very fast in the current industrial era, companies must be able to produce quality products. Therefore Statistical Quality Control is one of the company's ways to control quality in management. The average value that becomes the center line of the control chart ash content in wheat flour is 0.53 with the upper control limit (BPA) = 0.53 and the lower control limit (BPB) = 0.50. The average value of the Range is 0.015 with the upper control limit (BPA) = 0.029 and the lower control limit (BPB) = 0.00. Thus all data is within the control line, meaning that the results are in accordance with the control standard.

A statistical weld process monitoring system is described. Using data collected while welding, the welding statistical process control (SPC) tool provides weld process quality control by implementing techniques of data trending analysis, tolerance analysis, and sequential analysis. The SPC system computes the mean, standard deviation, and range of each of the parameters sampled by the data collection system. Changes in the mean, standard deviation, and range are displayed using control (or trend) charts. The control chart displays a function of a parameter with respect to the ordering of the weld records (for a single weld) or weld number (for multiple welds). The SPC tool also permits plotting tolerance charts of the mean, standard deviation, and range for each of the sampled parameters. The tolerance chart is plotted versus the record number (or weld number) and consists of a vertical line for each record (or weld number) showing the minimum and maximum value of that parameter for that record (or weld number). The upper control limit (UCL), lower control limit (LCL), and nominal value may also be displayed on the tolerance chart printout. The SPC also performs sequential analysis, which allows the user to examine the process as it goes along, which in turn may permit the user to locate a possible change in the process before it goes out of control. Work directed toward developing an expert interpreter of the voluminous statistical output generated by the SPC is also described.

The aim of this research is to analyze the production quality control policy at PT Glory Industrial in Sragen, to identify the factors that cause damage and loss to products produced by PT Glory Industrial in Sragen. This research is included in the type of case study research at PT Glory Industrial in Sragen by conducting a survey to the company. This research only covers production quality control at PT Glory Industrial in Sragen. Product quality control uses the Statistical Process Control method. Starting with determining quality standards then production results, then damaged production is analyzed through quality control using Statistical Process Control, so that you can receive analysis results that can be useful for PT Glory Industrial in Sragen. The results of the research show that the implementation of product quality control at PT Glory Industrial has not been under control, this is proven by the production process that there were 4 points that exceeded the upper control limit (UCL) and lower control limit (LCL). Occurred on the months February (0,023), June (0,084), September (0,022) and November (0,075). From this data, it can be seen that there are still those who exceed the control limits, thus indicating that quality control has not been implemented properly and improvements are needed to overcome these deviations. Thus H1 is proven correct, there are 6 types of damage that occur at PT Glory Industrial, namely looseness, broken fabric, embossed threads, skipped threads, damaged stitching and wrong labels. Based on the causes and effects, the factors that cause damage are human factors, machines, materials and work methods. Thus H2 is proven correct.

The need for exchange transfusion (ET) as a treatment modality for neonatal hyperbilirubinaemia declined in developed countries with the advent of effective phototherapy. The trends of ET from India are unknown. Our objective was to investigate the trends of ET in India. Retrospective data (January 2006-December 2016) was collected on total outborn neonatal admissions and ET procedures from a centre in north India. A combination of change-point analysis (CPA) and statistical process control (SPC) was used to investigate the trends of ET. During the study period, a total of 39 217 outborn neonates were admitted and 1575 (4%) underwent 1816 ET procedures. The CPA unravels four critical change points (October 2009, May 2011, September 2011 and November 2014) in ET rates. An SPC chart showed a decline in mean ET rate from 89.3 (upper control limit [UCL] 176.9, lower control limit [LCL] 1.7)/1000 neonatal admissions at the start of the study to 7.7 (UCL 34.6, LCL 0)/1000 at the end of the study. The greatest decline in ET rate was witnessed in October 2009, from 89.3 (UCL 176.9, LCL 1.7)/1000 neonatal admissions to 34.8 (UCL 87.1, LCL 0)/1000 neonatal admissions. Our study demonstrated a progressive decline in the number of neonatal ET procedures over 11 y.

BackgroundLoss to follow-up is an under-recognised problem in primary care. Continuity with a primary care provider improves morbidity and mortality in the Veterans Health Administration. We sought to reduce the...

Purpose:To evaluate the sensitivity of gamma criteria for patient‐specific volumetric modulated arc therapy(VMAT) quality assurance of the Delta4 dosimetry program using the statistical process control(SPC) methodology.Methods:The authors selected 20 patient‐specific VMAT QA cases which were undertaken MapCHECK and ArcCHECK with gamma pass rate better than 97%. The QAs data were collected Delta4 Phantom+ and Elekta Agility six megavolts without using an angle incrementer. The gamma index(GI) were calculated in 2D planes with normalizing deviation to local dose(local gamma). The sensitivity of the GI methodology using criterion of 3%/3mm, 3%/2mm and 2%/3mm was analyzed with using process acceptability indices. We used local confidence(LC) level, the upper control limit(UCL) and lower control limit(LCL) of I‐MR chart for process capability index(Cp) and a process acceptability index (Cpk).Results:The lower local confidence levels of 3%/3mm, 3%/2mm and 2%/3mm were 92.0%, 83.6% and 78.8% respectively. All of the calculated Cp and Cpk values that used LC level were under 1.0 in this study. The calculated LCLs of I‐MR charts were 89.5%, 79.0% and 70.5% respectively. These values were higher than 1.0 which means good quality of QA. For the generally used lower limit of 90%, we acquired over 1.3 of Cp value for the gamma index of 3%/3mm and lower than 1.0 in the rest of GI.Conclusion:We applied SPC methodology to evaluate the sensitivity of gamma criteria and could see the lower control limits of VMAT QA for the Delta 4 dosimetry and could see that Delta 4 phantom+ dosimetry more affected by the position error and the I‐MR chart derived values are more suitable for establishing lower limits.Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2015R1D1A1A01060463)

The level of consumption and the number of coffee processing industries is increasing so that competitiveness is also getting higher. Product quality is a factor that affects business competitiveness, but Partungkoan Tarutung Coffee as a ground coffee processing industry still find product defects in the production process. This study aims to determine product quality control. This research method used descriptive qualitative with case study research techniques. The analytical tools used were descriptive statistics and Statistical Process Control (SPC). Sampling using purposive judgment sampling technique to determine respondents that consists of business owners and daily production workers and thirty consumers of UMKM Coffee Partungkoan Tarutung. The results showed that the product quality control was under control because it was within the upper control limit (UCL) and the lower control limit (LCL)

In some production and administrative processes, the occurrence of certain events is best described by a hyper-geometric distribution, which in turn should be pictorially depicted by what should be called a “hyper-geometric chart (Hg-chart)” in the field of Statistical Quality Control (SQC). However, this has never been the practice, since the existence of such a chart is absent; as such, prompting administrators and process engineers to make use of already existing charts for approximately depicting hyper-geometric processes. In this article, an SQC chart for any hyper-geometric process has been developed for the total number of events in a fixed number of units. This chart has been referred to as the Hg-chart. The center line (CC), lower control limit (LCL) and the upper control limit (UCL) have been obtained for the proposed chart with a sketch of how the proposed chart should be if used for simulation. It has been recommended that simulation should be used to test the proposed chart as this could prove to be more efficient and appropriate for describing hyper-geometric data rather than using an inappropriate chart to be an approximation for solving the problem.

Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic‐γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic‐γ charts do not differentiate between the points falling outside the upper control limit (UCL) and lower control limit (LCL). Hence, this paper proposes a side‐sensitive synthetic‐γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL. Formulae to compute the average run length (ARL), the standard deviation of the run length (SDRL) and expected average run length (EARL) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side‐sensitive synthetic‐γ chart consistently outperforms the existing synthetic‐γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart‐γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.

Objective.Establishing control and specification limits for volumetric modulated arc therapy (VMAT) pre-treatment quality assurance (PTQA) is essential for streamlining PTQA workflows and optimizing plan complexity. This study aimed to develop and implement new methods and tools across treatment sites of varying complexity using multiple global and local gamma index criteria.Approach.350 VMAT plans comprising brain, prostate, pelvis and head and neck treatments were retrospectively compiled. For each site, control limits were obtained using statistical process control (SPC) along with heuristic methods (scaled weighted variance (SWV), weighted standard deviation (WSD), skewness correction (SC)). Specification limits were derived employing a new formalism aligned with the heuristic approaches. Calculations were performed under various global and local gamma index criteria using custom-built software (freely available athttps://github.com/AEvgeneia/SPC_GUI_Scientific_Tool.git).Main results.WSD and SC control and specification limits were comparable, while SWV deviated with increasing complexity and stricter gamma index criteria. Conventional criteria (e.g. global 3%/2 mm) lacked sensitivity to detect subtle errors. Global 2%/1 mm and 1%/2 mm, and local criteria stricter than 3%/1 mm, met sensitivity requirements for low-complexity plans while maintaining clear separation between control and specification limits to identify plans with suboptimal delivery accuracy. High-complexity plans showed that global criteria stricter than 3%/1 mm and all evaluated local criteria are optimal, provided specification limits for the most stringent criteria remain clinically acceptable.Significance.A nuanced framework is provided for determining control and specification limits for gamma index passing rates, as well as corresponding thresholds for the mean gamma index, allowing for site-specific detection of suboptimal treatment plans. The open-source software tool developed can operationalize the proposed methodology facilitating the clinical adoption of advanced statistical methods. Site-specific thresholds could serve as inputs for machine learning and deep learning algorithms aimed at automating error detection and PTQA classification for plan complexity management.

Supplementary run‐rules have been used since the 1950s to improve the performance of the basic Shewhart control chart. A procedure known as Klein's 2‐of‐2, a simple and efficient supplementary rule, signals that the process is out‐of‐control only when two sequential sample averages are observed above the upper control limit (UCL) or below the lower control limit (LCL). The use of this rule results in a substantial reduction of the average run length (ARL) in detecting small process shifts when compared to the traditional control chart. However, it presents a poor performance of the out‐of‐control ARL for moderate to large process shifts (the converges to 2 for large shifts in the mean). In order to minimize this issue, some authors introduce additional control limits to delimit decision zones in relation to deciding whether the process is in control or out‐of‐control. Despite a better performance for moderate‐to‐large process shifts, the process complexity is also higher. In this paper, we include a simple procedure (with no additional control limits) in relation to the 2‐of‐2 rule by examining if all values of each unit of the sample are beyond the control limits whenever its average is beyond the control limits. The new procedure has a similar performance in terms of ARL for small shifts in the mean and a better performance for moderate‐to‐large shifts (the now converges to 1) when compared to the standard 2‐of‐2 rule (where the converges to 2).