Use Of Statistical Process Control Research Articles

Statistical Process Control in Service Industries: Unveiling the keys to quality Management

In the service industries, maintaining a consistent level of quality is crucial for both client satisfaction and competitive advantage. Statistical Process Control (SPC) provides a systematic approach to monitoring and controlling processes, ensuring that quality criteria are met and maintained. This abstract will examine the application of SPC in several service industries and how it has contributed to raising the bar for process efficiency and service quality. The use of statistical process control (SPC) techniques is on the rise in the service industry, despite the methodologies' traditional association with manufacturing. According to this study, SPC is highly beneficial for service industries including healthcare, banking, and hospitality. We demonstrate the usefulness of SPC in identifying variations, reducing errors, and improving service quality through the examination of case studies and real data. Based on the findings, SPC is useful for making decisions in real-time, which opens the door to proactive management of service quality. It promotes a growth mindset and operational excellence as well. The research emphasizes the importance of training and the dedication of both personnel and management in order to successfully integrate SPC into service operations. The potential ways in which SPC could transform service sectors are outlined in this overview. Customer satisfaction and company productivity are both enhanced as a result of the systematization and data-driven approach to quality management that is promoted.

Use of statistical process control in quality improvement projects in abdominal surgery: a PRISMA systematic review

BackgroundThe use of quality improvement methodology has increased in recent years due to a perceived benefit in effectively reducing morbidity, mortality and length of stay. Statistical process control (SPC) is...

APPLICATION OF STATISTICAL PROCESS CONTROL IN MECHANIZED CORN SEEDING

Several factors alter the quality of the mechanized sowing process of corn, and the use of the statistical process control (SPC) technique not only allows the identification of process limitation points but also ensures the correct functioning and conduction of the mechanized process, determining critical factors. that may decrease their performance. In view of the above, the present study aims to qualitatively evaluate, through this technique, the longitudinal distribution of corn seeds as a function of different stands and operational speeds during the sowing process. The experiment was carried out in a randomized block design (CBD) with three replications and in a split-plot design. Data were submitted to analysis of variance and when significant, analyzed using Tukey's test, with 5% significance. To carry out the statistical process control, mean and amplitude control charts were used to evaluate the effect of operational speed, seed distribution, deposition depth and plant germination. The results indicate that the use of statistical process control is an efficient tool to identify the behavior of the corn sowing process. Based on the results, speeds close to 4.5 km/h allowed an efficient sowing process, as it presented a higher percentage of normal spaces in the distribution of maize seeds.

Use of Statistical Process Control for Coking Time Monitoring

Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart’s, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time.

Commentary: Establishing Causality in Quality Improvement Studies.

Commentary: Establishing Causality in Quality Improvement Studies.

Statistical process control to monitor use of a web-based autoplanning tool.

To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. For RayStation, the average mean percent dose differences across all contours were 0.65%±1.05%, -2.09%±0.56%, and 0.28%±0.98% and average control limit ranges were 1.89%±1.32%, 2.16%±1.31%, and 2.65%±1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62%±0.34%, 0.32%±0.23%, and -0.91%±0.98%, while average control limit ranges were 1.09%±0.77%, 3.69%±2.67%, 2.73%±1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202±0.019cm and MLC-TF value of 0.020±0.001 for Acuros and a DLG value of 0.135±0.031cm and MLC-TF value of 0.015±0.001 for AAA. Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.

Use of Statistical Process Control in Bus Fleet Maintenance at SEPTA

As a provider of public transportation services in the Philadelphia area, the Southeastern Pennsylvania Transportation Authority operates and maintains a large number of transit vehicles for daily use. In an attempt to improve service quality and reduce costs, the Southeastern Pennsylvania Transportation Authority underwent a program to train staff on the use of Statistical Process Control to track and address quality issues with its bus fleet. This research presents the steps taken by this public transit agency to implement Statistical Process Control on part of its bus fleet as one element of an overall quality improvement program.

PSXII-20 Use of Statistical Process Control to Evaluate the Effect of Isoquinoline Alkaloids Supplementation on Productive Performance of Growing-Finishing Pigs

Abstract Statistical process control (SPC) is a statistical method that can be used to evaluate the production variation in swine operations, thus facilitating decision making. The objective of this study was to evaluate the effect of plant-derived isoquinoline alkaloids (IQs) supplementation on production performance of growing-finishing pigs by using SPC.The experiment was carried out in a commercial swine integration in Spain. Historical control (calibration) data was compiled from 2017 to June 2020. During this period, all animals received a standard commercial diet. During the treatment period, which started in July 2020 and lasted until February 2021, all pigs were fed the standard diet supplemented with 1 kg/t feed of a plant-based IQ product (Phytobiotics Futterzusatzstoffe GmbH, Eltville, Germany), from day 70 of life until slaughter. Data recorded during both, historical control and treatment periods included feed conversion ratio (FCR), average daily gain (ADG, g/d), average daily feed intake (ADFI, g/d), cost of medicines (Euro/pig), runts (%) and mortality rate (%). SPC tools were used to monitor the previously described performance parameters. CUSUM control charts were obtained for all parameters and for each nutritional and health cluster to show the evolution or the changes of each parameter. The results showed that pigs supplemented with IQs had a lower FCR, increased ADG and reduced cost in medications as compared to the control period (p ≤ 0.05; Table 1). SPC methods were successfully implemented to evaluate the effect of IQs supplementation on growth performance of grow-finish pigs. The results of the study indicated that IQs supplementation improved FCR and ADG, whereas the cost of medication was significantly reduced. Therefore, IQs supplementation in pigs from day 70 of life until slaughter could be a good strategy to improve the efficiency and profitability of the production system.

Using control charts to understand community variation in COVID-19.

Decision-makers need signals for action as the coronavirus disease 2019 (COVID-19) pandemic progresses. Our aim was to demonstrate a novel use of statistical process control to provide timely and interpretable displays of COVID-19 data that inform local mitigation and containment strategies. Healthcare and other industries use statistical process control to study variation and disaggregate data for purposes of understanding behavior of processes and systems and intervening on them. We developed control charts at the county and city/neighborhood level within one state (California) to illustrate their potential value for decision-makers. We found that COVID-19 rates vary by region and subregion, with periods of exponential and non-exponential growth and decline. Such disaggregation provides granularity that decision-makers can use to respond to the pandemic. The annotated time series presentation connects events and policies with observed data that may help mobilize and direct the actions of residents and other stakeholders. Policy-makers and communities require access to relevant, accurate data to respond to the evolving COVID-19 pandemic. Control charts could prove valuable given their potential ease of use and interpretability in real-time decision-making and for communication about the pandemic at a meaningful level for communities.

On monitoring industrial processes under feedback control

AbstractThe concurrent use of statistical process control and engineering process control involves monitoring manipulated and controlled variables. One multivariate control chart may handle the statistical monitoring of all variables, but observing the manipulated and controlled variables in separate control charts may improve understanding of how disturbances and the controller performance affect the process. In this article, we illustrate how step and ramp disturbances manifest themselves in a single‐input–single‐output system by studying their resulting signatures in the controlled and manipulated variables. The system is controlled by variations of the widely used proportional‐integral‐derivative(PID) control scheme. Implications for applying control charts for these scenarios are discussed.

A review on straightening of bars and application of probabilistic approach on Moment-curvature relationship

Bar straightening is a significant process in the value stream of bar manufacturing processes. Any improvement in the process has a substantial impact on quality and productivity as a whole. This paper presents the outline of the fundamentals of the straightening process from a material science perspective and discusses the state of art in terms of process analysis and development. It has been inferred that the major aspect of the process is to understand the moment-curvature relationship in bars subjected to re-rolling. The process is governed by several parameters such as bar speed, the angle between osculating plane and loading plane, bar size or radius of bar, applied reverse bending moment, initial and final curvature, etc. It has also been observed that the research in this domain has seldom applied a statistical approach to enhance process capability. A brief account of the potential use of statistical process control has been presented. Use of statistics shall enable a better understanding of various process parameters. This, in turn, will help in deciding whether the process is in control and therein to assist in deciding the acceptance or rejection of lot.

Quality Improvement Basics: A Crash Course for Pediatric Cardiac Care.

Lack of knowledge of quality improvement (QI) methodology and change management principles can explain many of the difficulties encountered when trying to develop effective QI initiatives in health care. An interactive QI workshop at the 14th Annual Meeting of the Pediatric Cardiac Intensive Care Society provided an overview of the role of QI in health care, basic QI frameworks and tools, and leadership and organizational culture pitfalls. The top five QI projects submitted to the meeting were later presented to an expert QI panel in a separate session to illustrate examples of QI principles. Workshop presenters introduced two major QI methodologies used to design QI projects. Important first steps include identifying a problem, forming a multidisciplinary team, and developing an aim statement. Key driver diagrams were highlighted as an important tool to develop a project's framework. Several diagnostic tools used to understand the problem were discussed, including the "5 Why's," cause-and-effect charts, and process flowcharts. The importance of outcome, process, and balancing measures was emphasized. Identification of interventions, the value of plan-do-study-act cycles to fuel continuous QI, and use of statistical process control, including run charts or control charts, were reviewed. The importance of stakeholder engagement, transparency, and sustainability was discussed. Later, the top five QI projects presented highlighted multiple "QI done well" practices discussed during the preconference QI workshop. Understanding QI methodology and appropriately applying basic QI tools are pivotal steps to realizing meaningful and sustained improvement.

Using lean thinking to improve hypertension in a community health centre: a quality improvement report

BackgroundAchieving better care at lower cost in the US healthcare safety net will require federally qualified health centres (FQHC) to implement new models of team-based population healthcare. Lean thinking may...

SPC techniques using M/M/1 queueing model

In a manufacturing process, no two products can be exact, which might be due to many factors. These variations need to be monitored and controlled through the use of statistical process control (SPC). When they are small, and their effects are barely noticeable, we say the process is 'in control'. Otherwise, they lead the process to 'out of control'. Items enter the system and wait in a line for measurements; by following a queue discipline. Such a queue is then characterised according to service time, number of quality inspectors and waiting space. A good process control will also protect the product in the sense that the lots produced at permissible levels of quality will have a good chance to be in control. The objective of this paper is to introduce a control chart using M/M/1 queueing discipline to study and maintain a process control in a regular interval.

Best practices: Mixing and sampling

In the manufacture of animal food, proper mixing is of utmost importance, no matter whether one is concerned about the inclusion of an additive, nutritional adequacy, or simply the general quality of the finished product. In order to produce a high quality, homogenous mixture, it is important to put into place good practices for both processes and sampling procedures utilized for process evaluation. However, producing a properly mixed animal food actually begins well before the mixing process itself, with a number of important steps in manufacturing coming first, including receiving, grinding, and batching of ingredients. At the mixer, feed mills should be concerned with proper ingredient addition order, appropriate mixer fill, and dry mix, wet mix, and discharge times. As with any process, attention must also be given to preventive maintenance and repairs as necessary. With all of these things in mind, a well-designed quality assurance program, including the use of statistical process control to provide data-driven reviews of performance, is key to ensuring that each of the processes throughout the feed mill do not place limits on the effectiveness of mixing, and that mixing itself is adequate.

Statistical methods – still ignored? The testimony of Swedish alumni

Researchers have promoted statistical improvement methods as essential for product and process improvement for decades. However, studies show that their use has been moderate at best. This study aims to assess the use of statistical process control (SPC), process capability analysis, and design of experiments (DoE) over time. The study also highlights important barriers for the wider use of these methods in Sweden as a follow-up study of a similar Swedish study performed in 2005 and of two Basque-based studies performed in 2009 and 2010. While the survey includes open-ended questions, the results are mainly descriptive and confirm results of previous studies. This study shows that the use of the methods has become more frequent compared to the 2005 study. Larger organisations (>250 employees) use the methods more frequently than smaller organisations, and the methods are more widely utilised in the industry than in the service sector. SPC is the most commonly used of the three methods while DoE is least used. Finally, the greatest barriers to increasing the use of statistical methods were: insufficient resources regarding time and money, low commitment of middle and senior managers, inadequate statistical knowledge, and lack of methods to guide the user through experimentations.

Confirmatory Factor Analysis of TQM Implementation Constructs: Evidence from Nepalese Manufacturing Industries

INTRODUCTIONSince last decades, quality management has been used widely irrespective of size of organization and is consider as the major component of competitiveness and long term success of organization. The successful implementation of TQM in manufacturing sector would promote exports, attract foreign direct investment, improve business performance, achieve a competitive advantage, and customer and staff satisfaction (Magd, 2014). Ensuring TQM and optimum utilization of Human Resource is very much essential to gain sustainability of organization. (Rashid, Taib, & Ahmad, 2016) These benefits are important for all organizations, especially in developing countries, such as Nepal, where the general quality level is relatively low and need to be increased in the chase of effective inter-organizational cooperation. However there exist large number of examples of failed or poorly performed implementation process of TQM. During the process of implementation, organization has to face different complications and often fails to get benefits from quality management implementation. However employee look forward to various development and learning programs and plans available to them to grow not only professionally but only personally (Arora, 2016) Fotopoulus et al. (2009) and Kumar et al. (2009) discussed the impact of TQM practices on quality management results and explains the relationship between different TQM practices like leadership, strategic quality planning, employee management & involvement, supplier management, customer focus, process management, continuous improvements and their effect on quality management results in the form of market benefits like increase in profits, improved competitive position, improved performance and increased sales. While customer satisfaction is measured by decline in customer complaints, increase in loyalty, and customer retention rate. The strategic alliances result in superior performance of the organizations (Ouedraogo, 2016).This study focuses to obtain knowledge of total quality management implementation in a unique, and influencing, organizational environment and situation. The study is focused on obtaining key factors of quality management implementation and is expected to provide new insights into the implementation process in order to improve organizations ability to meet their objectives. There is a consensus that TQM is a way of managing an organization to improve its overall effectiveness and performance (Zhang et al, 2000). No uniform view of TQM exists today. So far, TQM has come to mean different things to different people (Hackman & Oldham, 1995). This study aimed at identifying TQM implementation constructs, developing an instrument for measuring these constructsREVIEW OF LITERATUREDeming is considered the founding father of the TQM and is perhaps the most famous of quality gurus because of his contribution to the creation of quality principles. Deming has focused on the following four points i) idea of internal and external customers or stakeholder satisfaction ii) concept of continuous improvement iii) thought of common and special variation on quality problem iv) management and worker's responsibilities. Deming believes in continuous improvement. He also believes that the consumer is the most important part of a production line. Meeting and exceeding the customers' requirements is the task that everyone within the organization needs to accomplish with total commitment. Furthermore, Deming believes in the use of statistical process control (SPC) charts as major method for solving problems. Like Deming, Juran worked extensively with quality management and perhaps he has best summed up in his 'quality trilogy' of quality planning, quality control and quality improvement (Juran & Gryna, 1993). Juran's emphasis in this respect is in three main areas: changing management behavior through adopting quality, training and then spilling down new attitudes to supporting management. …

The use of statistical process control to improve the accuracy of turning

The present work deals with the turning process improvement using means of statistical process control. The approach on improvement is related to the fact that several methods are used in order to achieve quality defined by technical specifications. The experimental data is collected during identical and successive manufacturing processes of turning of an electrical motor shaft. The initial process presents some difficulties because many machined parts are nonconforming as a consequence of reduced precision of turning. The article is using data collected in turning process, presented through histograms and control charts, to improve the accuracy in order to reduce scrap.

One approach in using multivariate statistical process control in analyzing cheese quality

The objective of this paper was to investigate possibility of using multivariate statistical process control in analysing cheese quality parameters. Two cheese types (white brined cheeses and soft cheese from ultra-filtered milk) were selected and analysed for several quality parameters such as dry matter, milk fat, protein contents, pH, NaCl, fat in dry matter and moisture in non-fat solids. The obtained results showed significant variations for most of the quality characteristics which were examined among the two types of cheese. The only stable parameter in both types of cheese was moisture in non-fat solids. All of the other cheese quality characteristics were characterized above or below control limits for most of the samples. Such results indicated a high instability and variations within cheese production. Although the use of statistical process control is not mandatory in the dairy industry, it might provide benefits to organizations in improving quality control of dairy products.

Phase I analysis of multivariate profiles based on regression adjustment

The use of statistical process control in monitoring and diagnosis of process and product quality profiles remains an important problem in various manufacturing industries. Although the analysis of profile data has been extensively studied in the literature, the challenges associated with monitoring and diagnosis of multiple functional profiles are yet to be well addressed because it is usually difficult to properly model the inter-relationship of multiple profiles. Motivated by a real-data application in semiconductor industries, we develop a new modelling and monitoring framework for Phase-I analysis of multiple profiles. The proposed framework incorporates the regression-adjustment technique into the functional principal component analysis. In this framework, the multiple profiles are treated as multivariate functional observations and their regression-adjusted residuals are used for monitoring. Simulation results show that the proposed method could describe the major structure of profile variation well and effectively find outlying profiles in a historical dataset due to sufficiently utilizing the information on between-profiles correlation.