Quality Control and Acceptance Sampling

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.

This paper presents a quality control tool, that is, a quality control method, which in this case concernsthe mechanical processing of wood, but in general, it can be implemented in any form of production (mechanicalengineering, traffic, food technology...). The goal of the research is to show the way of applying the control chartmethod, that is, to determine the stability of the technological process of mechanical wood processing in order to beable to define appropriate corrective measures in order to improve that part of the production process. The controlchart that was created in this paper is a graph consisting of a horizontal (abscissa) and a vertical axis (ordinate). Theorder of observations is plotted on the abscissa, and the corresponding values on the ordinate. Apart from theprecise creation of control charts, their correct interpretation is also of great importance. It is easiest to interpretcontrol charts where the process is "outside statistical control", that is, the process where the values of individualmeasurements are outside the control limits. This means that some special cause of variation is present in the processand an adjustment needs to be made. However, placing all points within the control limits, that is, obtaining acontrol map on which the process is "within statistical control", does not necessarily mean that such a process isacceptable by statisticians. On the basis of the results, that is, on the basis of the graph that has the shape of a brokenline, we observe whether the control limits have been exceeded, and we draw certain conclusions about the stabilityof the process. If the graph is between the control limits, we can conclude that our process is stable and undercontrol, that is, that our quality variation is within normal limits. If there are any jumps, ie. if some value in someobservation is out of bounds (above the upper control limit or below the lower control limit), then we conclude thatour process is not stable, not under control and that our quality variation is not within normal limits. In that case,certain corrective measures were proposed, which should improve the technological process of mechanical woodprocessing and bring it under control.Control charts, as an engineering-mathematical tool, are a very suitable tool for achieving the goals of statisticalcontrol. Optimal application of control charts ensures constant monitoring of the process, direction towards thedesired flow and undertaking of possible corrective measures. Also, their application ensures the fulfillment of thedesigned or required quality characteristics, that is, the fulfillment of the requirements of a certain standard. Withtheir simplicity of application and reliability, they provide users with convenience in work and relevant processindicators, and provide process controllers with valid data about the process flow. As such, they represent a tool thatkeeps the entire process within the given limits.

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.

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.

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.

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.

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

Statistical process control (SPC) is the method that monitors process quality characteristics. Through control charts, one can detect whether the present process malfunction. However, some annoyances may arise while the engineers need to choose appropriately control chart under different levels of process variation. The Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been widely used for monitoring semiconductor manufacturing processes. Generally weighted moving average (GWMA) control chart is a new method of SPC, which was proposed by Sheu and Lin (2003). The main objective of this research is using step-by-step procedures to present a comparative study of the monitoring performance for Shewhart, CUSUM, EWMA and GWMA control charts. According to a specified in-control average run length (ARL), we determine the parameters of each control chart based on the Monte Carlo numerical simulation. The setting parameters in each control chart sre displayed and tabulated. While the process means or the process standard deviations are changing in different levels, the performance of each weighted control chart can be then compared by using the ARL. A rule of thumb for selecting better control schemes is provided as a truthfully reference to help engineers in choosing the more appropriate control charts immediately when the assignable causes occurred.

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).

The yield and quality of CPO is influenced by the condition of unripe and overripe FFB (fresh fruit bunches), which can cause an increase in ALB, water content and dirt content. The quality of CPO is also influenced by FFB from the community. Analysis of the consistency of the yield and quality of CPO was carried out due to the age of the plants that were too old and the performance of the factory machines decreased due to the age of the machines. Analysis of yield consistency and quality was carried out using statistical quality control methods, namely quality control using a statistical approach using control charts and causal diagrams. This study uses secondary data on the yield and quality of CPO for the past year (July 2021-June 2022). The research results show, there are data that are outside the upper control limit, yield in July 2021, free fatty acid content in May 2022 and dirt content in October 2021. While the water content is statistically controlled (in statistical control). The yield and quality of ALB are influenced by type of raw material, production capacity and oil losses. Moisture content and dirt content are affected by treatment of production process and machine conditions.

The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification of process irregularities across various fields. This research proposes and implements a novel SQC methodology in agricultural areas. A control chart is one of the SQC tools that facilitates real-time monitoring of multiple activities, including agricultural yield, industrial yield, and hospital outcomes. Advanced control charts with symmetrical data are being subjected to the new SQC method, which is suitable for this purpose. This research aims to develop a novel hybrid exponentially weighted moving average control chart for detecting the coefficient of variation (CV) using a repetitive sampling method called the HEWMARS-CV control chart. It is an effective tool for monitoring the mean and variance of a process simultaneously. The HEWMARS-CV control chart used the repetitive sampling scheme to generate two pairs of control limits to enhance the performance of the control chart. The proposed control chart is compared with the classical HEWMA and Shewhart control charts regarding the average run length (ARL) when the data has a normal distribution. The Monte Carlo simulation method is utilized to approximate the ARL values of the proposed control charts to determine their performance. The proposed control chart detects small shifts in CV values more effectively than the existing control chart. An illustrative application related to monitor the wheat yield at Rothamsted Experimental Station in Great Britain is also incorporated to demonstrate the efficiency of the proposed control chart. The efficiency of the proposed HEWMARS-CV control chart on the real data shows that the proposed control chart can detect a shift in the CV of the process, and it is superior to the existing control chart in terms of the average run length.

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.

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.

The quality of footwear products such as shoes is a major factor that influences the success of a company in producing footwear products. Shoe companies usually have various quality control methods to maintain the production process. One of the most widely used methods in the industry is the statistical quality control method. PT ABC is a company that produces sports shoes located in Bandung. ABC Company is experiencing problems in terms of production, namely the number of defective products produced is still high. This study aims to apply statistical quality control to control the number of defects that appear in the production process. The application process is focused on the problems that arise in the screen printing division where there are many defects that appear during the production process. The steps taken in this study include data preparation and initial analysis using check sheets and Pareto diagrams, data adequacy tests, making p control charts, and analyzing results. Based on the control chart that has been made on the initial defect proportion and the proportion of defective data after improvements have been made to the screen printing process, it can be concluded that there is a decrease in the average value of the defect proportion of 0.02. This figure is quite significant. In addition, there was a drastic decrease in the LCL (lower control limit) value. This significant decrease is a positive indication that the improvements made to the screen printing process have succeeded in reducing product defects significantly. Keywords: Control Chart, Quality Control, Shoes Defect, Statistical Quality Control

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.